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Distributed Thinking Symposium VI

Cognition Enacted

31 March 2014Kingston University London



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Monday 31 March

Rooms JG2012 and JG2003 (John Galsworthy Building)

09:00 Coffee JG2012

09:15 Opening remarks JG2003

09:30 Adam Toon

10:00 Gaëlle Vallée-Tourangeau

10:30 Stephen Cowley

11:00 Coffee

11:15 Nick Shipp

11:45 Chris Baber

12:30 Lunch and poster presentations

Johanne Stege Bjørndahl

Charlotte Harris

Matthew Harvey

Angeliki Makri

13:30 Lisa Guthrie

14:00 Jens Koed Madsen

14:30 Lucas Bietti

15:00 Frédéric Vallée-Tourangeau

15:30 Coffee

15:45 Orestis Palermos

16:15 Dan Hutto

17:00 Discussion (Pub)

19:00 Dinner



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Talks

No Anticipation without Representation?

Chris Baber

University of Birmingham

This talk will consider the challenge that

‘anticipation’ raises for Distributed Cognition. I

will use examples drawn from my work on how

people use hand-tools. If one takes a Radical

Embodied Cognition stance (à la Chemero) and

assumes that human activity can be explained

through a combination of affordances and

dynamical systems (as I do) then this raises a

question of how one deals with anticipatory

control. A traditional (representationalist) viewmight argue that people form a model of the

world, then use this model to plan a course of

action and then perform this action. The

modelling and planning, therefore, form an

essential part of anticipation. In the motor control

literature, notions of feed-forward control are

popularly invoked to explain how neural

activation arises prior to movement. My talk will

review some neuroimagining studies (relating to

tool use) and anticipatory control studies. I wantto ask whether there is too much reliance on the

assumption that anticipation rests on a ‘model’

that is used to define a ‘program’ to be

performed, and too little on the moment-by-

moment correction of action-in-the-world.

Aligning Behaviors in Everyday

Conversations about Shared Memories

Lucas Bietti

Telecom ParisTech

In this talk, I investigate the roles that the

interactive alignment of manual gesture, postural

sway and eye-gaze play in small groups engaged

in collaborative remembering. The video data

used for these analyses come from an ongoing

project on collaborative remembering in small

groups. All participants were native Spanish

speakers and data was collected in real-world

environments (participants’ homes in Buenos

Aires). Qualitative and quantitative analyses of a

co-speech gesture, postural sway, and eye gaze

has different interactional dynamics while

interactionally fostering collaborative

remembering among small groups. Moreover,

these analyses show whether and how the

differences in these behaviors’ roles in joint

remembering are reflected in the temporal

dynamics of the alignment patterns observed in

our data. Afterwards, in order to examine

whether the instances of simultaneous and

sequential alignment during joint remembering

found in our data set can be associated to either

priming effects (automatic) or conscious aspects

of the interactions (task-mediated), I report

preliminary results of an agent-based computer

simulation. Finally, I discuss the potential ofcombined qualitative-quantitative approaches for

illuminating the interplay of verbal and bodily

coordination during contexts such as interactive

memory construction.

Replacing Representations

Stephen J. Cowley

University of Southern Denmark

The new consensus in cognitive science is that a

system’s embodiment grounds cognition. For

some, an agent’s embodiment constitutes

cognition, for others this grounds conceptualizing

and, for radicals, embodiment can replace

representation. Taking a systemic perspective,

the paper pursues the replacement view. From a

systemic perspective, living human beings

animate distributed cognitive systems. This

results in a species specific, future-directed

sense-making that depends on non-local or

second-order constructs. For example, humans

alone use music, money, numbers and language.

Before arguing that representation can be

replaced by a clear view of the second-order, it is

important to show what is at stake. First, what we

know about cognition depends on modeling

systems (e.g. cockpits) that track environmental

features. This tracking can be applied to pilots,

ants, thermostats and rats. When describing

tracking, appeal is often made to representations

(Rs): these function as such for human



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one need not posit that Rs function as

representations for the pilot, ant or thermostat.

Indeed, they are likely to arise from the coupling

of organism-environment systems that link up

world, embodiment and brains. That is not my

concern. I focus on representations that, by

definition, count as such for the living

(representing) organism. Tracking mechanisms

(Rs) thus contrast with entities that perform a

modeling function within an animal-environment

system. By definition, these representations or R-

models permit a person to separate out patterns

from ongoing action/perception. An R-model can

thus function as if ‘off-line’: it can be associated

with, for example, coins, images, music or

thoughts. As a result R-models can underpinobserver-dependent ways of thinking, managing

time and acting. For Vallée-Tourangeau and

Vallée-Tourangeau (in press), higher cognition

depends on getting –and using- ‘the gist’ of

things. R-models clearly describe gist generation

and manipulation. Do they serve any explanatory

role? The Vallée-Tourangeaus are surely correct

that they cannot be traced to on-line coupling: in

Shapiro’s terms, there is nothing radical about

the view that living bodies that conceptualizeand/or constitute cognition. In pursuing a

replacement view, I therefore turn to language. I

argue that, by using interactivity within distributed

systems, living human infants use routines and

ways of feeling to self-constitute as speaking-

observers. By acting strategically, a baby

develops ways of languaging that show

extraordinary sensitivity to the verbal patterns of

languages: a child comes to create and construe

speech-in-action while also hearing utterance-

types. As children come to believe in language

(qua ensemble of verbal patterns), they link

activity and hearing to a history of interactivity

that becomes embrangled with imaginings of

speech. While enabled by brains, skills in using

patterns and routines thus transform cognitive

powers based on the discovery of mental time-

travel (e.g. by pretending or telling people what

happened). As Dartnall (2005) suggests, this

ensures that the world leaks into the head. Far

from depending on Rs that sustain R-models, the

latter are the child’s own cognitive constructs.

Radical Enactivist: Rethinking Basic Minds

Dan Hutto

University of Hertfordshire and University of

Wollongong

The cognitive revolution deposed behaviourist

thinking (in both philosophy and psychology) and

licensed a return to active theorizing about

mental states and their place in nature.

Promoting representational and computational

theories of mind, many researchers in diverse

fields have assumed that the contentful

properties of such mental states play critical

causal roles in computational processes enabling

intelligent activity. But serious problems have

been identified with the very idea that contentfulmental representations (of the kind that might do

such work) exist. Moreover, new, non-

representationalist approaches in the philosophy

of mind and cognitive science – enactive,

embodied approaches – have emerged and are

growing in popularity. These developments

suggest that the time is ripe for a complete re-

think of the cognitive revolution. Against this

backdrop, I give reasons to preferring radically

embodied/enactive accounts of cognition thanabandon traditional assumptions of classical

cognitive science, proposing a fundamentally

shift in how we might conceive of the basic

nature of minds.

Drawing in Negatives: Exploring the

Theoretical Placement of the SPIMP Cognitive

Model of Persuasion

Jens Koed Madsen

Birkbeck College

A recently developed cognitive psychological

model of persuasion argues for a subjective-

probabilistic, immersed, and cultural description

of how humans deal with uncertain information

from uncertain sources in potentially misleading

situations (the so-called SPIMP). The model

represents a departure from previous models of

persuasion that focus on a dual-process

perspective, which divides reasoning into rule-

based and heuristic (e.g. the ELM and HSM).



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of research applying a Bayesian probabilistic

approach to reasoning. Centrally, this moves the

description of reasoning towards a subjectively

interactive and contextually immersed

perspective while retaining empirical testability

and support. The model performs well in

predicting and describing how humans seem to

computationally deal with uncertain information

from an uncertain source from a subjective

perspective. Conceptually, however, the

underlying framework remains under-developed,

as the notion of subjectivity and immersion lack

definitions and descriptions. Thus, the talk points

to a conceptual framework where the model is

grounded in the persuadee’s interaction with the

environment in order for subjectivity to emerge.Further, in order to move towards a theory of

influence, the model needs to be placed in a

larger theoretical framework concerning the link

between belief and behaviour. Here, the model

offers a potential piece of the puzzle in that it

describes the process of evaluating uncertain

information from an uncertain source, but fails in

accounting for influence. The talk explores two

limitations of the current model. Firstly, in terms

of its theoretical foundation and secondly interms of its placement in a larger conceptual

framework that points towards interaction,

distributed cognition, and the limits of persuasion.

The talk is an invitation for a holistic perspective

on the psychology of persuasion and influence in

which the SPIMP offers a central, but inadequate

element.

Dynamic Problem Solving: The Effect and

Affect of Interactivity on Mental Arithmetic

Lisa G. Guthrie

Kingston University

Individuals often gesture, point or use objects as

an aid to solving quotidian arithmetic problems.

In attending to a problem, the dynamic loop of

information and action flows between the person

and the outside world, the very nature of these

interactions constrain and guide strategic

choices. This interactivity has been linked to

better performance in problem solving, possibly

resources and better distribution of cognitive

load. In attempting to simulate these moves

made in the world, different levels of interactivity

were examined with a series of mental arithmetic

problems. The use of artefacts, such as tokens or

a pen promoted more accurate and more efficient

mental arithmetic performance. Participants were

also profiled in terms of attitude to varying

problem presentations as an assessment of their

engagement in the task. They felt more positive

about and better engaged with the task when

they could reconfigure the problem presentation

through interactivity. These findings underscore

the importance of engineering task environments

that support distributed problem representation

and adequate levels of interactivity that creates adynamically shifting topography of action

affordances.

Knowledge and Cognitive Integration

Orestis Palermos

University of Edinburgh

Cognitive integration is a defining yet overlooked

feature of our intellect that may neverthelesshave substantial effects on the process of

knowledge-acquisition. To bring those effects to

the fore, I explore the topic of cognitive

integration both from the perspective of virtue

reliabilism within externalist epistemology and the

perspective of extended cognition within

externalist philosophy of mind and cognitive

science. On the basis of this interdisciplinary

focus, I argue that cognitive integration can

provide a minimalist yet adequate epistemic

norm of subjective justification: so long as the

agent’s belief-forming process has been

integrated into his cognitive character, the agent

can be justified in holding the resulting beliefs

merely by lacking any doubts there was

something wrong in the way he arrived at them.

Moreover, since both externalist philosophy of

mind and externalist epistemology treat the

process of cognitive integration in the same way,

we can claim that epistemic cognitive characters

may extend beyond our organismic cognitive

capacities to the artifacts we employ or even to



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hinting towards some of the possible theoretical

and practical ramifications of this move.

Influencing Categorical Choices through

Physical Object Interaction

Nick Shipp

University of Hertfordshire

Recent theories on semantic memory have

proposed that concepts are grounded in sensori-

motor activity and action information plays a

strong role in tasks even when such knowledge

is not necessary. Research shows that

participants are more likely to use action

information as a basis for categorisation thanperceptual information. The following experiment

examined whether participants would be more

likely to match items based on shared actions

following interacting with the objects on a

physical basis. Participants engaged in one of

three priming tasks either using a series of

objects for their functional capacity (action

priming), grouping them into categories

(taxonomic priming) or moving them from one

table to another (movement priming). Followingthis participants were shown a forced-choice triad

task in which items could be matched based on

either taxonomic relations (rifle + sword ) or a

shared action relation (rifle + water pistol ). Items

within the triads were presented as an image

either on a white background (context-lean

condition) or as a functional scene with the object

being used by an agent (context-rich condition).

The results showed that participants were more

likely to select the action related item when they

were primed with the functional action of the

objects and when the images were presented

within context. The results are discussed as a

result of the context-dependent nature of action

knowledge.

Situating Styles

Adam Toon

University of Exeter

In a series of influential articles, the philosopher

different styles of reasoning within scientific

practice, each with its own history. Furthermore,

Hacking argues that, when taken together with

positivist theories of meaning, styles of reasoning

lead to a form of relativism: if the meaning of a

scientific claim depends upon the style of

reasoning appropriate to establishing its truth or

falsehood, then the birth of a new style brings

new propositions into being as candidates for

truth or falsehood. As a result, styles cannot be

subjected to independent criticism, since the

propositions they evaluate have no meaning

outside of the style. In his more recent work on

styles, Hacking has placed less emphasis on

their relativistic implications. Two other

developments are also important for the presentpaper. The first is that Hacking is keen to stress

that styles of reasoning are not styles of thinking ,

since “thinking is too much in the head” and

omits “the manipulative hand and the attentive

eye” (1992, pp. 3 – 4). Styles involve an

“embodied creature [that] uses not just its mind

but its body to think and to act in the world”

(2012, p. 600). The second important

development is that Hacking now links styles of

reasoning to a burgeoning form of inquiry that hecalls cognitive history . This is “the study of how

an organism with certain cognitive capacities, on

a planet like this, developed (etc.)” (2012, p.

607), exemplified by works such as Renfrew,

Frith, and Malafouris’ The Sapient Mind:

Archaeology Meets Neuroscience (2009).

Recently, Malafouris (2013) has argued that an

appropriate theoretical framework for such

studies can be found in recent work in areas

such as situated , embodied , extended , and

distributed cognition, which reveals the

importance of interaction between the brain, body

and environment in our cognitive processes. In

this paper, I will ask how we might draw on these

frameworks to understand styles of reasoning in

science, thereby underpinning Hacking’s own

emphasis on the role of the body in scientists’

reasoning. Interestingly, I will argue, this

approach to understanding scientific reasoning

might also be thought to give rise to a form of

relativism, since some work in situated cognition

suggests that people are unable to engage in



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particular external, material devices. I will

examine this ‘situated’ reading of styles of

reasoning in detail, and ask what it might mean

for our understanding of science.

Insight Enacted

Frédéric Vallée-Tourangeau

Kingston University

People solve problems by recruiting and

interacting with artefacts. Through this

interactivity, a problem representation evolves

and is distributed across resources internal and

external to the reasoner. Real-world interactivity

was domesticated in a laboratory environment byoffering different artefacts to participants in a low

and a high interactivity group. An insight problem

involving the spatial rearrangement of sets, but

masquerading as an arithmetic problem, was

employed. Participants in the low interactivity

group were never able to break the impasse, that

is to abandon their representation of the problem

as one involving an arithmetic solution.

Participants in the high interactivity group were

more likely to break the impasse and discover aproductive representation of the problem. Video

evidence reveals substantial differences in the

manner with which participants ‘thought’ about

the problem as a function of the nature and

degree of interactivity; insight was enacted

through the manipulation of certain artefacts.

The Spatio-temporal Dynamics of Systemic

Thinking

Gaëlle Vallée-Tourangeau

Kingston University

Recent developments in cognitive science reject

the classical view of cognition as a cerebral

activity involving the rule-based processing of

symbols inside the mind and call for a

reconceptualization of cognition as emerging in a

system encompassing the brain and the body in

situ. Current dissenting views comes with two

corollaries. First, representations are

unnecessary to explain complex behaviours.

cognition. We argue that a radical departure from

the classical information-processing model is

untenable because higher-level cognition is

fundamentally representation-based. However,

we also argue that classical accounts of thinking

put too great an emphasis on the role of internal

representations and mental processing. This

obscures the symbiotic relationship between

thinking and acting and the role of spatio-

temporal dynamics and ecological affordances

on thinking. To fully understand how people

think, solve problems, and make decisions, we

need to break from traditional conceptions of

thinking activities as sequestered in a static mind,

transcend current debates about the localisation

of cognition and, instead, focus our effortstowards better understanding how thinking

emerges in ecological space and ecological time

from the transactional flow of action and

representational opportunities outcropping from a

dynamic agent-environment interface.

!!!!!!

Posters

Thinking Together with Material

Representations: The Role of Joint Epistemic

Actions in Creative Cooperation

Johanne Stege Bjørndahl

Aarhus University

In many situations in people’s everyday lives,

cognition is a highly situated and social activity.

This study investigated the role of material

representations in collective creative processes.

How do material objects shape and aid joint

epistemic processes? How do people coordinate

and spontaneously distribute cognitive labor

when solving an open-ended creative task

together? These questions guided a qualitative

study of social interactions in 6 groups of 4-5

participants solving a series of creative workshop

tasks involving LEGO blocks as part of a

psychology experiment. A qualitative micro-

analysis of the video recordings of the



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material representations play in the joint

epistemic processes involved in the tasks:

illustration, elaboration and exploration. Firstly,

the LEGO blocks were recruited for illustration, to

support top-down structured processes, such as

to represent already well-formed ideas in order to

support communication and epistemic alignment.

Furthermore, the LEGO blocks were examined

and questioned in ways that gave rise to

discussions, clarifications and highlighted

underlying disagreements, this we call

elaboration. Lastly, the LEGO blocks were used

for exploration, that is, the material

representations were experimented on and

physical attributes were explored resulting in

discoveries of innovative practices and newmeaning potentials. The study points to a

tendency for the more top-down ways of

engaging material representations to be

supportive of gaining and maintaining authority

as well as to provide powerful tools for achieving

epistemic alignment. However, more bottom-up

oriented approaches were characterized by more

distribution of cognitive labor, more

unconventional usage of materials and more

possibility for unanticipated solutions to takeplace that shaped the problem solving

processes.

It All Adds up: Interactivity and Expertise in

Mental Arithmetic

Charlotte L. Harris and Lisa G. Guthrie

Kingston University

People solve problems in a situated and

contingent manner. The problem presentation

shifts dynamically as people interact with its

constituent elements, either through the re-

arrangement of physical artefacts or the space

that configures and contains the problem.

Recently, laboratory-based research on problem

solving aimed to recreate the dynamic nature of

problem solving by designing problem tasks in

mental arithmetic that encourage interactivity.

The fixed, paper-based problem presentation of

conventional mathematics may discourage

ingenuity and re-formulation of complex sums. In

problem space offers the prospect of improving

efficiency in problem solving. However, any

benefits derived by re-designing the problem task

have the potential of being eroded by expertise in

that given field, equally those low expertise folk

may benefit from an altered problem

presentation. The experiment presented here

investigated problem solving from a distributed

cognition perspective, examining the impact of

interactivity on mental arithmetic for ‘maths

experts’ and ‘non-maths experts’. Results

showed that in the high interactivity condition, low

expertise participants equipped with tokens were

more accurate and efficient in completing the

addition problems than in a low interactivity

paper-based condition. In addition, averagedeviation from the correct solution was also

reduced, however the use of tokens increased

latency. Results showed the effect of interactivity

was different for participants with greater

expertise: Efficiency and accuracy only

marginally improved, and average deviation from

the solution increased in the high interactivity

condition, compared to the low interactivity

problem presentation. Latency in both high and

low interactivity conditions remained constant.These results indicate that mental arithmetic

performance may be improved for those with low

expertise by using a distributed problem-solving

environment. The benefits of interactivity,

however, may be reduced for those with greater

maths expertise.

Enaction, Anti-representationalism, and

Content in Natural Languaging

Matthew Harvey

University of Cambridge

This paper outlines an enactivist alternative to

the idea that natural language is necessarily

content-involving, framed as a reply to Daniel

Hutto and Erik Myin (2013). The authors

simultaneously defend a thoroughgoing anti-

representationalism about sensorimotor

engagement with the world and (at least prima

facie) accept a classical representationalist

account of meaning in natural language. I argue

that they are mistaken, however, in identifying



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enactivism, because the idea of enaction - taken

to be a positive argument to the effect that

biologically autonomous organisms “enact” or

“bring forth” their worlds – (i) entails anti-

representationalism without reducing to it but

also (ii) precludes any account of linguistic

meaning in terms of informational content. In this

light it is important that Hutto and Myin’s anti-

representationalism is logically independent from

their position on linguistic meaning; the former is

motivated by the “Hard Problem of Content”

(HPC), which demonstrates that explanatory

naturalism is incompatible with an account of

informationally contentful representations. The

latter is an assertion, for which Radicalizing

Enactivism does not offer much support, thatlanguage-involving behavior both instantiates

conditions of felicity (i.e., is informationally

contentful) and is exempt from the HPC. I depart

from Hutto and Myin in adopting what they call

“Really Radical Enactive Cognition”, which holds

that cognition is never, under any circumstances,

contentful or representational. I accept as

premises both (a) that informationally contentful

accounts of perception are untenable (due to the

HPC and related considerations), and (b) thatlanguaging “behaves as if” it were contentful. I

outline a conception of this phenomenal aspect

of languaging that draws on core enactive

accounts of non-contentful meaning-making

activity complexified by Steiner and Stewart’s

(2009) notion of “heteronomy” as a socialized

complement to the “immanent normativity” of

biological autonomy. I introduce the idea of

“attentional technologies” to describe the co-

enaction of joint-attentional structures (that is,

minimally heteronomous perceptual entities).

This theoretical construct allows me to link

bottom-up enactive theory to the philosophy of

techniques and to distributed linguistics, and so

to resolve the tension between the HPC and the

seeming-contentfulness of meaning in natural

language. The view of languaging that emerges

is, I believe, broadly compatible with Hutto and

Myin’s position as well as with early enactive

accounts of language.

Changing the Cognitive Landscape: Material

Engagement in Problem Solving

Angeliki Makri, Erica Mundahl, and Andra Popa

Kingston University

Traditional cognitive psychology research on

problem solving proceeds on the basis of

experimental procedures wherein the degree of

interactivity and the possibility of manipulating

the problem presentation are limited or

eliminated. As a consequence, participants’

problem solving performance is constrained and

limited by a sterilized environment which

amputates cognition, an environment that lacks

ecological validity. The experiment reported here

investigated problem solving performance withan analytic and an insight problem as a function

of the level of interactivity. In a low interactivity

condition, participants worked on the problems

using an electronic tablet. In a high interactivity

condition, participants were given artefacts that

configured the problem presentation and were

invited to manipulate them in order to solve the

problems. The high interactivity condition

fostered a dynamic and fluid problem

presentation that anticipated and complementedthe participants’ hunches as they worked through

a solution. Solution rates in the high interactivity

condition were higher for both the analytic and

insight problem, and significantly so for the latter.

Measures of creativity and need for cognition

were significant predictors of performance for the

analytic problem in both low and high interactivity

conditions, but not for the insight problem. In

turn, working memory was a significant predictor

of performance for the insight problem, again,

across conditions. High interactivity enhanced

problem solving performance and led to a higher

solution rates especially for the insight problem.

This suggests that in the high interactivity

condition, the correct solution is the product of a

largely contingent trajectory guided and

constrained by the shifting action affordances

cued by the dynamic problem presentation.

These findings support the fruitfulness of

adopting a distributed cognition perspective on

problem solving and encourage researchers in

this area to question the traditional efforts to limit



DTS6 10

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Philosop hica l Psycholog y , 2014Vol. 27, No. 1, 112–125, http://dx.doi.org/10.1080/09515089.2013.828371

Friends at last? Distributed cognition and the cognitive/social divide Adam Toon Distributed cognition (dcog) c la im s t ha t m an y c og ni ti ve p ro ce ss es a re “d is t ri bu te d”

a c ro s s g r ou p s a n d t h e s u rr o un d in g m a te r ia l a n d c u lt u ra l e n v ir o nm e nt . R ec e nt l y, N an c y Nersessian, Ronald Giere, and others have suggested that a d-cog approach might allow us

t o b ri ng t og et he r c og ni ti ve a nd s oc ia l t he or ie s o f s ci en ce . I e xp lo re t hi s i de a b y f oc us in g o n

t h e s p ec i fic i n te r pr e ta t io n o f d - co g f o un d i n E d w in H ut c hi n s’ c a no n ic a l t e xt Cognition in

the wild. F ir st , I e xa mi ne t he s co pe o f a d- co g ap pr oa ch t o s ci en ce , s ho win g t ha t t her e a re

i mp or ta nt d is pu te s b et we en c og ni ti ve a nd s oc i al t he or is t s o n w hi ch d - c og r em ai ns s il en t.

S ec on d, I su gg es t t ha t, w he re s oc ia l ex pl an at io ns c an b e re ca st i n d -c og t er ms , th is

r e fo r m ul a ti o n w i l l n ot b e a cc e pt a bl e t o a l l so c i al t h eo r i st s . F in a ll y, I a s k h o w w e s ho u ld

m ak e s en se o f t he c l ai m t ha t, o n a d -c og a na ly si s, s o ci al f a ct or s are cognitive factors.

Ke y w o r ds : D i s t r i b ut e d C o g ni t i o n; E d w i n H u tc h i n s; N a n c y N e rs e s s ia n ; R o na l d G i e re ;

S o ci o lo g y o f S c ie n ti fi c Kn o wl e dg e

1. Introduction There is sometimes thought to be an opposition between cognitive and social theories

of science. Perhaps the clearest instance of this opposition is Latour and Woolgar’s

infamous “tenyear moratorium on cognitive explanations of science” (1986, p. 280).

On one side of the divide, social accounts emphasize scientists’ social and political interests or institutional structures. On the other, cognitive accounts refer mainly to

scientists’ cognitive processes. In philosophical discussions, the term ‘cognitive’ is

often associated with terms such as ‘rational’ or ‘truthconducive’. In the present

context, however, ‘cognitive’ is used in the sense found in cognitive science, to refer to

psychological processes such as perception, reasoning, memory, and so on (Giere &

Moffatt, 2003, p. 302). Processes that are cognitive in this sense can, of course, fail to be

rational or truthconducive. The divide at issue here is therefore distinct from that

Adam Toon is a Marie Curie Fellow at the University of Exeter.

Correspondence to: Adam Toon, Department of Sociology, Philosophy and Anthropology, University of Exeter,

A B ildi R D i E t EX4 4RJ U it d Ki d E il t @ t k

http://dx.doi.org/10.1080/09515089.2013.828371

mailto:[email protected]


http://dx.doi.org/10.1080/09515089.2013.828371



P h i l os o p h ic a l P s yc h o l og y 113

concerning the relationship between social factors and the rationality of science (e.g.,

Longino, 1990; Solomon, 2001).

Recently, Nersessian (2005) has argued that the perceived divide between cognitive

and social theories is based on a mistaken “Cartesian” view of cognition, summed upin the tenets of GOFAI (“Good Old Fashioned Artificial Intelligence”) (Haugeland,

1985). According to GOFAI, cognition involves computational processes on symbolic

representations internal to the individual mind. Within cognitive science, GOFAI is

increasingly challenged by a range of different approaches. Particularly important for

Nersessian is d i st r i bu t ed c o gn i ti o n (d-cog ). In contrast to GOFAI, dcog claims that

many cognitive processes are “distributed” across social groups and the wider material

and cultural environment. Together with a team of researchers, Nersessian has offered

an analysis of the laboratory as an “evolving distributed cognitive system” (e.g.,

Nersessian, KurzMilcke, Newstetter, & Davies, 2003a; Nersessian, Newstetter, Kurz

Milcke, & Davies, 2003b). She suggests that this approach allows us to “integrate”

cognitive and social accounts of science, as well as yielding other important insights.

For example, dcog might allow us to see that the nature of cognitive processes in

science has changed over time (2005, p. 52).

Giere (2002a, 2002b, 2002c, 2004, 2006, 2007, 2012; Giere & Moffatt, 2003) has

expressed similar hopes for dcog. As well as applying the approach to a number of

aspects of scientific practice, such as the use of diagrams and models, Giere has

suggested that we reinterpret wellknown studies by sociologists such as Latour and

KnorrCetina in dcog terms (Giere, 2002b; Giere & Moffatt, 2003). Like Nersessian,

Giere believes that dcog “bridges the often perceived gap between cognitive and socialhistories of science” (2002a, p. 285) and that the approach offers important historical

insights. For example, it allows us to see that “what powered the scientific revolution

was an explosion of new forms of distributed cognitive systems” (2002a, p. 298).

At times, Giere also goes further than Nersessian, arguing that, on a dcog approach,

the cognitive and social “overlap” (2002a, p. 296) or “merge” (Giere & Moffatt, 2003,

p. 304). For example, referring to KnorrCetina’s analysis of experiments in high-

energy physics, Giere argues that dcog provides “a complementary, cognitive, account

of these experiments” (2002b, p. 639). In this account, “we can now say that aspects of

the situation that seemed only social are also cognitive” (2006, p. 114).

In this paper, I try to assess dcog’s potential to bring together cognitive and social

approaches to science. Exactly what is meant by ‘distributed cognition’ is sometimes

unclear. In section 2, I distinguish between a general and a more specific interpretation

of the approach. Without further development, the general interpretation of dcog is

difficult to assess. I therefore focus on the more specific interpretation, found in

Hutchins’ C og ni ti on i n t he w i l d (1995). Hutchins’ approach is introduced in section 3.

In section 4, I consider the scope of dcog: which aspects of science might be

understood using this approach, and which, if any, will remain beyond its reach?

In doing so, I argue that there are important disputes between cognitive and social

theorists on which dcog remains silent. In section 5, I suggest that, where socialexplanations can be recast in dcog terms, this reformulation will not be acceptable to

all social theorists Finally in section 6 I ask how we should make sense of Giere’s



114 A. Toon

claim that, on a dcog analysis, social factors are cognitive factors. Can this aspect of d

cog help us to reconcile cognitive and social approaches?

2. What is Distributed Cognition? The canonical text for distributed cognition is Hutchins (1995). This is an

ethnographic study of navigation on a U.S. Navy ship, which Hutchins calls the Palau.

Hutchins offers a detailed analysis of how the Palau’s crew accomplishes certain tasks,

such as determining the ship’s position and planning its course. Typically, he finds,

these tasks are not performed by any one individual, but by a group of crew members

working together. Each of the crew members also makes use of a variety of different

tools. The processes that accomplish the tasks are therefore distributed across the

members of the team and the tools they use. Moreover, the social structure plays a

crucial role in the crew’s activity: the way in which tasks are performed depends upon

the command structures that govern the crew’s interaction. On the ship there is a

“social distribution of cognitive labor” (Hutchins, 1995, p. 228).

For example, consider the “fix cycle.” This is the procedure in which the navigation

team determines the ship’s position by taking visual bearings to landmarks on either

side of the ship. The fix cycle involves a number of different crewmembers. Inside the

pilothouse are the navigation plotter and the bearings recorder. Together, they decide

on suitable landmarks for obtaining a position fix. They then pass on the names of

these landmarks to the pelorus operators, who stand on either side of the ship. The

pelorus operators must identify these landmarks on the horizon and take theirbearings. To do so, they use a device called an alidade, which has a hairline sight

aligned with a gyrocompass scale. The pelorus operators relay the bearings to the

pilothouse, where the recorder notes them in the log and the plotter plots them on the

chart to determine the ship’s position and project its future course. The plotter uses

various tools to carry out this task, such as a hoey, which is a special protractor with a

long arm that can be set to the recorded bearing.

Hutchins’ analysis thus encompasses both the cognitive acts of individuals (such as

reading the bearing of a landmark in the alidade sight) and the social structure guiding

their interactions (such as the ship’s command hierarchy). In this respect, his analysis

is perhaps not so remarkable. After all, as both Giere and Nersessian acknowledge, not

all sociological accounts are as hostile to cognitive or psychological explanations as

Latour and Woolgar (e.g., Bloor, 1976). Conversely, cognitive theorists sometimes

recognize the importance of social context (e.g., Dunbar, 1995). What exactly is

distinctive about a dcog approach? In particular, how does dcog offer a

reinterpretation of sociological accounts, such as KnorrCetina’s?

The distinctive feature of dcog, it seems, is that it treats social groups, along with

tools and parts of the material and cultural environment, as cognitive systems.

Hutchins’ key claim is that the navigation team may be analyzed as a “cognitive and

computational system” (1995, p. xiv). This passage already points to two differentinterpretations of the approach, however. According to the first interpretation, to

analyze an activity as d cog is to understand it as a cognitive process According to the




second, it is to understand the activity as a computational process. While the first

approach is not committed to any particular view of the nature of cognition, the

second claims that cognition takes a specific form, namely computation.

As we will see in section 3, it is the computational version of dcog that underpinsmuch of Hutchins’ (1995) analysis. Both Nersessian and Giere sometimes distance

themselves from the claim that cognition is computational (Giere, 2006, pp. 107–108;

Osbeck & Nersessian, 2006; see also Brown, 2011, p. 28). Nevertheless, it is worthwhile

assessing the computational approach, for a number of reasons. First, Hutchins’ book

remains the locus classicus for work on distributed cognition and forms the central

inspiration for both Nersessian and Giere. Second, as we shall see in section 3, the

computational version of dcog has much to recommend it. Third, the computational

view has the virtue of being more specific, and therefore easier to assess, than the first

interpretation of dcog. If dcog claims only that social groups are cognitive systems,

without sayingmore about what cognitive systems are, then it is difficult to see how to

evaluate the approach. In what follows, I shall therefore focus on the computational

interpretation of dcog, although some of what I say (especially in section 6) will apply

to the more general interpretation as well.

3. Ships as Computers At the outset, Hutchins tells us that his study is an attempt “to apply the principal

metaphor of cognitive science—cognition as computation—to the operation of [thenavigation team]” (1995, p. 49). Although he is skeptical of GOFAI’s claim that what

goes on inside the head is computational, Hutchins believes that this analysis may be

applied to the navigation team as a whole: “the system formed by the navigation team

can be thought of as a computational machine” (1995, p. 228). Moreover, “the

computation observed in the activity of [the navigation team] can be described in the

way cognition has been traditionally described—that is, as computation realized

through the creation, transformation, and propagation of representational states”

(Hutchins, 1995, p. 49).

It is important to note that Hutchins does not intend his analysis to be merely

metaphorical. In his view, applying the language of computation to the navigationteam is “not a metaphorical extension at all” (1995, p. 364). Hutchins develops his

analysis by drawing on Marr’s (1982) distinction between three different levels on

which a cognitive system may be understood. At the computational level we have an

abstract description of the computation that a system carries out. Thus, fixing a

position may be understood as combining two onedimensional constraints to give a

unique position in twodimensional space. The representational level concerns “the

choice of representation for the input and output and the algorithm to be used to

transform one into the other” (Marr, 1982, pp. 24–25). For example, the crew of the

Palau uses the standard Western coordinate system and onedimensional constraintsare given by lines of position. Finally, the implementation level specifies how the

representational system and algorithm are physically implemented On the Palau for



116 A. Toon

instance, the pelorus operators determine the bearings that give the line of position,

while the plotter determines their intersection on the chart.

It will be helpful to see some examples of how Hutchins applies this analysis to

social interactions. Within computer science, a daemon is an agent that monitors forcertain trigger conditions, and takes a specified action when those conditions are met.

During the Palau’s voyage, Chief Richards instructs another member of the crew,

Smith, to watch the fathometer and report when the depth of water falls below 20

fathoms. Hutchins claims that here we have “the social construction of an

informationprocessing mechanism” (1995, p. 192). By giving the order to Smith,

Chief Richards reconfigures the system formed by the navigation team to create a

daemon which will respond to certain conditions and output a symbolic signal when

those conditions are met.

Hutchins offers a similar analysis of “phone talkers.” These are members of the crew

posted at each end of the ship’s telephone lines. The job of a phone talker is to receive

messages and relay them to the relevant crew members when there is a suitable break

in their activity. In Hutchins’ analysis, phone talkers are i n fo r ma t i on b u ff e rs, which

“[permit] communication to take place when the sender and the receiver are not

overloaded” (1995, p. 195). The bearing recorder and log are also information buffers,

since they enable the pelorus operators and the plotter to work asynchronously: the

plotter need not plot each bearing as it is reported, but can instead refer back to the

bearing recorder or log. The log is also a memory , as well as a filter , which “passes the

bearingswithoutpassingthe temporal characteristics of their production” (1995, p. 195).

In Hutchins’ original formulation, then, dcog is a claim about what happens at theimplementation level of a computation. Ratherthan takingplacewithinthemind ofany

individual, the algorithm determining the ship’s position is implemented by a

distributed systemconsisting of the entire navigation crew. This computational version

of dcog has a numberof attractions. First, and perhapsmost important, it offers a clear

rationale for the claim that dcog integrates the cognitive and the social: according to

the computational view, social organization becomes part of the way that the relevant

computation is implemented.AsHutchins puts it,wemay “treat the social organization

as a computational architecture” (1995, p. 185). Second, the computational approach

provides a powerful tool for understanding different social arrangements and assessing

their epistemic merits. In this respect, it shares the attraction of Thagard’s earlier

proposal (1992) that we understand science using distributed artificial intelligence

(D.A.I.). Third, dcog also avoids oneof themain objections against Thagard’s proposal

because, unlike D.A.I., dcog is not committed to claiming that scientists’ internal

cognitive processes are computational (Thagard, 1992, p. 58).

4. Uncharted Waters Neither Nersessian nor Giere claim that dcog offers a complete theory of science, nor

that it allows us to reconcile all disputes between cognitive and social theorists. In lightof this, it is important to ask how far dcog might be able to take us. Which aspects of

science might be analyzed using a d cog approach and which if any will remain out of




its reach? These questions are rarely explicitly addressed in the literature. And, at first

glance, itmight seemdifficult to answer themat this stage, since even the computational

version of dcog remains rather general. In itself, the approach claims only that

laboratories carryout some formof computation; it does not say what that computationis or exactly how it is implemented. In fact, however, I want to suggest that any

computational dcog analysis will remain silent on some parts of scientific practice.

To see this, recall that dcog is a claim about the implementation level of a

computation: Hutchins shows how the computation determining the Palau’s position

is implemented by the entire navigation team and their social interactions. It is in this

sense that dcog offers a means of integrating cognitive and social factors. Notice,

however, that even if we accept such an analysis, a number of issues will remain to be

addressed. First, a dcog analysis will not tell us why a particular computation is being

performed orwhy a specific representational system is being used to carry it out. Second,

the analysiswill not tell us how a representational systemgains its representational status.

When applied to science, I suggest, both of these questions receive competing answers

from social and cognitive accounts. As a result, they will remain areas where a dcog

analysis of the laboratory cannot help to reconcile debate between the two.

First, consider the choice of representational system. Alongside his study of the

Palau, Hutchins offers an analysis of traditional navigation techniques in Micronesia.

Hutchins argues that the Western andMicronesian systems are, in fact, the same at the

computational level: both combine onedimensional constraints to give a unique

position in twodimensional space. But the two differ radically at the representational

level. For example, rather than taking the boat to be moving across a fixed two-dimensional space, Micronesians think of the canoe as stationary while the water moves

past it. Progressduringa journey is represented not byaunitof length but by the changing

bearing of a reference island. Onedimensional constraints are sometimes provided by

sightings of birds, which indicate the canoe’s distance from land.

There are many questions that we might ask about these representational systems.

Why do Micronesian navigators think of the canoe, rather than the water, as fixed?

Why do we employ standard units of distance and time and embody these in charts

and other devices, while the Micronesians do not? Are there still other ways in which

humansmight represent the world to find their way around it? Each of these questions

concern the choice of representational scheme used to carry out a computation, not

the way that the computation is implemented. As a result, they cannot be answered

through a dcog analysis of a particular navigation practice. Hutchins himself offers

many fascinating insights into the origins of the representational scheme used in

Western navigation (1995, chapter 2). He does so, however, not by relying on his

analysis of the Palau, but instead by looking at the history of charts and other

navigational tools in the West. In Hutchins’ view, the features of the Western

representational system are historically contingent, rather than “natural and inevitable

or simply the consequences of the interaction of human nature with the demands of a

given task” (1995, p. 114).Many of the questions that we ask about the sciences also concern representational

systems For example consider classification schemes These have been the focus of



118 A. Toon

considerable attention in the sociology of scientific knowledge. In fact, according to

Bloor, “one of the central propositions in the sociology of knowledge” is that “the

classification of things reproduces the classification of men” (1982, p. 267).

By contrast, some cognitive scientists claim that there are important similarities in theway that different cultures categorize the world that are due to universal cognitive

constraints (e.g., Berlin, 1992). I do not wish to enter into this debate here. All I want

to point out is that, like questions concerning the differences between Western and

Micronesian navigation, this debate about classification schemes concerns the choice

of representational system, rather than its implementation. As a result, if there is an

opposition here between sociological and cognitive accounts, then a dcog analysis of

the laboratory cannot help to overcome this opposition.

The second issue that dcog does not address is that of how representational systems

gain their representational status (that is, why they have meaning ). The reason for this,

of course, has to do with a general feature of the computational view. As Crane puts

the point, “a computational process is, by definition, a rulegoverned or systematic

relation among representations. To say that some process or state is computational

does not explain its representational nature, it presupposes it” (2003, p. 169). In line

with Hutchins’ analysis, dcog would treat the laboratory as a computational system

creating, manipulating, and destroying representations. This leaves open the question

of how those representations come to represent the world. Once again, this is a

question that receives very different answers from social and cognitive accounts of

science. For example, members of the Edinburgh Strong Programme in the sociology

of science propose a theory of meaning known as finitism, drawn from the laterWittgenstein (Barnes, Bloor, & Henry, 1996). According to finitism, meaning is a

fundamentally social phenomenon. By contrast, of course, many cognitive scientists

and philosophers reject this view, instead seeking to explain meaning in terms of non-

social factors, such as causal relations. Itmight be thought that dcog may legitimately

defer these disputes about the nature of meaning. And yet finitism is arguably the

central element of the Strong Programme, which gives rise to many of its key claims,

such as epistemic relativism (e.g., Kusch, 2002). Here again, then, we find an

important dispute between which dcog cannot help to reconcile.

5. A Storm Brewing? Section 4 pointed to aspects of science that will be omitted from a dcog analysis. Let

us now consider those parts of science that dcog does seek to analyze, and ask whether

this analysis can help to reconcile cognitive and social accounts. Unfortunately,

I believe that the prospects for reconciliation here may be limited in an important

respect, since the analysis offered by dcog will not be acceptable to all social theorists.

In fact, rather thanbridging thegapbetweencognitiveandsocial theories,dcog threatens

to return us to an old debate between the two, sparked by Slezak’s paper, “Scientific

discovery by computer as empirical refutation of the Strong Programme” (1989).Slezak focused on computer discovery programs such as BACON (Langley,

Bradshaw & Simon 1983) Given the relevant data BACON is able to “rediscover”




various empirical laws, such as the ideal gas law. According to Slezak, programs like

BACON constituted a decisive refutation of the Strong Programme in the sociology of

scientific knowledge (SSK), which claims that “social factors are an irreducible

component of scientific discovery” (1989, p. 564).1

In fact, for Slezak, the “very possibility of computer programs making scientific discoveries poses a fundamental

challenge to SSK’s radical claims” (1989, p. 564).

Slezak’s argument proved controversial. Even supporters of cognitive accounts took

issue with his claim that BACON was capable of making scientific discoveries. For

example, Thagard noted that such programs “are highly simplified and do not

constitute full simulations of how discoveries were actually made” (1989, p. 654).

Interestingly, however, most commentators agreed that, if computer programs could

make discoveries, SSK would be refuted. For example, in response to Slezak’s paper,

Giere wrote:

A minimal claim of a sociology of scientific knowledge (SSK) would be that thecourse of science is necessarily influenced by the human interests of scientists, whichare in turn partially shaped by their social relationships. If computers have nohuman interests and no social relationships, and yet can make scientific discoveries,this minimal claim would be refuted. (1989, p. 639)

Similarly, Collins agreed that “if [Slezak’s premise] is taken to mean that BACON

reproduces social collectivities then, if true, it would be fatal for ‘the strong

programme’ and its variants.” Why? “Because BACON is not a social collectivity”

(1989, p. 614). The trouble, I think, is that dcog poses a similar challenge to social theories. Recall

that, according to Hutchins, the navigation team is a “computational machine” (1995,

p. 185). As Hutchins himself reminds us, computation is independent of the physical

medium in which it is implemented (1995, p. 51). As a result, if the laboratory is a

distributed cognitive system, then the computation it performs could in principle be

carried out on some other system entirely (Magnus, 2007, p. 299). And this system

need not be a social one. In fact, as far as dcog is concerned, the social is only the

“hardware” on which the computation happens to be run. Consider phone talkers, for

example. According to Hutchins, they are information buffers, controlling the flow of

information between different parts of the system. On the Palau this is achieved by

assigning crew members a particular role within the social organization of the ship.

But there is nothing essentially social about an information buffer. If Hutchins’

analysis is correct, the phone talker might as easily be replaced by a silicon circuit, so

long as it implements the same computation.

Of course, dcog would not support all of the conclusions that Slezak wanted to

establish using BACON. For example, Slezak argued that, since BACON implements

general rules of problem solving, its success points to the existence of “contextfree or

supercultural norms of rationality” (1989, p. 572). A dcog approach does nothing to

suggest the existence of such norms: the computations carried out in differentlaboratories might vary widely with different social contexts. Nevertheless, like

BACON d cog would appear to be at odds with SSK’s claim that science is essentially



120 A. Toon

social. Similar claims are also found in the work of authors outside SSK. For example,

Longino writes that science is “necessarily social” (1990, p. 12).

There are a number of ways in which we might try to resolve the apparent conflict

here. Recalling our discussion in section 4, social theorists might argue that socialfactors are necessary for those aspects of science omitted from dcog, such as the

semantics of representational schemes. Alternatively, we might construe claims

regarding the essentially social nature of science in some weaker sense, which is not

threatened by the possibility of nonsocial implementation raised by dcog. For

example, perhaps some social theorists might rest content with the claim that, while a

nonsocial science is possible, actual scientific practice is social, and likely to remain

so. Others, however, are keen to argue for a stronger position (e.g., Kusch, 2002).

Taking a slightly different line, Thagard argues that D.A.I. is compatible with Longino’s

view, since computer networks can capture the critical interactions that she takes to be

required for scientific objectivity (1992, p. 61). Once again, these issues cannot be

explored fully here. Much will depend upon the details of particular social theories.

Nevertheless, I think it is important to note that there is an apparent conflict here and

that this conflict must be resolved if dcog is to bridge the gap between cognitive and

social accounts of science.

6. Merging the Cognitive and Social One way in which dcog promises to bridge the gap between social and cognitive

theorists is by revealing that social factors are cognitive factors. For example, recallGiere’s claim that, on a dcog analysis, “aspects of the situation that seemed only social

are also cognitive” (2006, p. 114). In a similar vein, he writes that:

Thinking of science in terms of systems of distributed cognition enlarges the domainof the cognitive in our understanding of science. It is typically assumed that there isa sharp divide between the cognitive and the social. From the perspective of distributed cognition, what many regard as purely social determinants of scientificbelief can be seen as part of a cognitive system, and thus within the purview of acognitive understanding of science. There is no longer a sharp divide. The cognitiveand the social overlap. (Giere, 2002a, p. 296)

With dcog,we are told, the cognitive and social “merge” (Giere &Moffatt, 2003, p. 304).

How exactly should we understand these claims? And how might this aspect of d

cog help us to reconcile cognitive and social theories of science? To answer these

questions, we first need to understand what is meant by ‘cognition’ in this context. In

everyday contexts, we normally use the term ‘cognitive’ to refer to internal,

psychological processes such as perception, memory, reasoning, and so on. Moreover,

it was broadly this sense of cognition that was at stake in our original dispute between

cognitive and social theories of science: social theorists argue for the importance of social

factors, such as social status or institutional structures, while cognitive theorists stress

internal, psychological processes such as reasoning, perception, memory, and so on.Given our usual notion of cognition, we might wonder how external processes such

as social interactions could possibly count as cognitive However Giere is keen to stress




that the concept of cognition invoked by dcog is not our everyday one: “we are

developing a science of cognition. In so doing we are free tomake cognition a technical

scientific concept different from everyday notions” (2002b, p. 642; see alsoGiere, 2006,

p. 112). What is this technical notion? What does it mean to say, for example, that thePalau or the laboratory is a cognitive system in this technical sense? Giere suggests that

“the reason for calling these systems cognitive systems rather than, say, transport

systems or agricultural systems, is that they produce a distinctly cognitive product,

knowledge” (2002b, p. 642). Elsewhere, he expands on this idea:

A distributed cognitive system is a system that produces cognitive outputs, just as anagricultural system yields agricultural products. The operation of a cognitive systemis a cognitive process. . . . But what makes the output of the system a cognitiveoutput? Here I think the only basis we have for judging an output to be cognitive isthat it is the kind of output we recognize as the result of human cognition, such as a

belief, knowledge, or a representation of something. (Giere, 2006, pp. 112–113)

Thus, a cognitive system is one which produces a cognitive output, such as a belief,

knowledge, and so on. In this new, technical sense, even distant galaxies used as

gravitational lenses for the Hubble Space Telescope may count as part of a cognitive

system (Giere, 2012, p. 201).

If we understand ‘cognition’ in this way, then to claim that the laboratory is a

cognitive system is simply to claim that its output is a cognitive state, such as a belief,

knowledge, and so on. While this claim might be uncontroversial, it is difficult to see

how it will help to reconcile debates between cognitive and social theories of science.

After all, both sides in this dispute would agree that science produces a cognitiveoutput , such as beliefs (and perhaps also knowledge). What they disagree about, of

course, is the nature of the processes that produce that output. Showing that the

laboratory is cognitive in the minimal sense that it produces a cognitive output would

not appear to resolve any debates over the nature of the processes that lead to that

output.

So it seems that, if dcog’s merger of social and cognitive factors is to bridge the gap

between cognitive and social theorists, it must employ a more substantial concept of

cognition. One obvious strategy would be to stress the computational aspect of dcog.

On this interpretation, to claim that social interactions are cognitive would be to claim

that they implement a computation (and perhaps also that they result in a cognitive

output, such as a belief). As we saw in section 5, this claim would be likely to receive

more resistance from social theorists. But let us suppose that this resistance could be

overcome. Would a computational analysis of social factors then help to reconcile

cognitive and social theorists?

Of course, it is likely that disputes will remain. Even if social theorists were to accept

that social interactions are computational, they might still disagree over their relative

importance compared to internal psychological processes, such as memory or

reasoning. Nevertheless, dcog’s proponents might argue that there is now an

overarching concept of cognition—namely, cognition as computation—thatencompasses both social processes and internal, psychological ones. The difficulty

with taking this line however is that Hutchins himself does not think that internal



122 A. Toon

cognitive processes are computational. And, as we have seen, both Giere and

Nersessian are also wary of the computational view.

Put simply, then, the challenge facing dcog would seem to be as follows. In the

original dispute between social and cognitive theorists, ‘cognitive’ took somethingclose to its usual meaning, referring to internal, psychological processes such as

memory, perception, reasoning, and so on. According to Giere, dcog invokes a new,

and much broader, notion of cognition that applies to systems involving many

individuals and their social interactions, as well as models, diagrams, instruments, and

sometimes even distant galaxies. At the same time, Giere also argues that dcog helps

to resolve the dispute between social and cognitive theorists since it reveals that social

processes are cognitive processes. The trouble is that, the further dcog’s technical

notion of cognition moves away from the everyday notion at stake in the original

dispute, the harder it is to see how dcog’s merger of cognitive and social factors helps

to resolve that dispute. Unless dcog’s notion of cognition displays at least some

significant similarities with our usual notion, it is difficult to see how it enables us to

close the gap between cognitive and social theories of science. Instead, what dcog

would seem to offer is simply a new way of analyzing the social aspects of science (one

which understands social interactions as computational).

At this point, one option for dcog’s proponents is to endorse the extended mind

thesis (Clark & Chalmers, 1998). Like Giere, proponents of the extended mind thesis

talk of cognitive systems that exist outside our heads, and even outside our bodies.

Unlike Giere, however, they argue that such claims do not involve a merely technical

sense of ‘cognition’. Consider the wellknown case of Otto and Inga. When Inga hearsof an exhibition at the Museum of Modern Art (MoMA) she recalls that the museum

is on 53rd Street and heads off. Otto is an Alzheimer’s patient who carries a notebook

with him wherever he goes to record useful information. When Otto hears of the

exhibition, he looks up the information in his notebook and heads off. Clark and

Chalmers claim that Otto’s notebook plays a similar functional role to Inga’s biological

memory. As a result, they argue, the notebook counts as part of Otto’s cognitive

processes, not in any merely technical sense, but in precisely the same sense as Inga’s

biological memory. Before consulting his notebook, Otto literally believes that MoMA

is on 53rd Street, just as Inga believes this before consulting her memory. If external

objects like notebooks can implement recognizably cognitive processes, like memory,

then perhaps social interactions can too (Clark & Chalmers, 1998, pp. 17–18).

Giere rejects the extended mind thesis, since he regards it as leading to unnecessary

and unanswerablemetaphysical puzzles about themind (e.g., 2006, pp. 110–113). It is

for this reason that he insists that dcog involves a technical concept of cognition. If d

cog’s proponents were sympathetic to the extended mind thesis, however, then this

would seem to offer a way to bridge the gap between cognitive and social theorists. Just

like Otto’s notebook, scientists’ social interactions might be said to implement

processes that are cognitive, not in any merely technical sense, but in exactly the same

sense as internal, psychological processes, such as memory. Of course, one problemwith this approach is that the extended mind thesis remains highly controversial (e.g.,

Menary 2010) But another is that supporters of the extendedmind use ‘cognitive’ in a



124 A. Toon

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DYNAMICS OF SYSTEMIC THINKING 1

The Spatio-temporal Dynamics of Systemic Thinking

Gaëlle Vallée-Tourangeau and Frédéric Vallée-Tourangeau

Kingston University

Author Note

Correspondence concerning this article should be addressed to either Gaëlle

Vallée-Tourangeau or Frédéric Vallée-Tourangeau, Department of

Psychology, Kingston University, Kingston-upon-Thames. UNITED

KINGDOM, KT1 2EE, [email protected] or f.vallee-

[email protected], tel: +44 (0)208 417 2000, fax: +44 (0)208 417

2388.




Abstract

Recent developments in cognitive science call for a reconceptualization

of cognition as emerging in a system that encompasses the brain and the

body in situ, and rejects the classical view that cognition is a fundamentally

cerebral activity based on the rule-based processing of symbols represented

in the mind. A radical anti-thesis to the classical information-processing

approach goes as far as positing that cognition emerges directly from people’s

actions in the world. This view comes with two corollaries. The first corollary is

that representations are unnecessary to explain complex behaviours, which

can be understood from the study of the dynamic relation between an agent

and her environment. The second corollary is that there is a spatio-temporal

dimension to cognition as it emerges from a continuous and fluid coupling of

neural and physical activity. In this paper, we discuss the implications of the

ecological view of cognition for higher cognitive functions such as problem-

solving, judgement, and decision-making. We argue that a radical departure

from the classical information-processing model is untenable, notably

because higher-level cognition is fundamentally representation-based, thus

rejecting the first corollary of the radical embodiment approach. However, we

also argue that classical accounts of thinking put too great an emphasis on

the role of internal representations and mental processing. This obscures the

symbiotic relationship between thinking and acting and the role of spatio-

temporal dynamics and ecological affordances on thinking, thus embracing

the second corollary of a systemic view of cognition. Finally, we discuss

current ecological accounts of higher cognition and show that they are, at

best, interactional. To fully understand how people think, solve problems, and




make decisions, we need to break from the traditional conception of thinking

activities sequestered in a static mind, and instead study how thinking

emerges in ecological space and ecological time from the transactional flow of

action and representational opportunities outcropping from a dynamic agent-

environment interface.





Recent developments in cognitive science call for a

reconceptualization of cognition as emerging in a system that encompasses

the brain and the body in situ, and rejects the classical view that cognition is a

fundamentally cerebral activity based on the rule-based processing of

symbols represented in the mind. A radical anti-thesis to the classical

information-processing approach goes as far as positing that cognition

emerges directly from people’s actions in the world. This view comes with two

corollaries. The first corollary is that representations are unnecessary to

explain complex cognition, which can be understood from the study of the

dynamic relation between an agent and her environment. The second

corollary is that there is a spatio-temporal dimension to cognition as it

emerges from a continuous and fluid coupling of neural and physical activity.

In this paper, we discuss the implications of the ecological view of cognition

for higher cognitive functions such as problem-solving, judgement, and

decision-making. We argue that a radical departure from the classical

information-processing model is untenable to understand how individuals

think, notably because thinking is fundamentally representation-based, thus

rejecting the first corollary of the radical embodiment approach. However, we

also argue that classical accounts of thinking put too great an emphasis on

the role of internal representations and mental processing. This obscures the

symbiotic relationship between thinking and acting. By contrast, we argue that

spatio-temporal dynamics and ecological affordances play a fundamental role

in thinking, thus embracing the second corollary of a systemic view of

cognition. Finally, we discuss current ecological accounts of higher cognition




and show that they are, at best, interactional. To fully understand how people

think, solve problems, and make decisions, we need to break from the

traditional conception of thinking activities sequestered in a static mind, and

instead study how cognition emerges in ecological space and ecological time.

Cognition Is an Emergent Property of Dynamical Systems

The missing part of the Turing machine

Thinking is an elusive concept. Classically, cognitive psychologists

have conceived human thinking as the mental process of pondering on, or

reasoning about, ideas and opinions produced by the mind. Current views

sometimes adopt a dual-process perspective, distinguishing between intuitive

and deliberative processes (e.g., Kahneman, 2003), but nevertheless

conceive thinking is a mental activity aiming to acquire knowledge and

understanding. Following influential thinkers such as Jerry Fodor who argued

that “the character of a mental state is independent of its physical realization”

(1981, p. 119), most cognitive psychologists view cognition as the product of a

“Turing Machine”, named after the British mathematician who first imagined it.

Turing (1936) compared a man to a computing machine, endowed with an

initial configuration or “state of mind”, a limited set of operations that he can

apply to information brought to its conscious awareness, and a limited set of

procedures stored in memory dictating the order and numbers of operations to

be applied. In this model, cognition results from the desultory, stepwise,

processing of information inside one’s head. The coupling of an individual’s

state of mind with the value of the information in his conscious awareness at

any moment in time determines which procedure should be applied to

transform that information. This results in a new state of mind, which can then




be coupled with the next piece of information to enter conscious awareness

and identify the next procedure to be applied, and so on. The Turing machine

metaphor has had a long-lasting impact on cognitive scientists’ conception of

cognition; they have since sought to discover and model the mental

procedures linking informational inputs and outputs, independently of their

physical realization.

Outside the realms of cognitive psychology, however, philosophers

(Clarke, 2008), anthropologists (Hutchins, 1995), and cognitive scientists

(Kirsh, 2013) have challenged this conception of cognition, arguing instead

that cognition emerges in complex systems that include, but cannot be fully

specified by, neural computations. Turing’s machine was intended to model a

mathematician who proceeds towards a computational goal. Yet, it leaves out

the person, with her eyes, ears and hands, and the physical world in which

she implements those computations, including, for example, her notebook and

fountain pen. It leaves out the notebook’s annotations, strikeouts, mistakes,

drawings, quotes, and so on. It misses out the dead-ends and insights

occurring over time. The Turing machine reduces this rich process to the

application of pre-determined rules to strings of symbols and dismisses the

contribution of the mathematician’s interactions in space and time with the

material world as mere implementational details. We are left with a sterilised

model of human cognition without a human being in it. As such, the Turing

machine epitomises models of the abstract computations that result from the

processing of symbols, but it is not an adequate model of human cognition

because it fails to explain how people’s interactions with the material world

through their eyes, ears, and hands support the instantiation of these




computations (Hutchins, 1995).

Not all dissenters agree on what alternative model we should adopt to

account for human cognition. Proponents of the distributed cognition

approach continue to characterise cognitive processes as involving

mechanisms operating upon representations but they reject the assumption

that those mechanisms and those representations are necessarily uniquely

mental: “Minds are not passive representational engines, whose primary

function is to create internal models of the external world.” (Hollan, Hutchins,

& Kirsh, 2000, p. 177). So, rather than conceive cognition as emerging from

the activity of the brain alone, the distributed cognition approach conceives

cognition as emerging from the coordination of people’s inner resources and

mental processes with the resources present in their immediate material and

social environments, and the processes taking place in these environmental

spheres. As such, cognition is taken to arise from the transmission,

transformation, and coordination of both mental representations (e.g., as

people mentally rotate a geometric form) as well as physical ones (e.g., as

people physically manipulate geometric forms in a game of Tetris to speed up

identification, Kirsh & Maglio, 1994). Other dissenters, however, are calling for

a more radical reconceptualisation of cognition. For proponents of the radical

embodiment approach (e.g., Chemero, 2009; Shapiro, 2011; Wilson &

Golonka, 2013; Van Gelder, 1995), the Turing machine is an inadequate

model of human cognition, not only because it confines cognitive processes to

those located inside the brain, but also because it assumes that cognition

emerges from the processing of complex representations. These views come

with two corollaries. The first corollary is that representations and complex




computations are unnecessary to explain complex behaviours. The second

corollary is that there is a spatio-temporal dimension to cognition. We review

both corollaries in the next two sections.

Neither representations nor complex computations are necessary to

account for cognition

Understanding how people store knowledge in their mind is a core

research topic for modern cognitive psychologists. This effort, in turn, calls for

a specification of the manner in which such stored knowledge is represented

in the human mind (e.g., Neisser, 1967). The Cognitive Revolution gave rise

to amodal conceptions of knowledge representations defined as “data

structures processed independently of the brain’s modal systems for

perception, action, and introspection” (Barsalou, 2010, p. 717). An alternative

conception of knowledge representations emerged from a grounded approach

to cognition, which posits that internal representations “have a situated

character, implemented via simulations in the brain’s modal systems, making

them well suited for interfacing with external structures.” (ibid.).

Perception, in the eye of classical constructivist theories of cognition, is

thus cast as a product of mental processes that disambiguate impoverished

environmental input to form modal representations (e.g., Gregory, 1980).

Ecological psychologist Gibson (1986) criticised this view for it implicitly

models the perceiver as an immobile, passive observer. He argued that

perception was not an approximate, mental process; rather, it was a physical

activity. From this perspective, perceiving organisms have access to a clear,




definitive, and direct identification—that is, an identification that is not

mediated by a mental representation—of environmental stimuli, through the

movements of their head and body as well as their actions. Moreover, the

purpose of such a direct perception is not to build a passive catalog of

stimulus categories. Its purpose is to identify what the environment may

“afford”; that is, what opportunities or possibilities for action it offers (Withagen

& Chemero, 2011). Proponents of the radical embodiment approach propose

that Gibson’s account of perception is applicable to cognition in general, thus

recasting cognition as emerging online, in real time, in situ, and from the

dynamical and continuous interactions of coupled systems made of brain,

body, and world. From this perspective, meaning and knowledge are not

invented through mental processes, nor are they mediated by

representational states. Instead, they are discovered through the coupling of

the cognising agents’ activities and the affordances available in their

environment (Turvey & Shaw, 1999; Wilson & Golonka, 2013).

The assumption that knowledge and understanding arise from the coupling

of a knowing agent and the environment within which this agent functions

calls for a shift in focus from the study of brain activity as a causal determinant

of cognition towards the study of the causal interconnections between the

agent and its environment. Cognition is assumed to self-organise over time

and emerge “live” from perception-action couplings, in a nonlinear,

thoughtless, but deterministic way. Constrained by their own biological

attributes, the characteristics of the tasks they face, and the features of the

immediate environment in which they operate, cognising organisms are

ultimately channeled into more or less stable attractor states of mind (Thelen,




1992). To understand cognition is, therefore, first to understand what

resources are at the disposal of the knowing agent to solve a specific task.

These resources may be located in the body, in the environment, and,

eventually, in the brain. Second, to understand cognition is to understand how

such resources can be assembled to create a dynamical system that evolves

over time towards a solution (Wilson & Gonlonka, 2013).

Numerous examples from robotics and animal cognition show that

complex behaviours can arise from the coupling between agents and their

environment without the need to use representations or computations as

mediators (e.g., see Barrett, 2011). Rat pups, for example, are born blind,

deaf and without fur. To survive, they need to stay close to each other to keep

their body temperature at a suitable level. Despite their “disabilities,” they

exhibit a complex and characteristic pattern of behaviours known as

“thigmotaxic behaviours” and which include wall following, corner burrowing

and huddling. May, Schank, Joshi, Tran, Taylor and Scott (2006) were able to

reproduce these behaviours in robot rats without sensors but simply

programmed to select randomly from a limited number of movements (stop,

move forward in a number of direction or move backward in a couple of

directions) every two seconds. The key feature of these roborats was that

their body was morphologically similar to that of real rats, and importantly,

featured a pointy nose. Once they encountered a wall, the moving robot-rats’

pointy nose caused them to slide alongside it until they reached a corner, at

which point they became stuck. As more robot-rats joined in, they all ended

up huddling in a corner. This example illustrates how complex pattern of

behaviours may emerge without the need for mental “action plans”. None of




the robot-rats intended to huddle. Instead, their behaviour emerged from the

interaction between their inner mechanisms (i.e., the different built-in types of

motions available to them), their body (i.e., the shape of their nose), and the

characteristics of their immediate environment (i.e., featuring walls and

corners).

Cognition results from a temporally continuous flow of activity

A second corollary of the conception of cognition as emerging in real time

from the continuous interactions of coupled systems made of brain, body, and

world concerns the manner in which cognition evolves in time. The Turing

machine metaphor for cognition suggests that cognitive processes unfold in a

formally-specifiable sequence of discrete events. The order of these events is

assumed to be dictated by a stored algorithm controlling the sequence of

steps required to transform an informational input into an output. From this

perspective, change is predictable and timing is irrelevant. In contrast, timing

becomes an essential feature of cognitive systems if one views cognition as

emerging from the real-time coupling of brain and body activities in the

environment. The coupling of brain, body and environment in a cognitive

system results in simultaneous unfoldings. Brain activity is changing while the

knowing agent acts in the world while the world is changing. Brain activity, the

agent’s actions in the environment, and the resulting changes in the

landscape are continuously dependent on each other; they co-evolve in a

cyclical pattern of causation and coupling.

The temporal dimension to the emergence of cognition spans from

milliseconds to weeks, months up to millennia. At the millisecond scale, real-

time cognition can be shown to evolve through continuous and dynamic




changes rather than “as a staccato series of abstract computer-like symbols”

(Spivey & Dale, 2006, p. 207). EEG studies of insight in remote-associate

tasks do not reveal a discrete, sudden, ‘epiphanic’ Aha! moment, but rather a

continuous pattern of activation including prior preparatory brain states

(Kounios & Beeman, 2009). Speech perception (e.g., distinguishing between

competing phonemes such as ‘ba’ and ‘pa’), spoken word recognition (e.g.,

recognising ‘candle’ instead of ‘candy’) and semantic categorisation (e.g.,

categorising ‘whale’ as a mammal rather than a fish) are all examples of

cognitive processes that do not involve a discrete, stepwise, resolution; rather

they involve a temporally continuous flow of neural activity that is first

attracted to competing “gravitational basins” until some “resolution” occurs,

leading to a stable mental state (Spivey & Dale, 2006).

Beyond the millisecond span, similar patterns of temporally continuous

behaviours can be observed in infants who learn to reach for hidden objects

over time. The classic A-not-B error occurs when 10-months old infants

persist in reaching for an old hiding place to retrieve an attractive object after

having seen the object being hidden in a new location. Traditional

computational accounts for this error propose that it arises as motor

experience takes precedence over a conscious representational system

where behaviour is controlled by rule-based algorithms (Marcovitch & Zelazo,

1999). This ‘error’ can be explained without resorting to a representational

system. Thelen, Smith, Schoner and Scheier (2001) showed that the

perseverance error was determined by multiple causes—such as the history

of reaching for the original location, the attention-grabbing of the hiding event

or of the cover, the delay between the hiding event and the reaching




behaviour, or the approach to reach—all potentially interacting over nested

timescales. The error is not a failure of motor control planning at the neuronal

level; rather it is the product of the interaction, in real-time, between the infant

body, its mind, and the activities afforded by its immediate environment. By

the age of 12-months, most infants stop persevering in the classical task;

however, changing the environment can make the error re-appear in two-

year-olds (Butler, Berthier, & Clifton 2002). This suggests that cognitive

development is not simply the product of brain maturation; it is the emergent

product of the continuous unfolding of infants’ activities in their environment,

where each action sets the stage for the next (Smith & Thelen, 2003). So,

here again, behaviour cannot be accounted for by a timeless, orderly, plan of

actions. Instead, time is of the essence: to predict what will happen at time 1

in the brain-body-world system, you need to know where everyone and

everything were at, at time 0. This is true for the trajectory of cognition at the

millisecond timescale, at the lifespan timescale, and the argument can be

made that this also account for the development of homo sapiens’ cognitive

capacities over millennia (Malafouris, 2013).

To summarise, recent views, ranging from the distributed cognition

approach to the radical embodiment approach recast cognition as emerging,

in real-time, from the interactions of a knowing organism, acting in, and

reacting to its immediate environment. While the radical embodiment

approach is arguing that cognition can do without representations, the type of

behaviours this approach aims to explain remain basic (usually implicating

action-perception loops to guide adaptive movement in simple environments).

Whereas one may ponder at the remarkable (in)ability of super-computers to




model simple behaviours, it remains that humans are capable of achieving

more than those simple behaviours. Humans can play chess, draw

inferences, solve problems and make decisions. In the next section, we

discuss the challenges these types of activities raise for both the traditional

computational approaches as well as the radical embodiment approach.

The Human Brain is not (Only) an Executive Controller of Actions

Representations are the undeniable substrate of some human cognitive

activities

The assumption that cognition emerges from a system that never

represents the world may be tenable from the perspective of robotics or

animal psychology; but it is largely untenable from the perspective of cognitive

psychology—the branch of psychology that is concerned with how humans

acquire, transform, and produce knowledge. Notwithstanding David Hume’s

(1740/1983, as cited in Turvey & Shaw, 1999) touchstone, some cognitive

activities are unique to humans and may require an explanatory framework

that is inapplicable to other organisms. For a start, there is ample empirical

evidence that people represent the meaning of what they have read or heard.

For example, Bransford and Franks (1971) showed that, after having studied

a set of sentences such as “the ants in the kitchen ate the jelly” (a) and “the

ants ate the sweet jelly, which was on the table” (b), people would later be

equally likely to recognise an old sentence such as (a) or a new sentence

combining propositions from studied sentences, such as (c): “the ants ate the

sweet jelly.” However, they would not recognise a sentence that contained

new units of abstract meaning such as “the ants ate the jelly beside the

woods.” Such findings strongly suggest people construct a mental




representation of the gist meaning of the sentences they read and later rely

on those representations to support their recall or recognition.

Besides meaning, people also represent and solve problems. Two

broad types of problems have been subjected to laboratory scrutiny by

psychologists: (i) analytic or transformation problems and (ii) insight problems.

Transformation problems are knowledge lean and well-defined problems,

presenting an initial situation or start state, and a final situation or goal state.

To solve transformation problems, people are assumed to represent the

problem’s start state and engage in a step-by-step mental transformation of

this representation until they reach the problem’s goal state. Such

transformations occur by applying operators, defined as more or less

constrained means of changing one state into another. Simple transformation

problems such as the Tower of London have been studied in the lab, primarily

to observe, or in neuropsychological cases to diagnose, participants’ planning

skills and move selection decisions. Insight problems, although they too tend

to be knowledge lean, are less easily characterized although all involve an

initial inability to envisage the operators one could apply to achieve the goal

state. It is this initial impasse that must be overcome for a solution to be

conceived. Insight problems interest psychologists largely because of the

‘Aha’ phenomenology associated with the sudden eureka clarity with which

reasoners envisage the solution. Take the following problem: How do you

throw a ping pong ball in such a manner that it comes to a complete stop and

reverses direction without coming into contact with anything? An initial

representation of the problem involving a horizontal throwing motion will

encourage possibly creative but futile solutions. However, representing a




vertical throwing motion quickly ushers in a satisfying resolution to this

‘problem’. What interest psychologists are the processes by which the initial

representation of the problem is restructured to yield a more productive

perspective from which the solution can be conceived easily. Once again, the

fact that people can solve both classes of problems, simply from reading a

problem statement written on a piece of paper (e.g., Metcalfe & Weibe, 1987)

suggests that people construct mental representations of problem states and

rely on those representations to support their problem solving activities.

Representations of meaning and problem states are two among many

more examples of higher cognitive activities. Similar arguments can be made

with regards to other higher cognitive activities such as mathematical

reasoning (e.g., Ashcraft & Battaglia, 1978), analogical reasoning (e.g., Gick

& Holyoak, 1983), deductive and inductive reasoning (Johnson-Laird, 1983),

or judgement and decision-making (Villejoubert & Mandel, 2002). The fact is

that humans are able to think off-line. Put a hungry human in a new maze

(e.g., a new city). Typically, he may hunt for a grocery store or restaurant

randomly walking along city blocks. He might remember his way around

initially through trials and errors but will soon develop a cognitive map of his

surroundings. Early orientation behaviours may be closely coupled to

perceptual inputs, but soon he will be able to plan his whereabouts or figure

out ways to overcome changes in his environment off-line; that is, in the

absence of concurrent dynamic interaction. This, as Clark (1997) aptly put it,

points to “the difference between inner systems that operate only so as to

control immediate environmental interactions and ones that use inner

resources to model the world so as to obviate the need for such continual




argument, tools are not natural; they are artefactual and disparate. The

processes involved in thinking with tools are not homogeneous, they are

varied and indefinable; therefore, there cannot be a scientific account of brain-

tool cognition that would lead to the discovery of general laws of behaviour.

This position, it seems to us, underscores the deficiency of the current

research methodology to capture how cognition may emerge from brain,

body, and environment interactions rather than a genuine theoretical and

ontological impasse.

There are many examples throughout history where human

understanding of natural phenomena shifted radically through methodological

breakthroughs and the accumulation of empirical evidence. The brain itself

was once considered such a trivial organ that it was simply removed in the

preparation of bodies for mummification in ancient Egypt or conceived as a

mere cooler for the heart’s passions in Aristotle writings (Finger, 1994).

Admittedly facile, this diachronic contrast nevertheless illustrates how our

current understanding of the workings of the brain was made possible once

scholars believed in the importance of its study, developed and honed

methods to study it, and started to accumulate empirical findings.

To conclude a priori that the external props and aids are motley and

irrelevant to cognition is unwarranted and risks missing a proverbial forest.

Until an appropriate methodology is developed for studying how cognition

may emerge from the dynamic configuration of extended cognitive systems,

and until enough empirical evidence is accumulated, we are bound to see

motley. Motley may seemingly preclude a scientific account of how

transactions between brains, bodies and environments contribute to the




emergence of higher cognition, but it does not necessarily entail that

interactivity is devoid of meaningful structures, organizing patterns, and

regularity. Only the empirical and scientific study of those systems can

provide a meaningful answer to this question.

This new programme of research may begin with a more qualitative and

ethnographic research methodology (see Steffensen, 2013, for an excellent

example) but could also borrow from established methods of applied behavior

analysis in germane fields of enquiries such as developmental psychology,

educational psychology and ethology (Bakeman & Quera, 2011; Sharpe &

Koperwas, 2003). Such approaches are well poised to offer generalizable

conjectures about how new ideas, understanding, and solutions emerge from

an ontological substrate woven by interactivity. Note that this is not to say that

we should treat artefact materiality as inconsequential: the evolution and

transformation of an approximate number sense to exact counting and

arithmetic in humans over millennia of cultural evolution is inextricably linked

to the development and manufacturing of a wide range of artefacts—clay

tokens, so-called ‘envelopes’, impressed tablets, and pictographic tablets

(Malafouris, 2013). On the contrary, this is a call to embrace an

epistemological approach and an empirical commitment to document how

cognitive systems work.

A related argument put forward against the proposition that external

props and aids are constitutive of human cognitive processing is the fleeting

nature of systems where brain and body interact with their immediate

environment (Rupert, 2009). Cognitive psychology’s aim, admittedly, is to

provide a nomothetic account of human cognitive processes through the




discovery of general scientific laws that underpin human cognitive activities

such as categorisation, memory, judgement and problem-solving, across

contexts and situations. By contrast, situated systems where brain, body and

environment interact seemingly epitomise the idiographic particular, with its

essentially transient and concrete interactions bounded to a specific space

and time. So in the face of the motley of environments, artefacts, and

localised interaction, the brain appears as the only constant and thus the only

suitable unit for studying human cognition. This argument, however, is

fallacious. The complex patterns of neural cognitive activity unfolding inside

one individual brain, at any point in space and time, are as particular,

idiosyncratic and seemingly unruly as the motor activities unfolding outside

the brain, at this very moment, in this very situation. Still, neuroscientists can

formulate function-to-structure deductive and structure-to-function inductive

inferences (Henson, 2005) despite heterogeneous patterns of brain activity.

So, once again, the fleetness argument does not hold up as an argument

against the possibility of formulating generalizable inductive inferences about

the spatio-temporal dynamic regularities of systemic cognition.

To summarise, the fact that individuals can think off-line does not take

away the fact that they do not typically do so (Clark, 2010). People think

everyday and yet, undeniably rarely come up with solutions to concrete or

abstract problems through long streaks of off-line thinking. Instead, most of

human thinking emerges online, through physical and verbal interactions with

the immediate environment. For example, real-world problem solving is

enacted through interactivity with people and things in a physical environment

(Cowley & Nash, 2013; Steffensen, 2013). Scientists use artefacts and




models to help them formulate and test hypotheses (Giere & Moffat, 2003;

Watson, 1968), and people naturally recruit artefacts and tailor their

immediate physical environments to augment and transform their problem

solving activities. Once one recognises that cognition typically unfolds in a

brain-body-environment system, the question of whether artefacts form a

constitutive part of human cognition—e.g., to paraphrase Clark and Chalmers

(1998), whether Otto’s notebook is included in his mind—or the question of

whether or not cognitive processing only takes place inside brains both

become peripheral to the concerns of the systemic cognitivist. The important

issue is no longer where cognitive processing begins and where it ends. The

important issue becomes how cognition emerges from the interactions of

brain activity, motor actions, and artefacts. Systemic cognitivists do not

question whether there can be a scientific study of real-time cognition. Actions

are not unbounded: body and environment constraint the interactive

trajectory. To adopt a systemic approach to cognition is to reject the a priori

assumption that there are no general laws that can account for how cognition

emerges in situ, and embark on a scientific journey of discovery,

documentation and ultimately theoretical understanding of untapped

psychological phenomena.


Interactivity plays a constitutive role in thinking (and in shaping the

world that enacts it)

As it disregard actions in the world as merely peripheral, traditional

cognitive psychology has generally been reluctant to study thinking in

problem-solving environments that permit and encourage interactivity (Vallée-




Tourangeau & Villejoubert, 2013). Once that reluctance is shed, and modest

efforts go toward implementing interactive thinking environments in the lab,

there is much evidence that interactivity substantially transforms problem

solving (Vallée-Tourangeau, 2013; Vallée-Tourangeau, Euden, & Hearns,

2011). Thus, for example, we observed that interactivity promoted a

significantly higher rate of insight solutions in problems for which a false

algebraic expression with Roman numerals can be transformed into a true

one by moving a single feature of either a numeral or an operator (e.g., how to

transform I = II + II into a true expression). Weller, Villejoubert and Vallée-

Tourangeau (2011) demonstrated that participants were more likely to

discover the solution (e.g., I = III – II) if the expressions themselves were

presented as manipulable three dimensional objects—and participants were

invited to manipulate them— than if they were presented on a piece of paper

and participants announced their solution to the experimenter. Interactivity

matters because action-perception cycles quickly unveil a range of potential

solutions that help participants home in on the right one. Participants may

experience a form of impasse, but the opportunity to manipulate the physical

elements of the problem evinces a fluid set of action affordances, which

encourage participants to tinker and continue their exploration. In turn, when

participants are confronted with an unchanging algebraic expression, the

perceptual feedback exerts a strong conceptual pull back to the false

formulation: The participants must overcome this perceptual information by

simulating the transformations mentally. To be sure, the problem solving

exercise exacts a greater toll on working memory in a static than in an

interactive condition. But interactivity does not simply augment working




memory resources by letting the environment shoulder some of the

computational burden; this indeed “hugely downplays what is going on” (Kirsh,

2013, p. 178). Interactivity transforms the terrain of cognition: the thinking that

emerges from the intertwining of a reasoner’s internal resources and the

external artefacts is qualitatively different.

Performance on mental arithmetic problems provides another example

of how interactivity transforms thinking. Asking college-age students to add a

long series of single-digit numbers, without using pen and paper, does not

exceed their acquired arithmetic skills. However, a long series of numbers

arrayed in a random configuration puts a significant burden on working

memory—participants must remember the interim total, what numbers were

added, which one should be added next—and counting inaccuracies are likely

to arise. Of greater interest, is the strategy employed in this situation:

Participants are likely to adopt a simple strategy that conserves working

memory resources, scanning single numbers in turn, keeping track of the

running total. This slow and methodical strategy underexploits stored

knowledge of simple sums which further handicaps the ability to identify

congenial groupings and subtotal to enhance the speed and accuracy of the

process. The same sum presented in terms of movable number tokens that

participants are encouraged to move about will give rise to substantially

different counting behavior. As Vallée-Tourangeau (2013) demonstrated,

interactivity fosters more creative and efficient paths to solution in mental

arithmetic: participants are less likely to adopt a conservative counting

strategy, and better able to identify groupings that facilitate the creation of

congenial interim totals (e.g., such as those that divide by 5 without a




reminder). Interactivity thus enhances the robustness and efficiency of the

‘mental’ calculation process. Interactivity provides additional working memory

resources, but the transformative impact on thinking cannot be predicted by

the simple addition of external storage.

Thinking emerges from transactions between online and offline

cognition

Classical accounts of thinking put too great an emphasis on the role of

internal representations and mental processing. This is not to say that the

impact of the environment has not been featured in such accounts, however.

Core cognitive processes have often assumed to result from both inner

processes and the situation within which people are embedded while they

think (e.g., Neisser, 1967). The world, however, is assumed to be mirrored

(more or less accurately) in people’s heads and the important question

becomes “how an exploitative relationship between mind and environment

has implication for the kind of cognitive machinery used by the mind”

(Brighton & Todd, 2001, p. 324). The answer, according to proponents of the

ecological rationality approach is that adaptive behaviour emerges when there

is a match between information structure in the mind and in the world. Thus,

this conception of cognition remain subordinate to the doctrine of animal-

environment dualism (Turvey & Shaw, 1999). It conceives the mind and the

environment as disjointed entities and cognition as emerging from a “self-

actional” brain, which has evolved its computational mechanisms in symbiosis

with nature, and as a result may be more efficient in computing “natural”

information (e.g., frequency information rather than probabilistic information;

Gigerenzer & Hoffrage, 1995) but nevertheless computes information in a




timeless manner, and independently of its immediate environment. This can

be contrasted with a situated and interactional account of cognition, where the

immediate environment has a direct impact on what is processed by the brain

and how it is processed as well as where, conversely, what is processed by

the brain can have a direct impact on the immediate environment (e.g., where

the immediate environment is transformed the instantiation of a plan of action

initially elaborated in the brain). Such an interactional account of cognition,

however, is still limited by a mechanical conception of brain and environment

as having independent influences on cognition. Intracranialists will readily

grant the importance of the environment in shaping cognition and behavior:

“the apparent complexity of our behavior (!) is largely a reflection of the

complexity of the environment in which we find ourselves (Simon, 1969, p.

53). But the interaction between two independent entities can be parsed in

terms of input-output segments, betraying an insidious dualism. This research

programme misses the mark (see also Villejoubert & Vallée-Tourangeau,

2011). The dynamic context in which cognition and behavior emerge reflects

transactional forces that coincidentally and reciprocally shape cognition and

the world that enacts it. In our laboratory work on problem solving, the proto

representations of a solution and the world that in turn reflects it, as well as

offering the means to project its potential development, both co-evolve in

space and time to a more complex representation of the solution. The only

research methodology that can capture the genesis of insight in problem

solving research is one that examines the thoughts and behaviours of an

agent in real time in a dynamic and malleable physical environment. As such,

Steffensen’s (2013) cognitive event analysis alongside other methods of




applied behavior analysis offers a promising compass to guide these future

efforts.

Concluding Remarks

We draw considerable inspiration and guidance from a more radical form

of embodied cognition. However, the associated philosophical commitments

that jettison mental representations in thinking require a herculean dismissal

of voluminous evidence to the contrary. We also argue that traditional

research methods in cognitive psychology reflect a dualism that keeps mind

and environment in distinct ontological categories. Classical accounts of

thinking put to great an emphasis on internal representation and mental

processes and ignore the symbiotic relationship between thinking and acting.

A more fruitful research methodology will map the spatio-temporal coordinates

of the co-constitutive processes that enact, co-temporaneously, the mind and

the world to which it is connected. Thinking emerges in ecological space and

ecological time from the transactional flow of action and representational

opportunities outcropping from a dynamic action-environment interface.




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The Context-Dependent Nature of Action Knowledge

Nicholas J. Shipp ([email protected])Department of Psychology, University of Hertfordshire

Hatfield, AL10 9AB, UNITED KINGDOM

Frédéric Vallée-Tourangeau ([email protected])Department of Psychology, Kingston University,

Kingston upon Thames, KT1 2EE, UNITED KINGDOM

Susan H. Anthony ([email protected])Department of Psychology, University of Hertfordshire

Hatfield, AL10 9AB, UNITED KINGDOM

Abstract

Recent theories of semantic memory have proposed thatconcepts are grounded in sensorimotor activity and

mediated by the context from which the knowledge isdrawn (Barsalou, 1999, 2003, 2008). Conceptualknowledge draws upon information from all modalities and

therefore includes knowledge of associated object actionslinked with both function and general movement (Bub,

Masson, & Cree, 2008). The following experimentexamined the conditions under which action informationexerts an influence on experimental tasks particularly when

taxonomic information is present. The experiment used aforced-choice triad task giving participants the choice of

selecting between items that shared either a taxonomic oran action based relation with the target. The results showed

that when the objects were presented as images on a white background (context-lean condition), participants were

more likely to select the taxonomically related item. Incontrast, when the same triads were presented as images being used in a functional scene (context-rich condition)

they were more likely to select the action-related item. Theresults show that action knowledge is not automatic but is

context-dependent. In line with views on embodiedsemantics the motor cortex is activated and drawn upon

when objects are viewed and this influences task performance despite being unnecessary for the task.

Keywords: Action; Triads; Categorisation; Context;Embodied Semantics.

Introduction

The traditional view of conceptual knowledge suggestedthat concepts are formed from amodal abstractions of the

context in which they were previously encountered

(Barsalou, 1999; 2003; Yeh & Barsalou, 2006). However

an increasing number of researchers have shown that

concepts are embodied within sensorimotor activity

(Barsalou, 1999, 2008; Wu & Barsalou, 2009; Yeh &

Barsalou, 2006). Embodied semantics takes the view that

concepts are much more than decontextualised feature

lists and that they reside within the same sensory-motor

circuits in which they were first established (Aziz-Zedah

& Damasio, 2008; Fernandino & Iacoboni, 2010).

According to Barsalou (1999, 2003, 2008) the conceptual

system does not record the images seen of an entity, butregisters the concomitant neural experience. When

encountering or re-instantiating the concept at a later date

h l i ll i h l

patterns, thereby producing a simulation of the

experience. Areas of the motor cortex that are active upon

the initial object encounter will be reactivated and as such

common actions associated with objects should readilycome to mind when thinking of an object. Empirical work

over the last decade has supported this view showing that

semantic knowledge is embedded within physical actions

which influences performance across a variety of

cognitive tasks (Anelli, Nicoletti, & Borghi, 2010; Borghi,

2004; Borghi, Flumini, Natraj, & Wheaten, 2012; Bub &

Masson, 2006; Bub, Masson, & Bukach, 2003; Chao &

Martin, 2000; Creem & Proffitt, 2001; Iachini, Borghi, &

Senese, 2008; Tipper, Paul, & Hayes, 2006; Tucker &

Ellis, 1998, 2004; Vanio, Symes, Ellis, Tucker, &

Ottoboni, 2008).

What has been particularly evident from the research is

that action can play a role even in tasks where knowledgeof associated action is neither required nor asked for.

Borghi (2004) used a property generation task to showthat when participants are asked to simply think about an

object such as a car the first parts of the car that they name

are those related to direct human interaction—e.g., the

gearstick, steering wheel. The same parts were named first

when participants were given a direct context to think

about such as building the object. This would be in linewith the view that thinking about the car activated the

motor cortex and as such direct interaction became an

influential feature in this task. Helbig, Graf and Keifer

(2006) showed how actions are drawn upon in object

recognition using a priming task. In their experiment participants were asked to name both a prime and a targetobject and showed that participants were more accurate in

naming the target object when the prime was congruent in

its action manipulation. Here the prime had a clear effect

of activating the motor system and its relevant action

knowledge that remained active for the target and hence

identification was quicker. Helbig, Steinwender, Graf and

Keifer (2010) used video primes of an agent performing

an action on an object that was blacked out. Following the

prime participants then saw an image of an object

followed by a word, they were asked to identify if the

word matched the object. Participants were again more

accurate in their responses when the action seen in the prime matched the action of the following object. Further

priming studies have shown similar action-based effects



Research has also shown that when participants are

asked to make an action-based response they are typically

faster and more accurate when action-based knowledge is

taken into account. Jax and Buxbaum (2010) presented

participants with objects that they termed as being either

conflict or non-conflict. Non-conflict objects, they

suggest, are objects that require the same action to bothuse and transport them (e.g., drinking glass). In contrast

when the action of use is different from the action of

transport (e.g., as for a calculator) the object is referred to

as a conflict object. When participants were asked to place

their hand on the object as though they would either use it

or hand it to another person, they were faster for the non-

conflict items over the conflict items. In addition they also

found that generally participants were faster at making

hand placements to use the objects rather than to hand

them to another person. Osiurak, Roche, Ramone and

Chainay (2013) showed the reverse effect when

participants were physically asked to either pick up the

objects in order to hit a ping pong ball or pass it to theexperimenter. Here participants were faster at making

transport actions over use. The authors attribute this to

additional information being activated such as weight and

solidity, which would not be activated if participants are

simply asked to place their hand on the objects.

Yee, Chrysikou, Hoffman and Thompson-Schill (2013)

found that performing concurrent actions along with a

semantic decision task slowed down task performance.

Their participants undertook a semantic decision task

judging if spoken words were abstract or concrete while

concurrently performing a three-step manual ‘patty-cake’

task. This involved participants placing two fingers, four

fingers or the whole hand on its side onto a table top in arepeating manner while engaging in the semantic decision

task. Yee et al. found a significant interference effect of

performing such manual actions with slower decisions

made when compared to participants who performed no

concurrent task. The patty-cake task was found to have an

increased interference effect on those objects that

participants had greater levels of previous handling

experience. No interference was found when participants

performed a mental rotation task.

The Influence of Context

Research has shown that the context in which information

is presented influences what type of conceptual

knowledge becomes activated. Barsalou (1982) showed

that while certain types of information are contextually

independent and activated irrespective of the context other

types of information can be contextually dependent and

activated under certain circumstances. Barsalou showed

that in a property verification task participants were

quicker to verify that a basketball can float when given a

context requiring someone to use one as a floatation

device compared to verifying whether a basketball can

bounce or not. Therefore such information would be

contextually dependent as it does not always come

straight to mind for this task but varies according to

context. In contrast participants showed no difference in

verifying sentences regarding how skunks have an

unpleasant smell across different conditions This would

upon across different contexts. In supporting such

findings Borghi et al. (2012) showed that activation of

object affordances was contextually dependent.

Participants were shown object pairs that were related

either functionally ( paper + scissors), spatially ( stapler +

scissors) or had no relation (bottle + scissors). In addition

to this the pairs were also shown with either a hand with afunctional grasp on one of the objects, a manipulative

grasp, a hand present but not holding either item or with

no hand present. The participants were quicker and more

accurate at making decisions on the objects being related

or unrelated when object pairs shared a functional context.

Participants were also faster when pairs were presented

with a functional hand grasp rather than a manipulative

grasp, however overall responses were faster when the

images were presented with no hand. Items that share the

same function also share a related goal. As such it is

possible that participants drew upon the related goal of the

items that in turn decreased the reaction time between

them. Since manipulative pairs share only a spatialcontext and no related goal they were slower than the

functional pairs.

The Present Experiment

The first aim of the current experiment was to explore the

role that action plays in category formation when it is

presented in conjunction with category membership. It

should be noted that for the purposes of this experiment

action is defined as the direct interface between objects

and the human body. For example the action of a rifle

would be the grasp made by the hand around the handle,

rather than action in terms of the act that can be carried

out using a rifle. For example an orange and a banana both require a peeling action and also belong to the same

category of fruit . In addition many items can share an

action without sharing category membership such as a

rifle and a water pistol which both require a grasp of the

handle in the same fashion but are not both weapons.

Therefore a task was designed in which category

membership could be directly pitted against the action of

interfacing with an object when using it in its functional

capacity. Using this method it could be tested if

participants would always choose category membership or

if action knowledge would be drawn upon in line with

recent views on embodied semantics. The experiment

used a forced-choice triad task. This basic task has been

used to demonstrate the influence of situational (thematic)

information (Lin & Murphy, 2001; Murphy, 2001) when

previously only taxonomic (shared property) information

would have been predicted to guide choices. In Lin and

Murphy’s (2001) studies, the choice items were selected

to share either a taxonomic or a thematic relation to the

target. For example the target bee was presented with

wasp (taxonomic similarity) or honey (thematic

similarity). In a similar manner to Lin and Murphy,

participants in the present experiment were presented with

a target and two choice options, only one of which shared

an action with the target. Based on previous research

showing the strong role of action knowledge in a range of

tasks based on category knowledge, we predicted that

participants would be more likely to select the action



Figure 1: Examples of stimuli employed in the experiment. From left to right: Same Category Object triad, Different

Category Object triad, and Perceptual Category Object triad in the context-lean condition (top panels) and in the context-

rich condition (bottom panels).

shared a taxonomic relation to the target. When the

action-related item bore no taxonomic relation to target,

we predicted that participants would be more likely to

select the taxonomic choice.

The second aim of the experiment was to investigate the

nature of action information and whether its activation is

contingent on the context of categorisation. The triads

shown were manipulated between subjects based on

context. Participants either saw the objects on a white background (context-lean condition) or shown within an

action based scenario with the objects being used by an

agent (context-rich condition). It was predicted that in line

with Borghi et al. (2012) the action choice within the

triads would be selected more often when the images are

shown within a functional context.

Method

Participants

Fifty undergraduate students (36 females) from the

University of Hertfordshire participated in return forcourse credit with a mean age of 25.3 years (SD = 7.9,

Range = 18-49).

Materials

The triads were based on the standard design of a target

item followed by two items from which a choice could be

made. Since the aim of the research was to compare the

effect of action knowledge both alongside and set against

taxonomic information, two sets of triads were initially

designed, namely same-category object (SCO) triads and

different-category object (DCO) triads (see Fig. 1). In the

SCO triads participants saw a target (e.g., orange) and

two choice items (e.g. , banana and strawberry) which all belonged to the same category ( fruit) as confirmed by

pilot work. Of these choice items both share category

shares a motor action with orange. In the DCO triads, one

choice item shared category membership with the target

but not an action (rifle and sword ). The remaining choice

item shared a motor action with the target but not category

membership (rifle and water pistol ).

In order to test the effect of context two sets of images

were collected. The first set showed the objects against a

white background (context-lean condition). The second

set projected the objects being used in a functional context(context-rich condition, see Fig. 1). Twenty participants

not used in the experiment took part in pilot work to

ensure that the SCO and DCO triads were matched in

terms of category membership and the action used to

functionally interact with them. Fifteen of each triad set

were initially designed and piloted. Using a Chronbach’s

alpha level of .7 as a threshold criterion, the final sets of

SCO and DCO items were composed 10 triads of each

type.

A third set of triads (PCO) was designed based on the

results of experimental pilot work. It seemed possible that

participants might select the choice item sharing an action

not because they shared an action, but because they shared perceptual properties. For example pencils and

paintbrushes share perceptual properties, in part as a

function of the ergonomic constraints that guide their

design. Using the triads described above it is not possible

to ascertain whether such items are selected because of

action or because they look the same. In the PCO triads

neither of the choice items shared category membership

with the target item. One of the choice items shared an

action with the target but few perceptual features (nut and

car key). The remaining choice item shared perceptual

features with the target but not an action (nut and money).

The PCO triads were again presented in the same context-

lean/context-rich manner as the SCO and DCO triads (see

Fig. 1). Twenty participants not used in the experimenttook part in pilot work to ensure that the PCO triads were



action used to functionally interact with them. Fifteen

PCO triads were initially designed and piloted. Using a

Chronbach’s alpha level of .7 as a threshold, a final set of

10 PCO triads were selected. Thus the experimental

material was composed of 30 triads consisting of 10 SCO,

DCO and PCO triads.

Procedure

The experiment employed a 3x2 mixed design where triad

type was a repeated measure and context was a between

subjects factor. The main dependent measure was the

percentage of action choices calculated for each set of

triad type in both context conditions. Stimuli and task

instructions were presented on a 15” Macintosh laptop.

Participants were instructed to “Please indicate which of

the two items goes best with the item at the top of the

screen”, developed from Lin and Murphy (2001). A

fixation cue was presented on the screen for 1000ms after

which the cue disappeared and the target word appeared

along with the appropriate picture depending on whichcondition the participant was assigned to. After 1500ms

the two choice options appeared beneath the target

alongside the appropriate images. The triad remained on

the screen while participants made their choice.

Participants were instructed to press the ‘a’ key to choose

the item on the left-hand side of the screen and the ‘l’ key

for the item on the right-hand side of the screen. The

choice items were counterbalanced across the triads so

that in half the triads the action choice appeared on the left

hand side while in the remaining half the action choice

appeared on the right. After they had made their choice

the triad disappeared and the fixation cue appeared again

for the next triad.

Results

The mean percentage action choices for each of the three

triad types in both the context-lean and context-rich

conditions are illustrated in Figure 2. As can be seen, the

SCO triads produced the highest percentage of action-

related responses in both the context-lean (61%, SD =

15%) and the context-rich condition (70%, SD = 15%).

The action choice was selected least often with the DCO

triads, though the mean was greater in the context-rich

condition (53%, SD = 21%) than in the context-lean

condition (32%, SD = 13%). In the PCO triads participants chose the action item less often than the

perceptual item in the context-lean condition (48%, SD =

20%) but more often in the context-rich condition (69%,

SD = 14%). For all three triad types participants selected

the action choice more frequently when contextualised. A

3x2 mixed analysis of variance revealed that the main

effect of context was significant, F (1, 48) = 39.22, p <

.001, !2 = .45. Participants were more likely to select the

action item in the context condition when pictures of the

objects were shown in a functional context. The main

effect of triad type was also significant, F (2, 96) = 22.77,

p < .001, !2 = .32. Post hoc analyses using the Bonferroni

adjustment showed that participants selected more actionchoices overall on the SCO triads than in both the DCO

triads ( p < .001) and the PCO triads ( p = .031).

triads than in the DCO triads ( p = .001). The interaction

between triad type and context was not significant, F (2,

96) = 2.33, p = .10, !2 = .05.

"#$%&' () Mean percentage action choices with SameCategory Object (SCO), Different Category Object

(DCO), and Perceptual Category Object (PCO) triads in

the context-lean condition (light grey bars) and in the

context-rich condition (dark grey bars). Error bars are

standard errors of the mean.

Discussion

The experiment reported here sought to investigate the

role of action in shaping categorical decisions. The first

aim was to measure how action knowledge was used in

the forced-choice triad task when pitted both against and

alongside taxonomic information. The results from the

different category object (DCO) triads showed that whenaction knowledge was pitted against taxonomic

information participants primarily grouped items together

based on taxonomic information. For example participants

were more likely to put rifle with sword rather than water

pistol . The finding that shared action could not overcome

taxonomic constraints is perhaps not surprising given the

central role that functional knowledge plays in category

membership. Participants were most likely to select theaction choice when it was combined with taxonomic

information, as with the same-category objects (SCO),

therefore showing that knowledge of action is perhaps

insufficient on its own to act as a basis for category

membership. Therefore while shared action may not be

considered a sufficient basis in which to form categoriesas gauged by this task, it does appear to have an additive

effect increasing the shared relations between two items.

The perceptual-category object (PCO) triads were

designed specifically to determine whether participants

were selecting the items based on shared action or shared

perceptual properties. If participants were drawing uponaction knowledge then in such pairs where the choice

comes down to an action or a perceptual choice they

should pick the action. In contrast if perceptual

information is driving choices then participants should

pick the item that looks more similar. The results showed

that action knowledge was more likely to be used on thePCO triads over perceptual similarity when shared

category membership was removed Therefore in line with

0%

10%

20%

30%

40%

50%

60%

70%

80%

SCO DCO PCO

P e r c e n t a g e o f A c t i o n C h o i c e

Triad Type

Context Lean Context Rich



participants were drawing upon knowledge of how they

interacted with the objects rather than how they looked.

The second aim of the experiment was to see if action

knowledge is drawn upon in all situations or whether its

use in such tasks is context-dependent. The results showed

that when participants saw the items in the context-rich

condition they were more likely to select the action item.Since objects can cue a variety of potential actions, it is

possible that when participants viewed the images in the

context-lean condition, multiple actions were simulated

and as such participants perhaps did not focus on the

shared functional action between the objects. In contrast

the context-rich condition clearly showcased the objects

being used in their standard capacity and as such the

shared actions inhibited the simulation of a broader range

of potential actions. The relevance of action knowledge in

driving categorisation intuitions is thus contingent on the

context of presentation.

In order to fully extend this research a new set of triads

would need to be designed in which the items share anaction along with taxonomic information, but do not share

perceptual properties. While this would be the ideal

condition there might be insurmountable constraints on

designing the material required to run this experiment: In

aiming to optimise the functionality of the human-artefact

interface, objects that share an action will invariably share

perceptual properties. For example pencils and

paintbrushes look similar as they are designed to be used

with a pinch grip and rest within the thenar space of the

thumb and index fingers. Items sharing category

membership further confines this problem as items

become more similar to each other based on the

ergonomics of design. Therefore it might proveimpossible to find items that require the same method of

interaction/operating but that did not share the perceptual

properties linked with that action.

The results of the experiment have brought attention to

the circumstances under which action knowledge informs

categorisation intuitions in a passive cognitive task. The

results from the current experiment suggest that action

knowledge plays an important role in categorisation. As

revealed with the same-category object triads, participants

were most likely to group items together when they shared

category membership and functional action. The use of

the perceptual-category object triads showed that this was

not due to shared perceptual features between items thatshare an action. However when participants were asked to

choose between either an action or category membership

participants were most likely to choose the latter, until

such items were shown in a functional context. When

participants saw the items in use they were more likely to

group the target with the item sharing a functional action.

This suggests that viewing items without a context is not

enough to instantiate action knowledge and is supported

by previous research (Borghi, Bonfiglioi, Lugli,

Ricciardelli, Rubichi, & Nicoletti, 2007; Borghi et al.,

2012). Action responses are more frequent because such

knowledge is accessed more readily upon presentation of

objects in a functional context. This is most likely theresult of activation of the motor cortex through both the

mirror and the canonical neuron systems Canonical

well as interact with it, where mirror neurons also become

active when they view another person interact with the

object (Grèzes, Armony, Rowe & Passingham, 2003). In

the present context-rich condition, participants not only

saw the object but an agent interacting with it. Research

shows that the motor cortex is activated upon viewing

physically manipulable objects (Chao & Martin, 2000;Hauk, Johnsrude & Puvermuller, 2004) suggesting that

some form of simulation is occurring allowing

participants to draw upon action related knowledge.

Under these conditions the associated actions are more

salient and as such more likely drawn upon with the

triads. This could also explain why action choices were

lower in the context-lean condition as viewing these

conditions should activate the canonical neuron system

but not the mirror neuron system. Hence the action

choices are less likely to be drawn upon in these

conditions. The data here clearly show that action

knowledge and motor affordances are more likely to be

drawn upon when stimuli are presented within afunctional context.

In conclusion the data reported here indicate that action-

related information is influential when participants are

engaging in categorisation tasks that do not require any

action to be made. This effect is made even more salient

when the presentation of the objects is embedded in an

action-relevant context. It has further been shown that

while perceptual information plays a strong role in

categorisation there are circumstances when action

knowledge is chosen over perceptual information. The

results are consistent with views on embodied semantics

that the motor cortex is activated when objects are

perceived and this activation influences task performance based on such shared action knowledge.

Acknowledgments

We are grateful to Lia Kvavilashvili for her advice

throughout the preparation of this work and comments on

earlier drafts.

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384.



HYPOTHESIS AND THEORY ARTICLEpublished: 24 February 2014

doi: 10.3389/fpsyg.2014.00116

Tool use as distributed cognition: how tools help, hinderand define manual skill

Chris Baber 1*, Manish Parekh 1 and Tulin G. Cengiz 2

1 School of Electronic, Electrical and Computer Engineering, University of Birmingham, Birmingham, UK 2 Department of Industrial Engineering, Uludag University, Bursa, Turkey

Edited by:

François Osiurak, Université de Lyon,

France

Reviewed by:

Blandine Bril, Ecole des Hautes

Etudes en Sciences Sociales, France

Lewis A. Wheaton, GeorgiaTech, USA

*Correspondence:

Chris Baber, School of Electronic,

Electrical and Computer Engineering,

University of Birmingham, B15 2TT

Birmingham, UK

e-mail: [email protected]

Our thesis in this paper is that, in order to appreciate the interplay between cognitive

(goal-directed) and physical performance in tool use, it is necessary to determine the rolethat representations play in the use of tools. We argue that rather being solely a matter of

internal (mental) representation, tool use makes use of the external representations that

define the human–environment–tool–object system.This requires the notion of DistributedCognition to encompass not simply the manner in which artifacts represent concepts but

also how they represent praxis. Our argument is that this can be extended to include howartifacts-in-context afford use and how this response to affordances constitutes a particular

form of skilled performance. By artifacts-in-context, we do not mean solely the affordancesoffered by the physical dimensions of a tool but also the interaction between the tool and

the object that it is being used on. From this, “affordance” does not simply relate to the

physical appearance of the tool but anticipates subsequent actions by the user directedtowards the goal of changing the state of the object and this is best understood in terms

of the “complimentarity” in the system. This assertion raises two challenges which areexplored in this paper.The first is to distinguish “affordance” from the adaptation that one

might expect to see in descriptions of motor control; when we speak of “affordance” asa form of anticipation, don’t we just mean the ability to adjust movements in response

to physical demands? The second is to distinguish “affordance” from a schema of thetool; when we talk about anticipation, don’t we just mean the ability to call on a schema

representing a “recipe” for using that tool for that task? This question of representation,specifically what knowledge needs to be represented in tool use, is central to this

paper.

Keywords: distributed cognition, tool use, affordances, representation, extended mind, systems dynamics

INTRODUCTION

The central question for this paper is what representations are

employed when using tools? In this paper, the term “representa-

tion” is taken to mean a set of parameters which describe an action(from goal to execution). In broad terms, one answer to this ques-

tion might see the set of parameters as being specified prior to an

action being performed, e.g., in the form of an action schema, or

as being recruited in preparation of the action, e.g., in the formof activation of specific brain regions. In this case, the question

becomes one of identifying what the representation might contain

and where it might be stored. This is what we refer to as an “inter-

nal representation.”Alternatively, the parameters might arise fromthe performance of the action in response to constraints imposed

by the environment, e.g., in the dynamic behavior of a system.

This is what we refer to as an “external representation.” We argue

that, while there is evidence to support the view that tool use canbe guided by“internal representation,” this only provides a partial

view of such activity and that the use of “external representation”

can provide a viable alternative account.

The position taken in this paper assumes that the physical

behavior of the person can be viewed as part and parcel of theircognitive activity, and that there is a close coupling between a

person’s action and their perception of features of objects in the

world. However, neither assumption fully captures human activ-

ity when using physical objects for goal-directed activity (whichis the broad definition of tool-use employed in this paper). Thus,

we argue for a broader appreciation of Gibson’s (1979) notion of

complimentarity as an explanation of affordance at a “system”level.

The notion of “system” here draws on Maravita and Iriki’s (2004)idea of the “hand-tool body schema” but we extend this to cover

person–environment–tool–object. For us, this requires the notion

of Distributed Cognition to encompass not simply the manner

in which artifacts represent concepts but also how they represent

praxis. In other words, the design of the tool (as a human-madeartifact) reflects not only the manufacturing process but also a set

of assumptions about howthat tool should be grasped and manip-

ulated, and how activities involving that tool can be performed“correctly.” This means that “tools” are distinct from other physi-

cal objects in the human environment because their use is defined

not only by their appearance or the user’s goals but also by cultural

constraints that have influenced their production (Baber, 2003,2006; Burghardt et al., 2011). While there are instances in which

other physical objects, such as sticks or stones, can fulfil tool func-

tions, and while the neurological evidence suggests that images

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Baber et al. Tool use as distributed cognition

of these objects activate similar regions in the brain to images of

tools, there is accumulating evidence that the pattern of brain acti-

vation for tools is somewhat different from that of physical objects

per se.

“Humanbeings,viewedas behaving systems,are quite simple.The appar-

ent complexity of our behaviour over time is largely a reflection of the

environment in which we find ourselves” [Simon, 1969]

While Simon was not talking explicitly about Distributed Cog-

nition, this quotation points to the need to understand human

behavior in the environment in which it occurs. For us this

implies a need to better understand how the environment makesan impact on our actions and decisions, and this suggests the ben-

efit of an approach which studies human action as they occur

in natural (or as near natural as possible) conditions. This raises

challenges for “ecological validity” (Neisser, 1967) which takes usout of the laboratory (or, for that matter, the brain scanner) and

into the settings in which activity is performed. A primary rea-

son for this quest is the assumption that the relations between

human, environment, tool, and object are fundamental to the

study of perception and action (Gibson, 1979; Beek and Bing-ham, 1991; Newell, 1991). A study of the activities of tool use

away from typical environments runs the risk of ignoring the con-

straints that the environment places on the performance of these

activities. Thus, it is vital to ensure that enough of the character-istics of the person–environment–tool–object system are reflected

in the design of studies (even if these are conducted in labora-

tories). We are interested in ways in which we might be able

to capture data from the tool using actions of people in work environments, through analyzing video of their activity (and dis-

cussing these videos with them) or through putting sensors on

the tools that they use. For this paper, the focus will be on the

use of data collected from sensors on tools. Two areas of activ-

ity will be used in this paper: using hand-tools in jewelery andeating with cutlery. In both areas, the concern will be to com-

pare experienced and less experienced users of the tools. The

comparison will be qualitative rather than quantitative, i.e., exam-ples of the data collected during our studies will be presented

but more detailed analyses of these data will be found in other

papers.

WHAT NEEDS TO BE REPRESENTED IN TOOL USE?

By way of a definition of the word “tool,” we propose that a

tool is a physical object which lends itself to manipulation by a human (or animal) in order to solve a problem presented by

objects in the physical environment. This notion of tool-use as a

form of problem-solving not only emphasizes the goal-directedaspect of using tools but also the need to respond to, and over-come, constraints. This definition allows us to combine both the

physical action of manipulating the tool with the cognitive aspects

of goal-directed, purposeful behavior. Following a similar line of

argument (tool use as problem solving), Osiurak et al. (2010) sug-gest that the coordination of the physical actions involved in using

tools represent a problem to be solved. They view cognition and

physical activity in a dialectic in which a particular goal encour-

ages the perception of particular affordances in the world and

serves to influence the bodily action to perform, which, in turn,

moves the person towards their goal. This strikes us as an ele-

gant reformulation of the notion of affordance as a goal-directed,physical response to the environment. The difference between this

view and the one presented in this paper is simply (we believe)

a matter of scale: rather than considering problem solving in the

broad terms that Osiurak et al. (2010) offer, our focus is on theinterface between tool and object (or, rather, we propose that the

“problem” that concerns tool users is how to modify the object inways that satisfies a goal, given the constraints that the tool (and

the tool-users’ ability to wield that tool) might impose on theiraction).

In order to explore further the question of representation in

tool use, it is important to consider what needs to be represented

in order to use a tool. Tool use is not only a matter of recog-nizing that an object is a tool but also of knowing how to hold

and manipulate that particular tool. It is also a matter of under-

standing the consequences of a particular way of using a particular

tool. Knowing that a piercing saw (used by jewelers to cut metals)is held vertically for cutting (with the wrist more or less locked

and most of the motion about the elbow), and has teeth which

cut in one direction, leads to an understanding that the cut ismade on the downstroke (not the upstroke), and helps define aset of possible actions when using this tool. From this it might

appear that we are arguing for (at least) some representations

of the tool and the actions associated to be internal to the per-

son. Does this mean that these representations are stored in the

brain?

INTERNALREPRESENTATION: NEURAL ACTIVATION IN TOOL

USE

The suggestion that the use of tools depends on “internal models”

is nicely encapsulated in a recent paper by Imamizu and Kawato

(2012). They review literature and report studies which indicate

the existence of both a feed-forward model, taking efference copiesof motor commands to enablemotiondynamics, andinversemod-

els used to manage these dynamics. During learning, changes in

cerebellar activity indicate the acquisition and refinement of such

models. As we argue in this paper, the notion that brain-based“internal models” are causal represents a particular view of tool

use, and we are proposing that it is possible to explain much of

the activity involved in tool use through a combination of Dis-

tributed Cognition and dynamics which might not be representedin the brain per se . However, beforeexploring this proposal further,

we consider some of the neuropsychological evidence relating to

tool use. Imamizu and Kawato (2012) review neuropsychological

studies of tool use and suggest that, “[A]lthough the brain regions

related to each typeof component cannot be uniquely determined . . .

” (p. 325) there are two distinct functional regions of the brain

related to tool use: one related to the physical skills involved in

dextrous tool manipulation, and one related to the semantic andconceptual knowledge relating to the functions of tools (see also

Lewis, 2006; Higuchi et al., 2007, 2009). These distinct regions are

discussed in more detail in the rest of this section.

In their now classic study, Chao and Martin (2000) used func-

tional magnetic resonance imaging (fMRI) to show that viewingand naming of tools led to activation of the left ventral premotor

cortex, suggesting a strong relationship between the physical

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appearance of objects and the fact these objects could be acted

upon. Grafton et al. (1997) used positron emission tomography

(PET) scanning of participants asked to observe or (silently) name

tools and their use. Observation of tools resulted in strong acti-vation of the left dorsal premotor cortex, and (silent) naming of

these tools resulted in additional activation of Broca’s area. How-

ever, naming the use of the tools led to activation in Broca’s area,

together with activation in left dorsal premotor cortex, left ven-tral premotor cortex, and left supplementary motor area. This

implies that naming the use of a tool (even when the action is

not performed with it) has motor valence which is additional to

that obtained when looking at the tool. It also suggests that thephysical appearance and name of a tool activates slightly different

areas than the use of the tool. Taken together these, and related,

studies imply that brain activation relates to specific properties

of the tool-as-form and tool-as-function, and that these proper-ties are not solely related to a tool’s physical appearance but also

to how it moves or how it is used (Johnson-Frey , 2004; Martin,

2007).

One suggestion is that representations of tools are held in spe-

cific regions of the brain and become activated during activities inwhich similar objects are used. According to Gallivan et al. (2013)

the distributed coding of different actions associated with hand

movement and tool use imply that these actions are represented

separately and then integrated in the frontoparietal cortex. As Yeeetal. (2013) show, in an ingenious experiment, asking people to

think about manipulable objects when they are performing man-

ual actions which are incompatible with those objects is difficult

(but it easy to think about non-manipulable objects during theperformance of such actions). This suggests that the meaning of

objects (specifically in terms of their properties which support

manipulation) is recruited during action, and that incompatible

action interferes with this. Furthermore, work by Hoeren et al.

(2013) points to the suggestion that the recognition of action(performed by other people) is processed using distinct streams:

the dorso-dorsal stream focusing on movement determined by

the properties of the objects being used, and the dorso-ventralstream focusing on functional appropriateness and dexterity of

task performance.

KNOWLEDGE OF (FAMILIAR) TOOLUSE

The discussion so far points to the need to draw on knowledge of

theappropriatenessof a given tool fora given task andhow to wield

that tool to achieve the most effective result. Riddoch et al. (2006)presented patients (manifesting visual extinction) with images of

pairs of objects. The pairs showed objects which people are likely

to have experienced being used together (e.g., a bottle and a glass),or objects which could plausibly be used together, although mightnot have been experienced as such (e.g., a bottle and a bucket),

or were randomly paired in order to, as far as possible, produce

pairs which had no association. The results showed that com-

monly paired objects were identified more quickly than plausibly paired objects which, in turn, were identified more quickly than

the randomly paired objects (although this latter finding only held

when the image showed the objects being used together rather

than having them presented side by side). One implication of thiswork (which could be applied to normals as well as patients) isthat

the common and plausible pairs activate familiar routines in tooluse. In contrast with this observation, Vingerhoets (2008) found

that presentation of images of “familiar”or “unfamiliar”tools acti-

vated the same brain regions, with “unfamiliar” tools generating

more activation in the left hemispheric medial posterior occipi-

tal and inferior posterior temporal areas (in comparison to imagesof “familiar”tools) and more activation around the supramarginal

gyrusfor thefamiliar tools. While these results showedstrong indi-vidual differences, they also imply that the activation in response

to “familiar” tools can be associated with knowledge of the appro-priate hand position for the use of the tool (as opposed to simply

whether or not the tool could be grasped).

A similar line of argument comes from studies in which par-

ticipants are asked to pick up handled objects (such as cups)when the handle faces either towards or away from the hand

that they are instructed to use (Tucker and Ellis, 2001). For

example, Bub et al. (2012) presented images of everyday objects

together with images of hands in different orientations. Theobjects all had handles which were either oriented horizontally,

e.g., pliers, frying pan, or vertically, e.g., beer mug, hairdryer.

Participants were asked to name the object. Reaction (naming)time was significantly faster when both hand and wrist orien-tation matched the type of handle, or when neither hand and

wrist orientation matched the handle, but much slower when

either hand or wrist orientation was incongruent. Relating this

to the previous discussion of neural imaging, one can assume thatthe photographs of the hands and the objects might have acti-

vated different regions, with a combination occurring prior to

response.

The suggestion that there might be preparative neural activ-

ity which corresponds to different types of action (Rizzolattiet al., 1988) could provide evidence for the recruitment of a

set of representations determining task performance. Certainly

the movement-related cortical potential (MRCP) recorded fromelectroencephalography (EEG) begins 2–3 s before the onsetof movement (Toma and Hallett, 2003; Wheaton et al., 2005).

Furthermore, onset seems to be proportional to complexity of

movement, with more complex movements having longer onset

times. Such activity, typically in the left posterior parietal cor-tex, is taken to indicate the need to manage complex motor

activity and, as Wheaton et al. (2005) propose may include

“ . . .imagining executing such movements; the goal of the move-

ment; determining the natural position and setting required for proper performance; sequence of motor acts and comprehension of

the task.” (p. 535). While we have every reason to accept that

complex movements involve recruitment of appropriate muscle

groupings and specification of appropriate control parameters,we do not see why this necessarily involves the definition of

specific representations of the task context. Thus, our debate is

not with the neurological evidence per se but with the assump-

tions that these must point to internal representations which drive

behavior.What is interesting in the Bub et al. (2012) study is less the

reinforcing of activation of congruent images (or, indeed, the

effect of incongruence) than the problems caused when one of

the hand images did not match the other image or the object.Bub et al. (2012) suggest that this reflected disruption of the plan














being developed in working memory (with the images activat-

ing particular judgments about using tools). However, the images

presented in these studies serve as the (external) representations

about which people are asked to make judgments. As such theidea that they would need to create corresponding internal rep-

resentations in order to make such judgments seems a little odd.

The images that are presented provided sufficient information to

make a judgment and the need is to determine whether theseare “true” or “possible.” On the one hand, it seems plausible to

assume that prior experience provides the “grounding” (Mizelle

and Wheaton, 2010) of a tool in terms of its usage, but on the

other hand, it is equally plausible that this could be part of theper-son’s action repertoire (e.g., in terms of Bernstein’s (1967) idea of

coordinative structures) as it is activation of specific regions of the

brain.

For an action in which participants had to use different toolsto touch a target, precuing the target had no benefit on per-

formance, but precuing the tool to use had significant benefits

(Massen and Prinz, 2007). We take this to suggest that the pre-

cuing of the tool enabled the recruitment of the appropriate

“coordinative structure,” to use Bernstein’s (1967) phrase describ-ing combinations of muscle enervation and limb movement, to

perform the task with a given tool. What is interesting about

this interpretation of their findings is that “representation” need

not be same for different tasks (and, we would argue, showshow it can shift to outside the brain per se ). Hermsdörfer et al.

(2006) compared performance of apraxic patients with a con-

trol group of normals on a sawing task. Participants were asked

to demonstrate sawing under three conditions: when they wereshown a photograph of a saw and asked to pantomime saw-

ing; when they were shown the photograph, given a piece of

wood (the same size as the saw’s handle) to hold and asked

to pantomime sawing; when they were given the actual saw to

hold. While the controls showed fairly consistent performanceacross the three conditions, apraxic patients showed motion

errors (deduced from 3D motion tracking) in the first two con-

ditions. Typically, these errors involved substituting mediolateralmotion for the anteposterial motion expected. Interestingly, these

errors were not apparent when the apraxic patients were given

the actual saw to use. On the one hand, this supports a com-

mon finding in apraxic studies (that providing people with the

physical object seems to enable them to perform tasks moreeffectively and reliably than when they do not have the object

to hand). On the other hand, we believe it tells us something

about the need for internal representation when using tools.

Hermsdörfer et al. (2006) conclude that “ . . . pantomiming the

use of a tool and actually using the tool are facilitated by largely different neural processes which differ in demands and goals.”

[p. 1651]. We would argue further that these differences arise

because the use of the tool involves the control of the person–environment–tool–object system and need not dependon internal

representation.

CONCLUSION

Just because the tool-using behaviors have neural correlates does

not mean that these are the only places in which representations

for the behaviors exist. Clearly, the type of grasp is likely to be

influenced by the action which one intends to perform with the

tool. We have a repertoire of appropriate grasps for manipula-

ble objects, and we adapt these grasps according to contextualdemands. The adaptation often occurs with sufficient fluency and

speedto make it unlikely that we have simply retrieved a particular

piece of “motor schema” from memory and applied this; indeed,

the very notion of a “motor schema” (with its attendant implica-

tion of stored sequences of action) has been called into question(Sherwood and Lee, 2003; Shea and Wulf , 2005). Thus, we argue

the tool user is, partly, using the tool to make changes to objects in

the environment, but also partly using the tool to help create fur-

ther opportunities in the environment for using the tool. In otherwords, tool use is an interplay between seeking a defined goal and

managing the affordances arising from changes in the object in the

environment (resulting from the ongoing use of tools). Before dis-

cussing the collection of data and their analysis, the next sectiondescribes the particular stance taken in this paper: Distributed

Cognition.

EXTERNAL REPRESENTATION: DISTRIBUTED COGNITIONAND THEEXTENDEDMIND

As the phrase implies, Distributed Cognition addresses situa-

tions in which the processing of information occurs outside thebrain. For some writers, this is the proposal that the environment

and the objects it contains can shape the way in which cogni-

tion is performed (Zhang and Norman, 1994; Hutchins, 1995a,b;

Scaife and Rogers, 1996). While this position could be seen asparaphrasing the well-known observation that the representation

of a problem space influences the strategy that problem solvers

apply (Chase and Simon, 1973; Larkin et al., 1980; Chi et al.,

1981), e.g., changing the layout of a puzzle can make it easieror harder to solve, it also points to the importance of interactiv-

ity in behavior. For example, people playing Tetris or Scrabblecan benefit (in some situations) by being allowed to manipu-

late and rearrange the playing pieces (Kirsh and Maglio, 1994;

Maglio et al., 1999). This points to the need to not simply focuson the arrangement and design of the problem representation, but

also on the nature of the interaction between person and objects.

From this point of view, “embodiment” becomes an essential fea-

ture of acting not only on the objects but also on the cognitivetasks involved in problem solving. In other words, rearranging

the pieces is not simply performed in order to assist thinking, it

is thinking. This is taken to mean that the relationships within

the human–environment–tool–object system not only supports(or affords) different actions but also shapes cognition (Wilson,

2002). The reason for this is that activity within this system isoften time-limited, in that the actions are performed at speed, in

real-time and offer little opportunity for planning (what Clark ,1997, has termed “mind on the hoof”). From this, the main

purpose of cognition (in tool use) is to support action in as

situation-appropriate manner as possible. It also suggests that,

rather than needing to construct “internal representations” of the environment, it is sufficient to respond to the appearance

of the environment. From his work with robots, Brooks (1990)

pointed out that robot performance could be more efficient if they

spent less time “planning”and creating representations, and more
















time “acting” because “the world is its own best model” [Brooks,

1990, 12].

CONSIDERINGAFFORDANCE

Our reading of Gibson’s (1977, 1979) concept of affordance lies inhis notion of “complimentarity” in which the properties of objects

in theenvironment are responded to by the animal. Turvey (1992)

offersthe term“effectivity”as a way of capturing these propertiesof theanimal. So,one way of seeingaffordance lies in the complimen-tarity between the object’s properties and the animal’s effectivity.

One of the problems that the idea of “effectivity”and “properties”

raises is the suggestion that these are separate aspects which are

brought together during the performance of a task, which impliesthat they are independent, autonomous features which become

coupled during task performance. Indeed, Gibson (1979) suggests

that the “affordance” exists whether or not the observer perceives

or attends to it. If this is the case, then it makes sense to assumethat one aspect of the “effectivity” of the task performer would be

the neural representations of the actions involved in performing

the task (as well as morphological features and motor skills).

There are many situations in which the observer cannot butattend to the affordance, e.g., perseveration in the behavior of

stroke patients, or response to “fake” cues by animals. Rather than

implying (as Gibson seems to) that the“affordance”is an invariant

property of the environment, the fact that perseveration is anunusual state of affairs suggests that humans (and some animals)

are able to choose to respond to affordances (and by implication,

to see affordances in different situations). This implies that what

is essential to “affordance” is this combination of the property of

the object in the environment and the effectivity of the specificindividual (with specific knowledge, skills, abilities, and goals)

in that environment. While the properties of the object in the

environment may well be invariant, the actual affordance arises

from the complimentarity of environment andactor. Affordance ispartly a matter of perception-action coupling and partly a matter

of intention (goal) – action coupling. Perhaps a better way of

putting this is that perception-action coupling is mediated by the

intentionality of the actor. However, as Chemero (2009) pointsout, the idea of separable components that can be coupled runs

counter to the notion of complimentarity; taking affordance as the

result of the system created by person–environment–tool–object

(as we do in this paper) leads to the conclusion that this is asystem which is non-decomposable and which exists only during

the performance of the given task. From this, we suggest that the

goal, in the person–environment–tool–object system is partly held

by the person (in terms of the effect that they intend to produce

on the object) andpartly situated (Suchman, 1987) in the ongoinginteractivity in the system. This assumption echoes the earlier

assertion of van Leeuwen et al. (1994) that tool use can be “ . . .

defined as performing an action on a target by performing an action

on a tool. The action on the tool is embedded in the action on the target.” [van Leeuwen et al. (1994), p. 188–189]. For van Leeuwen

et al. (1994) this embedding reflected a “higher-order affordance

structure” of “mutually constraining complimentarities.”

van Leeuwen et al. (1994) argued that it was important tounderstand the role of context in task performance in terms of a

sufficiency principle , i.e.,“if an affordance has already been realized,

there is no need to take it into account.” [van Leeuwen et al. (1994),

p. 190]. To take this a little further, Turvey (1992) suggests that

affordance might play a role in“predictive control” of activity and,

while the analysis (and indeed use of the term), in this papermight differ from his, the idea that affordance refers not only to

immediate action but to future actions is central to the ideas in

this paper. Additionally, Mizelle et al. (2013) discuss the notion

of functional affordance, in which there is an optimal manner inwhich a given object can be used to achieve a desired goal. For

example, Mizelle et al. (2013) note that a hammer can be held a

variety of grasps (some involving the handle, some involving the

head, for instance) but that there is a grasp which “ . . . best affords the action of driving a nail . . .” (p. 280). This can be seen as taking

the predictive control further, in that there is a goal state against

action can be optimized. While these notions of affordance could

be represented internally (in terms of specific neural correlates of functional affordance that can be adapted to contextual demands),

the notion of complimentarity followed in this paper offers a more

parsimonious explanation. In other words, the Gibsonian notion

of affordance is taken in this paper to describe a particular form of

complimentarity in the person–environment–tool–object system,and it is the “system” as a whole which can be said to optimize the

tool-using activity.

TASKONOMYANDHOWTHE ENVIRONMENTAFFORDSSKILLED

ACTION

One way in which the environment can be created to provide

affordances for future action is in the ways in which experts lay

out their workspace. In their discussion of blacksmiths, Keller andKeller (1996) use the term “taskonomy” to refer to the ways in

which an expert’s knowledge of the tasks to be performed help

create the arrangement (taxonomy) of tools in their space. This

arrangement is not simply a matter of having particular types of

tools kept near each other, but arises through a combination of tools and actions. A similar pattern can be seen in the workspace

of the jeweler (Figure 1).

As the jeweler performs a particular task, so a tool is pickedup, used and then laid down in the workspace; as work progresses

so tools are either reused or new ones introduced. However, the

expert is often able to describe what work had been completed

in a particular workspace by looking at the collection of tools inthe immediate vicinity. In some cases, specialized tools will be

brought to the workspace with the intention of supporting a par-

ticular goal. Thus, the workspace becomes managed to provide

particular affordances (in terms of available tools and the posi-

tion in which these tools are placed to support particular types

of grasp). This suggests the anticipation of tasks and the arrange-ment of the workspace in line with these anticipations. In these

ways, the movement of tools in the workspace (as the result of

deliberately selecting these in preparation of a specific job, or asthe result of picking up and putting down the tools during the

performance of the job, or as the result of moving tools which are

no longer needed further away from the central point of reaching)

becomes part of the structuring of the workspace. Rather thansimply reflecting the ebb and flow of actions in the workspace, we

argue that this reflects the management of potential affordances

and, as such, is a form of Distributed Cognition. The suggestion














FIGURE 1 | “Taskonomy” in jewelers’ workspaces.

that moving tools around the workspace is a form of “cognition”is

logically the same as the suggestion that presentation of a problem

will “frame” the approach to the solution and that manipulating

pieces in a puzzle might be a form of thinking. In other words,layout of the workspace will frame the actions which are most

likely to be performed and this framing is the result of deliber-

ate choices made to retain, discard or move a tool after it has

been used (rather than merely a consequence of moving toolsaround).

WORKINGWITH TOOLS

In his discussion of craftwork, David Pye draws the useful dis-tinction between “certainty” and “risk” in craftwork. He argues

that in “the workmanship of certainty” there is an impetus to

design work to ensure consistency, repeatability, and minimize

variation or ambiguity. Such work involves heavily proscribed

procedures and measures of quality and could be interpreted interms of industrialized production processes. In this approach,

the artifact being produced will be tightly specified prior to pro-

duction and the resulting artifact will be considered in terms of

this specification. Anyone who has constructed flat-pack or self-assembly furniture will have encountered a situation in which the

manufacturer has sought to encourage workmanship of certainty.

However, anyone who has built self-assembly furniture will also

recognize the challenges that this poses. Misreading the instruc-tions or believing that you know what you are doing so don’t

need to read the instructions can lead to results which differ

from the goal. This could be quite minor (a handful of left over

components) or quite major (the door which doesn’t open, the

shelf which drops out when the unit is stood up). This varia-tion illustrates the workmanship of risk. This, in turn, reflects

the variability in outcome which can arise from decisions made

by the worker during the performance of the tasks. The deci-sions could reflect a choice of tool, or knowledge/skill in the

use of the tool, but they could equally reflect responses to the

opportunities presented (or constraints created) by the materials

being used. For example, the knot in a piece of wood, or the fin-ish on one side of the self-assembly wardrobe, could constraint

the actions which are possible or could suggest an appropriate

action to perform. In contrast, the “workmanship of risk” does

not involve such tight specification, i.e., “ . . . the quality of the

result is not predetermined, but depends on the judgment, dexter-

ity and care which the maker exercises as he works.” (Pye, 1968,

p. 20). Rather than the intent or purpose being predetermined, itis now something which crystalizes through the developing inter-

action between craftworker, tools, and materials being worked.

This is something which we noted in our study of jewellery mak-

ing (Baber and Saini, 1995): the jeweler worked to very sketchy “plans” but adapted these plans to suit the resulting state of the

material, often modifying a particular ring or brooch to capital-

ize on a particular facet that they noticed as the metal was being

worked.

“First, the experienced worker usually employs “smoother ” and more

consistent movements…Secondly, the experienced worker operates more

rhythmically, indicating that a higher degree of temporal organization

has been achieved. Thirdly, the experienced worker makes better use of the

sensory data . . .Fourthly, the experienced worker reacts in an integrated

way to groups of sensory signals, and makes organized grouped responses

to them” [Seymour, 1972, 35–36]

The quotation from Seymour (1972) indicates how the output

of the human–environment–tool–object system is being opti-

mized, but not necessarily how the dynamics of the system relate

changes in input to output. In order to consider this, we turnour attention to series of studies conducted by Bril and her col-

leagues, focusing on tasks involving hammering (either stone

hammers to knap flint or metal hammers to shape stone or glass

beads).

SYSTEMDYNAMICS: TRANSFORMATIONS IN TOOL USEOur actions, when using tools, involve the coordination of a set

of transformations (Biryukova and Bril, 2012). We transformkinetic energy into tool motion – but need to appreciate how

much energy to exert in order to produce the desired motion

of the tool (and in order to produce the desired effect on the

object from the tool’s motion). We manage dynamic transforma-tions, balancing the movement of the tool in the air and on the

object with our own motions and with the outcome of the tool’s

activity. We anticipate what effect the tool’s motions will pro-

duce and relate these to the outcomes that we desire. As Ingold
















(2000) points out, “Intentionality and functionality are . . . imma-

nent in the activity itself, in the gestural synergy of human being,

tool and environment.” (Ingold, 2000, p. 352.) The ability to both

anticipate the outcome of the tool’s action and manage the func-tionality of the tool are an integral part of the use of the tool. The

dynamics of using the tool thus becomes far more important than

might be implied by the neurological imagingwork which concen-

trates on the form and function of the tool. Given that these (andrelated) transformations need to be managed during the use of

tools, it is worth asking where these transformations might be rep-

resented? If they are “represented”simply during the performance

of an action, and arise from the moment-to-momentcorrection of the action, then one might not expect to see anticipatory effects.

On the other hand, if there is evidence of anticipation (of the

consequences of any of these transformations) then this implies

a need to represent the consequence and the question remains,where does this representation reside and what form does it

take?

Before considering the questions of transformations, it is worth

repeating some of the observations from these studies regarding

expertise. For example, Roux et al. (1995) showed that expertcraftsmen (making stone or glass beads) showed significantly

less inter- and intra-individual variations in performance than

less experienced workers. Similarly, Biryukova and Bril (2008)

showed that expert knappers used a larger repertoire of joint anglecombinations than their less qualified colleagues (who tended to

demonstrate more rigid behavior), and Bril et al. (2010) showed

that experts showed a lower variability in kinetic energy com-

pared to intermediates and novices. In related work, Vernooijet al. (2012) explored learning in a task involving the use of a

300-g hammer-stone. Analysis of motion tracked during the per-

formance of this task showed inter-individual differences in the

ways in which joint angles were combined to strike a particu-

lar type of blow and that these combinations changed duringthe course of the study. The analysis of learning to use such

a hammer suggested that participants were only able to modify

one parameter (relating to joint angles or impact force) but notboth at the same time, until they had gained proficiency in the

task.

In a series of experiments comparing expert, intermediate and

novice users of stone hammers (in flint-knapping tasks conducted

in the laboratory), Bril et al. (2010) identify three primary param-eters that seem to contribute to the dynamics of tool use in this

context. The first are Control Parameters, such as the velocity

with which the hammer stone approached the target. The study

showed that, in general, novices appreciated the need to control

velocity but were not able to control this efficiently (this finding issupported by the work on Vernooij et al., 2012, discussed above).

Thus, we would expect greater variability in the novice perfor-

mance on these control parameters; as Seymour (1972) put it, theexpert actions would be performed in a “smoother,”“more consis-

tent,”“more rhythmical[ly]”manner. The second set of parameters

considered by Bril et al. (2010) are regulatory parameters, such as

the trajectory followed by the hammer stone and the potentialenergy applied. Experts tended to show shorter trajectories and

smaller ratios between parameters. In Seymour’s (1972) terms,

this shows howexperts are ableto usea “higher degree of temporal

organization” and also to make “better use of the sensory data” in

managing their actions. As Bril et al. (2010) note, “In the present

task, the velocity of the hammer had to be controlled to produce the required kinetic energy in relation to the mass of the hammer. This

was achieved by concurrently changing the trajectory, the amplitude

of the movement, and the muscular force. In this perspective, the

movement became meaningful only in relation to the production of

functional parameters at the level of the task, which allowed for move-ment flexibility as long as the task requirements were fulfilled.” (Bril

et al., 2010, p. 837). This quotation introduces the third parame-

ter, the Functional parameter, such as kinetic energy, which experts

appear to hold constant and aim to apply thelowest kinetic energy that is sufficient for the task. As the experiments involved present-

ing participants with hammers of different weights and requiring

them to produce flakes of different sizes, one can assume that all

participants would be ableto discern changes in hammer weight ortask demands (in terms of flake size), but the results suggest that a

characteristic of expertise (which was not available to the novices)

was the ability to respond to “nested relationships” (Wagman and

Carello, 2003) between weight of hammer and size of flake to pro-

duce. The ability to appreciate these“nested relationships”allowedthe experts to interpret the constraints placed on them by the

person–environment–tool–object relationships and respond to

these in ways that the novices could not. So, we return to the ques-tion of where these constraints might be represented? One possi-

bility (implied by Seymour, 1972 and mooted by Bril etal., 2010)

is that the initial representations involve Functional parameters

which are learned and then adapted to changes in context.In his discussion of dexterity, Bernstein (1967) highlighted that

the main determinant was not bodily movement so much as the

capability to respond to changes in the conditions surrounding

the person. Bernstein’s (1967) notions of tool use, in terms of

dexterity, relate to the quotation from Simon (1969) at the start

of this paper. The manipulation of tools is rarely an end in itself but is performed with the intention of shaping objects in the envi-

ronment. The actions performed lead to changes in the objects

but also indicate the intentionality of the tool user (providing they have sufficiently dexterity in their use of the tools). The expert tool

user thinks through the tools that are used because the actionsper-

formed with the tools shape the environment in such a way as to

solve the problems that it presents and in such a way as to producethe results that the tool user desires. The action performed with

the tool also creates the opportunities for the next action; and this,

in turn, reflects the type of grip and posture which the tool user

adopts. In this way, grip and posture (in holding and using a tool)indicate the chosen solution to the problem that the tool user is

solving.In much the same way that Rosenbaum et al. (2012) speaks

of end-state comfort (and the ways in which a posture antici-pates a particular end-state following the movement), so we can

think of the ways in which the tool user is continually seek-

ing to adapt their current motions in anticipation of subsequent

motions and states of the object. For this paper, we take this tomean that the skilled tool-user is better able to coordinate the

person–environment–tool–object system and to anticipate how

changes in this system require adaptation of activity. This real-

time adaptation need not imply internal representation of either














FIGURE 2 | (A) An instrumented handle and (B) using a file in an instrumented handle to remove paint from a piece of wood.

FIGURE 3 | Example of data collected from experienced silversmithusing a file. The data were sampled at 120 Hz. Velocity is derived from

accelerometer data, de-trended using a moving average of 100 samples

and grip force is the average output from the Analog to Digital

Converter (ADC). The Y velocity line describes anteposterial motion, the

Z velocity line describes vertical displacement, and the top grip

describes the force applied to the top of the handle (pressing down on

to the metal).

task dynamics or some form of “motor program.” Rather, theexpert is able to produce movements which are coordinated to

task goals (being more efficient and economical in terms of energy

use). In a sense, expertise is the practiced adaptation of intrinsic

dynamics to task dynamics (where task dynamics are defined by the person–environment–tool–object system) so that changes in

task constraintsand affordancescan be appropriately responded to

through subtle tuning of actions. This implies that experts are able

to modify the pattern of activity without necessarily impairing the

functional impact of the activity. We do not believe that experts

need to possess, or even represent, these various patterns of activ-

ity but rather these arise on-the-fly during the coordinated control

of limbs holding and controlling tools.

STUDIES USING SENSORS FITTED TO THE HANDLES OF

TOOLS

In order to explore these questions of dynamics, we have been

exploring ways in which to capture behavior in the field (or, at
















FIGURE 4 | Spectrograms from three participants performing one of

the “paint removal” tasks. The spectrogram of frequencies 0–20 Hz over

time for y -movement (i.e., back and forth motion of the file across the

wood), moving between the 1st and 3rd dots on the file with the goal of

removing two layers of paint.

least, in laboratory and workshop settings which are as close to

the field as possible). This has involved designing and developing

handles which combine different types of sensor to capture theactions a person performs. Often such data are collected from

using camera systems with markers on the person. While thesecan be very accurate, they are not easy to use in the field. Thus,

it makes more sense to instrument the person or their tools in

order to collect data in situ. We have taken the lead from Bril and

her colleagues (discussed in the previous section) to instrumentour tools (Figure 2). In our work, strain gages are used to capture

force applied to the handle and a three-axis accelerometer is used

to capture motion (Parekh and Baber, 2010 for a description of

the design of these handles).

In order to appreciate how experience in using a given tool can

shape activity, Figure 3 presents an extract of recordings (froma three-axis accelerometer and strain gages integrated into the

handle of a jeweler’s file) of an experienced silversmith filing the

edge of a metal strip. Figure 3 shows three filing strokes over

the course of 2.5 s. Each stroke (occurring at approximately 11.6,12, and 12.9 s) is indicated by an increase in the y -velocity data.

There are two types of stroke here: rapid (at 11.6 and 12.9 s) inwhich the file in moved rapidly across the metal, and slow (12 s)

in which the file is drawn more slowly over the metal. Duringeach stroke, there is downward pressure on the file (indicated by

the decrease in z -velocity data and increase in “top grip” force

applied to the top of the handle). Immediately following the

stroke, the file is lifted up (increase in z -velocity) and broughtback to the starting point. Prior to the next stroke the file is

adjusted and aligned with the metal (which takes around 1 s),

which involves little change in grip force applied and z-velocity.

The top grip loosens as the file position is reset for lifting the

file off the object (movement in the z direction); the expertuser only applies force on the forwards motion. This action is

partly dictated by the file being used and partly by the resultsthat the tool user intends. As the expert said, you can remove

metal easily enough but you can’t put it back. So filing is aboutremoving sufficient (but not too much) of the metal. Further-

more, the metal being worked (copper in this instance) could

easily be dulled if too much of the upper surface was removed,

and so filing was also a matter of retaining the luster of the metal.Such knowledge can affect the way in which the tool is wielded

and influence the outcomes that one might expect when using the

tools.

In another study, we asked novice users of a file to removepaintfrom a piece of wood. Figure 2B shows the task being performed.

There are three dots painted on the top of the file and participant

was instructed to ensure that thefile was kept betweenthe first andsecond, or the first and third dots.

Contrasting three people performing the filing task (Figure 4),

we can see that while the main activity (yellow on the spectro-

graphs) occurs at similar frequencies, the harmonics vary. These

variations might reflect differences in strategy. We would expect

to see harmonics from these data due to the periodicity of therepetitive motions employed. This also suggests that differences

in performance can be captured through a better appreciation

of dynamics and, potentially, following the lead of Bernstein

(1967), can be reflected in the conservation of energy of the toolusers.

The raw accelerometer data were integrated to produce veloc-

ity, on which we applied a Fourier transform to determine thefundamental frequency of the filing motion. Table 1 suggests thatthe main determinant of this fundamental frequency is not the

tool-specific goal to keep the two dots inside the wood, but the

task-specific goal to remove one or two layers of paint.

CULTURAL AFFORDANCES

In this section, we turn our attention to the broader question of

cultural effectsin tool use. For thesake of thediscussion,we restrict

ourselves to the simple assumption that cultural constraints can














Table 1 | Comparing fundamental frequency of filing for task and tool-directed goals.

Task goal Filing white paint down to show red paint Filing white paint down to show bare wood

Tool-directed goal Keep file between dot a

and dot b

Keep file between dot a

and dot c


and dot b


and dot c

F0 6.201 Hz 5.518 Hz 3.174 Hz 3.467 Hz

have a bearing of the experiences that people might have with spe-

cific types of tools and, in particular, can serve to define acceptable

or proper ways in which particular artifacts are used. Thus, one

question that can be used to address the issue of “culture” in tooluse is to ask how should one properly use cutlery, such as a spoon,

knife or fork?

In their study of eating (kale or water) with a spoon, van der

Kamp and Steenbergen (1999) used video-based motion trackingto record arm motion. The likelihood of spilling the contents of

the spoon (kale or water) when it was moved from bowl to mouth

increased the number of corrective sub-movements made during

the action which affected the kinematic profile of the movement.The contents of the spoon also affected head motion. Participantswere more likely to move their head towards the spoon when it

contained water which, in turn, shows howthe coordination of the

motionsystem (i.e.,contents–spoon–hand–arm–head) changes in

response to task demands. Interestingly, the study also hinted atvariation in“eating styles”which reflected individual differences in

performance. We are interested in how these “eating styles”might

also reflect cultural responses to cutlery and how culture defines

the“proper”way to use an item of cutlery. Of course, the use of theword “properly” is deliberately provocative and culturally loaded.

At one level, “proper” use could simply mean that food is moved

from plate to mouth in a controlled manner, in sufficientquantities

to make it easy to eat. At another level, “proper” use could relateto various social mores and rules of etiquette in terms of how the

knife and fork are held and moved, and how much food is held on

the fork or put into the mouth. For example, in her discussion of

using forks, Visser (1991) contrasts the “English” style (of eatingfrom the back of the fork tines and holding the knife in the other

hand) with the “American” style (of eating from the bowl of the

fork and swapping, or “zig-zagging” fork and knife).

We asked participants, using a knife and a fork (fitted to our

instrumented handles), to perform a somewhat unusual versionof “eating.” The task goal required participants to lift a forkful of

sweet-corn to their chin. This breaks down into: “load fork’, “lift

fork’, and “terminate” (e.g., most participants simply tipped the

fork to drop the sweet-corn back onto the plate). The “English”or “American” styles outlined above are illustrated by Figure 5.

In order to consider variation, we selected one participant who

was familiar with the “English” style (Figure 6) and one of the

participants who had never used cutlery in this manner (Figure 7).Figures 6 and 7 show that variability in the data from the inex-

perienced user are consistently higher than the experienced user

for both grip and accelerometer data. This echoes the earlier find-

ings relating the variability in “skilled” performance. Rather thanthe “skill’, in this case, being the result of instruction, training

and practice (as one might expect in the use of hand-tools), these

results hint that enculturation and exposure to particular beliefsabout appropriate use of cutlery can have an impact on the ease

with which these artifacts are manipulated in different ways.

DISCUSSION

We use tools to solve the problems that objects in the environ-

ment present to us. This is an obvious statement but hides a

couple of points which are worth noting. The first is that inten-

tion which underlies the use of the tool combines a task goal with

the affordances of the tool–object interface, and the constraintsof the person–environment–tool–object system. This means that

“cognition” becomes the active response to the affordances of theinteraction between tool and object in terms of the task goal that

the user is seeking to achieve. Taking Gibson’s notion of com-plimentarity, we can say that the dynamic aspect of this activity

continually shapes the actions of the person as much as it shapes

the state of the object. In other words,the statesof the object,envi-

ronment, tool, and person become combined to form the focus of action and, by implication, to help frame and reframe the task

goal. One might expect the task goal to be kept constant during

the performance of the task. However, our discussions with, and

observations of, expert jewelers suggests that this not entirely thecase. While the high-level objective might remain the same (e.g.,

produce a ring of a particular size set with a particular stone), the

development of the “plan” to achieve this goal adapts to the stateof the metal and the performance of the task. Thus, the task goalwould appear to follow the notion of “situated action” (Suchman,

1987) which changes with context. This raises the second point,

that, the focus of action is context-dependent and the context

is continually changing. So, tool use is enactive, embedded and

embodied.The comparisons of experienced and inexperienced users of

tools (and cutlery) considered in this paper show that expertise

not only involves less variability in physical performance but also

better control of energy expended in the performance of a giventask witha given tool. We believe that this pointsto the well-known

assertion that the expert develops a “feel” for the tool, and often

prefer to use their own tools for particular tasks because these havebecome very familiar to them. Indeed, a potential problem thatwe face with the instrumented handles that we use is that these

feel different from those that the experienced tool users prefer.

Anecdotally, only the experienced tool users commented on the

feel (weight, balance, material) of these handles during the datacollection.

The skilled craft-worker will often speak of the tool becoming

part of the body, and the feeling of manipulating the tool being

akin to simply moving the hand in which the tool held. For somewriters, this implies that the tool can be considered as a physical

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FIGURE 5 | Comparing English (left) and American (right) cutlery use.

FIGURE 6 | Consistent “English” use (over six separate attempts as

indicated by thick lines between each attempt). The pattern of

grip force applied (particularly to the fork handle) and the

smoothness of the fork’s accelerometer trace show how the

experienced participant’s repetitions are consistent and reflect a

well-practiced motion.

www.frontiersin.org February 2014 | Volume 5 | Article 116 | 11













FIGURE 7 | Variable “English” use (over six separate attempts). The inexperienced participant shows large variation in grip force and

accelerometer trace for the fork. During the task, his preferred approach was

to tilt the fork on its side and move it towards the kernels, using the knife as a

stop. He then held the fork at an angle and used the knife to keep the kernals

pressed to the tines as he lifted both knife and fork. There is less correlationshown between grip and activity from the knife, which is being pushed on to

the top of the fork, and, particularly towards the right of the graphs, the fork is

held with force primarily only on two sides of the handle as opposed to a full

grip.

extensionof theperson andthat, therefore,motorcontrol becomes

a matter of adaptingto the added potential of the“extended-limb.”However, rather than simplybeing a matterof planning movement

with the addition of the tool, it is plausible to suggest that the

tool changes the perception of space around the tool user (Mar-

avita et al., 2002). “People who use tools . . .build an increasingly rich implicit understanding of the world in which they use the tools . . .”

[Cutler, 1994, p. 80]. In her discussion of representations in tool-use, Massen (2013) emphasizes the need to appreciate how tools

become part of the peripersonal space of the user, such that “there is no need to distinguish between external goal locations to which the

tool has to be moved and the locations to which the bodily effector

has to be moved.” (p. 2). While this makes sense when considering

movements with tools, it overlooks an equally important aspect

of the skilled craft-worker. The reason that the tool feels as if it ispart of the person is because it “disappears” from attention which

becomes more and more focused on the object being worked on.

This suggests that, rather than the tool being an extension of the

body, it makes more sense that the tool creates a focus of atten-tion – with the sense that the tool’s movement becomes so central

to attention that the control of the limb operating it becomesless important. This suggests that, rather than considering the

tool-hand combination, it is more important to consider the tool-object combination because this is where the skilled practitioner is

attending.

The use of tools, by experts, seems to involve anticipatory, feed-

forward control of movement (as well as rapid and efficient use of feed-back through all of their senses) in which subtle adjustments

in the manipulation of the tool are performed in order to effect

desired changes in the object being worked on. Not only does

this explain the minimal variability but also highlights the central

question of this paper; if so much of the activity of the expert tooluser is anticipatory, how are these anticipations represented? We

propose that it is not sufficient to only look in the brain of the

expert tool user to discover these representations. Even if there are

regions which are active under specific conditions, the skill of the

expert tool user comes from the ability to control their activity with sufficient spare capacity to cope with future demands and to

respond to the changing context in which they are using the toolsto effect changes in the object being worked on. The idea that the

environment (and the objects it contains) can be interpreted indifferent ways, suggests that these become “external representa-

tions” to which the person responds. Response is partly a matter

of knowledge, skill and ability of the person, partly a matter of

fit between action and environment and partly a matter of thenature of the environment and the objects it contains. As the per-

son focuses on specific aspects, which are relevant to the task (of

shaping a piece of metal orarranging tools ina workspace) sothese

aspects become the cognitive space in which subsequent decisionsare made. Tool use, as a form of problem solving, becomes a

matter of making these decisions as the cognitive space changes;

and a means of acting upon the cognitive space to create new opportunities. This further suggests that much of the activity which is assumed to be “feed-forward” (in the sense that there

needs to be a model which guides behavior) could be explained by

fast-acting, negative feedback loops (integrated across several sen-

sory modalities) which support moment-by-moment correction

through solving the inverse kinematics problems of positioning agiven tool in a given position in order to effect change in the object

being worked on.

We believe that much of the “representation” drawn upon in

the use of tools can be in the form of external representations (the
















objects and tools in a given environment, particularly in support

of the situated action of ongoing planning in tool use) and in the

form of coordinative structures (the control and management of

physical activity, particularly in terms of feed-forward control of movement and use of feed-back from the results of the move-

ment). In other words, following the lead of Riccio (1993) and

the more detailed arguments of Chemero (2009) an internal rep-

resentation is not necessary for the control, coordination and (wepropose) planning of tool use because it is sufficient for the tool

user to have the ability to perceive the state of the object on which

she works and to manipulate the tool in order to produce a partic-

ular pattern of perceptions (and, in this case, we suggest that thesepatterns are equally as likely to be olfactory, haptic, and auditory

as visual). This ability becomes manifest only during the per-

formance of a person–environment–tool–object system (echoing

Butler’s claim that “strictly speaking, nothing is a tool except dur-inguse”)and this systemcan be described using System Dynamics,

in which the systems goal is the optimization of specific movement

parameters in order to produce an effect on a given object. This

reduces the need for there to be internal representations per se (see

also Barrett, 2011). Furthermore,any“representation”that the tooluser employs is likely to spread across the entire nervous system

rather than solely in regions of the brain. From this, the strong

and compelling evidence accumulated from the activation of spe-

cific regions in the brain is taken to indicate the result rather thanthe cause of tool using behavior (whether observed, imagined,

or performed) which arises from the recruitment and activation

of coordinative structures (Bernstein, 1967) through task-specific

devices (Beek and Bingham, 1991). While our paper has notsought to present evidence in support of this claim, we believe

that this statement helps to bring together the ongoing work that

we have reviewed and raises the opportunity to develop testable

hypotheses for future exploration of the ways in which people

use tools.

ACKNOWLEDGMENTS

An early draft of this paper was much improved through com-

ments from Anthony Chemero and two anonymous reviewers.

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Conflict of InterestStatement: Theauthorsdeclarethat theresearch was conductedin the absence of any commercial or financial relationships that could be construed

as a potential conflict of interest.

Received: 28 November 2013; accepted: 27 January 2014; published online: 24 February

2014.

Citation: Baber C, Parekh M and Cengiz TG (2014) Tool use as distributed cog-

nition: how tools help, hinder and define manual skill. Front. Psychol. 5:116. doi:

10.3389/fpsyg.2014.00116

This article was submitted to Cognition,a section of the journal Frontiersin Psychology.

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The Hands That Guide the Thinking: Interactivity in Mental Arithmetic

Lisa G. Guthrie ([email protected])Department of Psychology, Kingston University,


Julia K. Mayer ([email protected])Faculty of Psychology and Neuroscience, Maastricht University

6200 MD Maastricht, THE NETHERLANDS

Frédéric Vallée-Tourangeau ([email protected])Department of Psychology, Kingston University,


Abstract

Whether it is in mining distal cultural influences or using more proximal artefacts, problem solving in the wild routinely

scaffolds on the basis of interacting with resources outside the

head. Individuals often gesture, point or use objects as an aid to

solving quotidian arithmetic problems. Interactivity has been

linked to better performance in problem solving, possibly due to

a more efficient allocation of attentional resources and better

distribution of cognitive load. Previous research suggests an

interplay between the cognitive and motor system whereby the

later can lighten the strain on working memory capacity (Goldin-

Meadow, Nusbaum, Kelly & Wagner, 2001; Carlson,

Avraamedes, Cary & Strasberg, 2007; Vallée-Tourangeau,

2013). In attempting to simulate these moves made in the world,

different levels of interactivity were examined with a series of

mental arithmetic problems. Participants were also profiled interms of attitude to varying problem presentations as an

assessment of their engagement in the task. The integration of

artefacts, such as tokens or a pen, provided individuals with the

possibility to explore the opportunities afforded by a dynamic

modification of the problem. Mental arithmetic performance was

more accurate and more efficient under these conditions.

Participants also felt more positive about and better engaged

with the task when they could reconfigure the problem

presentation through interactivity. These findings underscore the

importance of engineering task environments that support

distributed problem representation and adequate levels of

interactivity that creates a dynamically shifting topography of

action affordances.

Keywords: Interactivity; Mental arithmetic; Problemsolving; Distributed Representation; Task engagement.

Introduction

Mathematical problems are embedded in everyday life in

a variety of different shapes and forms. When confronted

with an arithmetic task, people often rearrange the

physical display by interacting with the environment.

They might move coins while counting their money, note

subtotals with a pen or use their hands to gesture, point or

count (Kirsh, 1995; Neth & Payne, 2001).

Mental arithmetic tasks often entail strategic thinking

and deliberate information processing, which require time

and effort (Vallée-Tourangeau, 2013). Besides basic,well-rehearsed sums, computations are generally said to

pose a relatively high cognitive load on an individual’s

internal resources, such as working memory (Ashcraft,

1995; DeStefano & Lefevre, 2004). Numbers are held,

added and manipulated in order to solve the problem

employing different working memory subsystems,

including storage, retrieval and allocation of attentional

resources. Dependent on the complexity and length of a

mental arithmetic task, the demands of finding a solution

may impose a relatively low or high cognitive load,

potentially imposing substantial demands on working

memory capacity. This capacity may, however, be

stretched or reduced by certain internal or external factors,

which can subsequently paint a misleading profile of an

individual’s true arithmetic capabilities (Ashcraft &

Moore, 2009).

Interactivity

The internal cognitive and physical resources deployed to

tackle a problem may be taxed by various features of the

task—such as time pressure, level of difficulty, fatigue.

Reasoners naturally recruit artefacts and use the physical

space to make thinking easier and more efficient.

Increased levels of interactivity have been linked to better

performance, possibly due to a stronger focus of attention

and better distribution of cognitive load (Goldin-Meadow,

Nusbaum, Kelly & Wagner, 2001; Carlson, Avraamides,

Cary & Strasberg, 2007; Vallée-Tourangeau, Sirota &

Villejoubert, 2013; Weller, Villejoubert, & Vallée-Tourangeau 2011). Previous research implicates an

interplay between the cognitive and motor system

whereby the later can lighten the strain on working

memory capacity reducing the expense of resources to the

task (Goldin-Meadow, Alibali, & Church, 1993).

Improved effectiveness, indicated by increased accuracy

and speed, has also been related to movement execution,

such as nodding and pointing (Goldin-Meadow et al.,

2001), as well as manipulations of the problem’s spatial

arrangement (Vallée-Tourangeau, 2013). So it seems that

the shaping and re-shaping of the problem presentation

can help surpass the original limitations of working

memory capacity by lowering the expense of internalresources necessary to solve the task and guide attention



Figure 1: The board on the top left is an example of a standard template used for all four conditions. The board

on the top right shows the participant undertaking the pen-paper condition. The board on the bottom left is an

example of the wooden tokens in preparation for the participant. On the bottom right the same board after the

participant has completed the task. Note the congenial groups of the numbers.

(Weller et al., 2011). This could subsequently increase

efficiency. Thus, interacting with the environment and

utilizing artefacts can increase efficiency by distributing

the storage and computational demands of the task across

resources internal and external to the reasoner. Suchdistributed cognitive processes shift the cognitive load

from the reasoner onto a system in which she is embedded

(Vallée-Tourangeau, 2013).

Mathematical tasks are frequently assessed in terms of

accuracy and efficiency. Accuracy measures the precision

of the calculated solution in relation to the correct answer.

Efficiency, on the other hand, involves a relation between

invested effort and resulting performance (Vallée-

Tourangeau, 2013). Yet, it is not only the problem itself or

its complexity that impacts how accurately or efficiently

an individual performs in a mathematical task. The

presentation of a problem can guide behaviours and

strategic choices in the path to a solution (Vallée-Tourangeau, Euden, & Hearn, 2011). Embedded in this

problem presentation are the varying possibilities for

interaction, the dynamic loop of information and action

flowing between a person and the outside world, the

nature of these interactions having the potential to direct

strategic choices (Neth & Payne, 2001; Kirsh 2013). Kirsh

(1995) describes an organizing activity that recruits

external elements such as the hands, coins and pen and

paper to reduce cognitive load as a complimentary

strategy to the internal processes of cognition. In turn this

coupling of the internal mind with that which is external

to the skull generates a distributed system of thinking.

Attitude Toward the Task

Student engagement in performing academic tasks may be

suggestion that the activity by which learning is

experienced may provide a stimulus for this engagement

(Shernoff, Csikszentmihalyi, Schneider, & Shernoff,

2003). It is also possible that a task that offers a student a

sense of connection to the real world is more likely tomaximize student engagement (Newmann, Wehlage, &

Lamborn, 1992). Furthermore Schiefele and

Csikszentmihaly (1995) discuss the importance of the

affective experience on performance, while engaging in

mathematics in the classroom. Positive emotions elicited

by the task experience may contribute to increased

problem-solving capacities (Shernoff et al., 2003).

The Current Experiment

This experiment explored the role of interactivity in adult

participants using tangible artefacts with which the

participants can modify the problem presentation as they

attempt to complete the arithmetic task. Thus the external problem presentation tracks the dynamic interface

between the agent’s internal representation and the world.

Previous research on the role of interactivity in

mathematical reasoning and learning has generally

presented material either on paper or a computer display.

Interactivity and the potential to re-shape the problem

presentation was manipulated in terms of four conditions.

In the first, participants added a sequence of single-digit

numbers with their hands down and in a second they were

allowed to point at the numbers. Thus in these two

conditions, the problem presentation can not be modified,

but participants can engage in some complementary

actions (Kirsh, 1995) in the latter. In the other conditions participants could re-shape the problem presentation: in

the third, they were given a pencil and could recast the



sums were presented as a randomly arrayed set of wooden

tokens that participants were invited to move to arrive at

the correct sum. Across these four different levels of

interactivity, performance was measured in terms of

accuracy and efficiency. Not only did we expect accuracy

to be influenced by interactivity, but efficiency should be

related to the degree to which participants can modify the problem presentation as they compute the totals. By

efficiency we mean, the degree of accuracy relative to the

resources invested in completing the sum. We

operationalized resources as the time taken to do the sum.

We expected that interactivity conditions that made it

possible for participants to manipulate the problem

presentation in a manner that reflected and complemented

their internal processing to yield the highest level of

accuracy and efficiency.

Method

ParticipantsSixty participants (40 females, mean age 23.32, SD =

4.41) were recruited for this experiment.

Materials and Measures

Arithmetic Task . All participants were presented with

five sets of numbers in four conditions and asked to

calculate the sum of the numbers. Therefore each

participant calculated 20 sums over the experimental

session. They were requested to calculate each set as

quickly and accurately as possible. Each set consisted of

11 single-digit numbers. For the purpose of the present

study, single-digit numbers between one and nine werefirst categorized as low (1-4) or high (5-9) in order to

generate the range of possible sums in a more principled

manner. Four groups of sums were created: Group I (5

low, 6 high), Group II (only high), Group III (3 low, 8

high) and Group IV (4 low, 7 high). Each of these groups

was assigned to one of the four interactivity conditions,

and this assignment was counterbalanced across

participants.

Sums were presented in the form of templates which

consist of 11 circles (2.2cm) covering between half and !

of a side of A4, that was delineated by a varnished cutting

board on which participants carried out all the additions

(see Fig 1). By altering the order of the templates withineach group, the visual presentation of the sums was

additionally randomized for each set. For the token

condition, templates of tracing paper were created with

the same configurations of the constituent numbers as the

paper version of the other conditions. Wooden tokens

were placed in the corresponding position and the tracing

paper was removed before the start of the task. The

numbers were not revealed to the participant until all

tokens were in place. Maths performance was measured in

terms of accuracy (the correct answer), absolute error (the

absolute deviation from the correct answer) latency (time

taken until answer verbalised) and efficiency. Participants

were not given feedback concerning the accuracy of theiranswers.

Efficiency was calculated as a ratio of the proportion of

proportion of time invested in solving that set (out of the

longest time the slowest participants invested in solving

that set). For each of the four conditions, participants were

first ranked according to their averaged latencies. The

average of the slowest 25% served as a reference point

and represented the maximum effort one could expend in

that condition. Thus the efficiency ratio denominator wasa given participant’s latency over the average latency for

the slowest quartile; the numerator was that participant’s

proportion correct solutions in that condition. For

example, a participant in a given condition may have

solved three out of the five sums, for a proportion .6

correct. In turn, the participant’s average latency for

completing the five sums in that condition might have

been 30 seconds. If the average latency for the slowest

quartile was 40 seconds, then that participants invested

75% (30/40) of the total possible time for completing the

sums in that condition. The efficiency ratio for that

participant would then be .6/.75, or .8.

Level of Interactivity. Interactivity was manipulated in

terms of four experimental conditions; namely (i) static,

(ii) pointing, (iii) pen-paper and (iv) token. In the static

condition, participants were asked to compute the sum

mentally with their hands flat on the table. In the pointing

condition, there were no restrictions on movement, other

than to exclude the use of the pen to make notes. Hence,

participants were allowed to use their fingers to point to

the numbers that composed the sum. In the pen and paper

condition, participants were given a pen and were allowed

to write on the sheet provided by the experimenter

containing the number set. Finally, in the token condition,

the sums were presented in the form of round numberedwooden tokens (2cm in diameter), which could be moved

by the participants. The format of the presentation was

visually constant and the material was always presented

on the same surface.

Attitude Toward Task Assessment (ATTA). Shernoff et

al. (2003) used the Effective Sample Method (ESM) to

measure a number of factors including affective

experiences. This affective experiences component of the

ESM questionnaire was used as the basis for a scale,

Attitude Toward Task Assessment (ATTA) designed to

assess the engagement of participants in the tasks

undertaken in this study.

A scale composed of eight items was created to assessan individual’s attitude towards completing the sums in

each experimental condition. The eight items asked

participants to rate how easy, pleasurable, fun,

threatening, stressful, tiresome or effortful the task was

and how motivated they were to perform well in the task.

Each item was scored on an 8-point Likert scale, labeled

from zero (definitely not) to seven (definitely yes). Total

scores could range from zero to 56 - the higher the score

the more positive the attitude toward the task. Each

participant completed the same ATTA scale four times

once following each of the four conditions. The alpha

reliability of the eight-item scale for each experimental

condition indicated that the scale had good reliability

(Static, Cronbach’s " = .80; Pen-paper, Cronbach’s " =

77; Pointing Cronbach’s " = 78; Tokens Cronbach’s "



Figure 2: Mean percentage correct sums (top right panel), absolute calculation error (top left panel), latency to solution

(bottom left panel) and mean calculation efficiency (bottom right panel) in the four experimental conditions. Error bars

are standard errors of the mean.

Results

Accuracy The mean number of correct answers, as shown in the top

left panel of Figure 2, was greatest in the token ( M = .69,

SD = .22) and the pen-paper (M = .69, SD = .23)

conditions. The pointing condition ( M = .66, SD = .26)

indicated slightly less accurate calculations, with the static

condition resulting in the weakest performance ( M = .60,

SD = .30). A one-factor repeated measures analysis of

variance (ANOVA) indicated a significant difference

between the conditions, F (3,177) = 3.12, p = .027, !2 =

.050. Post-hoc tests revealed a significant difference

between the static and the pen-paper conditions ( p = .006)

and the static and token conditions ( p = .020).

Absolute Error

Deviation from the correct answer was greatest in the

static condition ( M = 2.64, SD = 2.39); the pointing ( M =

1.90, SD = 2.43) and pen-paper ( M = 1.61, SD = 1.65)

conditions produced lower deviations than the static

condition while the lowest deviations from the correct

answer were observed in the token condition ( M = 1.41,

SD = 1.69; see top left panel of Fig. 2). The one-factor

repeated measures ANOVA revealed a significant

difference between the tasks, F (3,177) = 6.34, p < .001, !2

= .097, with post-hoc tests indicating a significant

difference between the pen-paper and the token conditionswhen compared to the static condition ( p = .005, p < .001

respectively).

Latency

The latency data are shown in the bottom left panel of

Figure 2. Participants generally took about the sameamount of time to complete the task across the four

conditions (static M = 26.79, SD = 9.88; pen-paper M =

27.26, SD = 9.73; pointing M = 25.70, SD = 10.09; token

M = 26.58, SD = 10.41). The main effect of interactivity

in the one-way repeated measures ANOVA was not

significant, F < 1.

Efficiency

As illustrated in the bottom right panel of Figure 2,

performance was most efficient in the token ( M = 1.20, SD

= .62) and the pen-paper conditions ( M = 1.15, SD = .60)

with the static ( M = 1.05, SD = .71) and the pointing ( M =

1.12, SD = .59) conditions being least efficient. However,these differences failed to reach significance, F (3,177) =

1.39, p = .247.

Attitude Toward the Task

The attitude of participants was more positive toward the

pen-paper ( M = 37.98, SD = 8.38) and the token ( M =

37.78, SD = 8.94) conditions, than the pointing condition

( M = 34.12, SD = 8.76) and least favourable for the static

condition ( M = 31.63, SD = 9.13). An overall main effect

was found for attitude toward the task, F (3,117) = 17.07,

p < .001, !2 = .231. Post-hoc tests further identified highly

significant differences between the static and the tokenconditions and the static and pen-paper conditions ( p <

.001 for both conditions). The static and pointing

diti l i ifi tl diff t 025

50%

55%

60%

65%

70%

75%

80%

Static P&P Pointing Tokens

M e a n P e r c e n t C o r r e c t

1.0

1.5

2.0

2.5

3.0

3.5


M e a n A

b s o l u t e E r r o r

20

22

24

26

28

30


M e a n L a t e n c y ( s )

1.0

1.1

1.2

1.3

1.4


M e a n E f f i e i c n c y

Accuracy Deviation

Latency Efficiency



Feelings toward the pointing condition differed

significantly from those in the pen-paper ( p < .001) and

token conditions ( p = .008). Therefore, these results

indicated that participants preferred to use artefacts either

pen and paper or tokens in calculating the solutions.

DiscussionThe present experiment was designed to explore the

effects of different levels of interactivity on arithmetic

performances for single-digit additions. The degree of

engagement with and attitude towards completing the task

as a function of the level and nature of interactivity was

also investigated. The results indicated that the use of

artefacts enhanced performance in simple arithmetic

problems, supporting the hypothesis that interactivity

benefits performance. When participants were given the

opportunity to use artefacts such as tokens, accuracy

improved and deviation from the correct answer

decreased. The increase in interactivity generally required

no more time to announce an answer, confirming similarfindings in previous research (Neth & Payne, 2011;

Vallée-Tourangeau, 2013). Since accuracy increased with

interactivity but latencies remained unchanged,

interactivity therefore promoted more efficient problem

solving. Conversely, when participants were asked to rely

primarily on internal cognitive resources, as in the static

condition, accuracy was impaired. In the higher

interactivity conditions participants were given the

opportunity to recruit external resources to aid in

calculating the answer. The opportunity to engage with

the environment enabled the distribution of cognitive

load, augmented working memory resources and

delegated the control of attention in part to the dynamic

environment that cued the next action. In addition, the

possibility of modifying the physical presentation of the

problem, enabled participants to reconfigure the problem.

This improved the cognitive congeniality of the problem

(Kirsh, 1995), but also provided a more dynamic set of

action affordances that supported more efficient problem

solving. Thus the beneficial effect of interactivity on

reasoning is not simply a function of off-loading working

memory, but also reflects better executive function skills

that are cued and prompted by the shifting affordances

offered by a dynamic problem environment.

The data on the participants’ attitude towards

completing the sums in the different conditions paralleled

the impact of interactivity on performance. Conditions

involving external resources, pens or tokens, seemed to

elicit a more positive, engaged attitude towards the simple

arithmetic problems, than the restricted, static condition.

Of course, participants were also more accurate in the

interactive conditions. But the more positive attitudes

towards the problems cannot be attributed to task success

as such since participants were never given feedback

about their performance, that is, after announcing each

sum, the experimenter did not tell the participants whether

their answer was right or wrong. Those findings are in line

with the suggestion that higher levels of personal

involvement positively affect performance (Shernoff et

al., 2003). Also, changing the visual display may ease the

t k d th b li ht th iti l d hi h

increases effectiveness and alters attitudes (Vallée-

Tourangeau et al., 2013).

In calculating simple arithmetic sums, an individual

presented with the opportunity to use a complimentary

strategy, such as manipulating tokens, is embedded in a

distributed cognitive environment. Studying systems

rather than individuals poses theoretical andmethodological challenges. Theoretically, the nature of

the problem representation and the trajectory of the

solution as it evolves from an embryonic to a fully formed

answer, should perhaps be understood as being distributed

and configured in terms of a transaction between the

participants’ internal resources and the shape and nature

of the resources in the external environment. What a

participant is ‘thinking’ is not independent of the state of

the environment, and as the environment is shaped by the

participants, understanding that environment is not

independent from the participant. The methodological

implications of this transactional perspective are

important. Of course, systems can be more complex, andcomposed of a much wider range of functional elements,

which challenge the traditional toolkit of experimental

cognitive psychologists designed to deal with a

cognitively sequestered individual in a laboratory

environment that generally prevents interactivity. But

beyond issues of complexity and computational

promiscuity (Wilson & Clark, 2009), the dynamic

meshwork of internal and external resources encourages a

more qualitative idiographic cognitive science supported

by an observational toolkit that can code at a much

smaller time scale the evolution of a problem

representation and its solution (for an excellent example

of how such a toolkit can be developed, see Steffensen,2013). Finally, adapting the cognitive psychologist’s

laboratory to permit the physical manipulation of a

problem presentation offers a more representative window

onto thinking outside the laboratory. To be sure, people

can simulate and think in their head without physically

interacting with the outside world (although this internal

cogitation may well reflect the internalization of much

interactivity); but they often “go to extraordinary lengths

to avoid having to resort to (…) fully environmentally

detached reflection(s)” (Clark, 2010, p. 24, emphasis in

the original). The data presented here reveals the

importance of engineering task environments in the lab

that support distributed problem representations to betterunderstand the engagement of individuals as they explore

and manipulate the external world to solve problems.

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Vallée-Tourangeau, F., Euden, G., & Hearn, V. (2011).

Einstellung defused: Interactivity and mental set. The

Quarterly Journal of Experimental Psychology, 64,

1889–1895.

Weller, A., Villejoubert, G., & Vallée-Tourangeau, F.

(2011). Interactive insight problem solving. Thinking

& Reasoning, 17 , 429–439.Wilson, R. A., & Clark, A. (2009). How to situate

cognition: Letting nature take its course. In P. Robbins

and M. Aydede (Eds.), The Cambridge handbook of

situated cognition (pp. 55-77). New York: Cambridge

University Press.



"

(forthcoming in Synthese)

KNOWLEDGE AND COGNITIVE INTEGRATION

S. Orestis Palermos

University of Edinburgh

Abstract: Cognitive integration is a defining yet overlooked feature of ourintellect that may nevertheless have substantial effects on the process ofknowledge-acquisition. To bring those effects to the fore, I explore the topic ofcognitive integration both from the perspective of virtue reliabilism within

externalist epistemology and the perspective of extended cognition withinexternalist philosophy of mind and cognitive science. On the basis of thisinterdisciplinary focus, I argue that cognitive integration can provide aminimalist yet adequate epistemic norm of subjective justification: so long asthe agent’s belief-forming process has been integrated in his cognitivecharacter, the agent can be justified in holding the resulting beliefs merely bylacking any doubts there was something wrong in the way he arrived at them.Moreover, since both externalist philosophy of mind and externalistepistemology treat the process of cognitive integration in the same way, wecan claim that epistemic cognitive characters may extend beyond ourorganismic cognitive capacities to the artifacts we employ or even to other

agents we interact with. This move is not only necessary for accounting foradvanced cases of knowledge that is the product of the operation of epistemicartifacts or the interactive activity of research teams, but it can further lead tointeresting ramifications both for social epistemology and philosophy ofscience.

1. INTRODUCTION

Cognitive integration is an overlooked yet defining feature of our intellect. It

is the reason why, in contrast to mere stimulus-response automata, weentertain advanced beliefs in an epistemically responsible way, and why we

can do so even in the complete absence of any reasons to back those beliefs

up.

Nevertheless, within philosophy of mind and cognitive science, it is

only recently that the topic of cognitive integration was brought into focus.

This recent change of focus, however, has not been and is still not guided by

an attempt to understand how our complex brain capacities intertwine with

each other. Due to Fodor’s (1983) persuasive understanding of our intellectual



#

architecture as modular, in-the-head cognition has largely thought to be for

the most part domain specific and informationally encapsulated.1

Consequently, cognitive scientists who take the brain to be the primary

cognitive explanandum have so far been indisposed to explore and

understand the phenomenon of cognitive integration.

Instead, only after the hypothesis of extended cognition (Clark &

Chalmers 1998) was proposed (according to which external elements can be

proper parts of our cognitive systems), did the topic of cognitive integration

start to attract attention; claiming that artifacts can be parts of an agent’s

cognitive system presupposes an account of how such external elements can

be properly integrated into our cognitive loops. Accordingly, over the past

fifteen years, several attempts have been made to account for the process ofcognitive integration, most of which rely either on a common-sense

functionalist understanding of our minds (Clark & Chalmers 1998; Clark

2010), or on a mathematically inspired approach (Cark 2008; Chemero 2009,

Palermos 2014) that focuses on the dynamical nature of cognitive processes.

Interestingly, however, the topic of cognitive integration has also

recently emerged within epistemology in reference to the concept of our

epistemic cognitive characters. Even within epistemology, however, the

discussion of cognitive integration has not been entirely unrelated to the idea

of cognitive extension. Admittedly, Greco (2010)—the first to discuss (in

passing) the idea of cognitive integration in epistemological terms—does not

commit himself to the possibility of cognitive extension. Both Pritchard (2010)

and I (Palermos 2011), however, have noted that the notion of the epistemic

agent’s cognitive character , to which all of the agent’s knowledge-conducive

belief-forming processes must have been properly integrated, is open to an

interpretation along the lines suggested by the extended cognition hypothesis.However, and despite these few efforts to elucidate the process of cognitive

integration within epistemology, more needs to be said since the effects of this

process on knowledge could turn out to be surprisingly substantial.

To bring these effects to the fore, we need to approach the idea of

cognitive integration both from the perspective of virtue reliabilism in

externalist epistemology (section 2.2) and the perspective of extended

1 Despite the influence of Fodor’s work within philosophy of mind and cognitive science, hismodular understanding of the mind has met some considerable resistance by equallyinfluential philosophers. One clear example is the work of Churchland (1979, 1988, 1989).



$

cognition in externalist philosophy of mind (section 2.3). This

interdisciplinary focus will help us make apparent, in section 2.3, that both

views treat cognitive integration in essentially the same way—an observation

that we can then use to bring both perspectives together and thereby

demonstrate how our epistemic cognitive characters can extend beyond our

organismic cognitive capacities to the epistemic artifacts we employ. This

combination of externalist epistemology with externalist philosophy of mind,

however, as I will argue in section 3, is not just an available option, but is

actually necessary for accounting for advanced cases of knowledge whereby

one’s true believing is the product of the operation of epistemic artifacts.

Furthermore, this approach can generate interesting ramifications both for

social epistemology and philosophy of science, wherein the pursuit ofknowledge has been traditionally associated with the use of artifacts, carefully

tailored labs, and the combined efforts of epistemic agents working in

research teams. To start with, however, a few introductory remarks are first in

order.

2. THE PROCESS OF COGNITIVE INTEGRATION

2.1 Introduction

“John is sad” is an assertion that can be true or false. In producing this claim,

of course, perception will always be foundational in a certain way, but in

order to make, as well as check, the validity of the claim we combine several

other processes as well. In the background of our minds we have a biological

as well as a primordial (animalistic) conception of what a human being is and

we also have a socio-contextual sense of who “John” is; we have a theory ofmind that enables us to understand other people with thoughts and emotions

like ours, and we may even need a more personal theory of John’s character:

John has spent the entire night socializing at the pub and he just made a witty

joke, but the downwards motion of his eyes accompanied by a melancholic

smile at the end of his remark are enough to indicate to his close friends that

his mind is secretly occupied with his recent loss.

It is in a sense like this that theories (commonsensical as well as

scientific ones) have a top-down effect on the bottom-up input we receive



%

from our sense-apparatuses. This overriding effect, known as the theory-

ladeness of observations, has been well-studied both within philosophy of

science (Kuhn 1962; Hanson 1961; 1969) and philosophy of mind (Churchland

1979; 1988; 1989; Fodor 1984; 1988), and indicates that perceptual beliefs are

never ‘purely perceptual’, but they instead emerge out of the cooperation of

several intellectual capacities operating in tandem.

Now, considerations like these indicate that perception (traditionally

thought of as the most foundational aspect of our belief systems) may not be

all that basic, and have thereby hinted towards a relativistic picture of

science—and possibly epistemology as well—whose most prominent

proponents are thought to be Feyerabend (1975) and Kuhn (1962) (the latter

quite possibly unjustly though). Fortunately, however, at least as far as thetheory-ladeness of observation is concerned, recent studies within cognitive

psychology not only point away from relativism, but they also demonstrate

how the interaction between our theoretical beliefs and our sensory apparatus

has a facilitatory effect on the overall process of perception. For example,

exploring the literature on relevant experiments, Brewer and Lambert (2001)

concede that “perception is determined by the interaction of top-down theory

information and bottom-up sensory information” (178, my emphasis):

However, note that in all of the above cases the stimuli were eitherambiguous, degraded, or required a difficult perceptual judgment. In thesecases the weak bottom-up information allowed the top-down influences tohave a strong impact on perceptual experience. It seems likely that strong

bottom-up information will override top-down information. [...] Thus, thetopdown/ bottom-up analysis allows one to have cases of theory-ladenperception, but does not necessarily lead down the slippery slope ofrelativism (ibid.).2

2 Similarly, Estany (2001, 208) holds that

the beliefs of the higher or more fundamental level influence how perceptual unitsare interpreted by the lower levels [...] Humans use both types of processes inperception because each have characteristic advantages and disadvantages. Thanksto top-down processes we can recognize patterns with incomplete or degradedinformation. Moreover, top-down processes make perception faster, but they caninduce us to make mistakes in a perception by relying on previous knowledge.

Nevertheless, Estany further notes that even though our perceptual systems get guidancefrom higher-order expectations, when attention is caused by the mismatches between

expectation and reality, the inputs from the arousal system constitute a “reset wave” makingit possible to avoid arbitrary relativistic errors of perception (Estany 2001, 213).



&

Therefore, we should neither be misguided nor feel intimidated by the

possibly false impression of relativistic bias that the integration of our

intellectual capacities into one overarching belief-generating system may have

initially created. To the contrary, we should focus on the phenomenon of

cognitive integration because, as we shall see, it actually has a very important

facilitatory effect on our epistemic standing. In particular, the

interconnectedness of our cognitive capacities allows us to be subjectively

justified even if we lack explicit reasons for holding our beliefs. Provided that

we act in a conscientious mode—i.e., provided we are motivated to believe

what is true—cognitive integration allows us to trust the deliverances of our

cognitive abilities by merely lacking any negative reasons against (as opposed

to possessing positive reasons for) our beliefs. This sense of epistemicallyadequate—yet unreflective—cognitive responsibility can only be achieved by

agents like us, whose intellectual capacities are appropriately interconnected

such that in cases where there is something wrong with the way we form our

beliefs or with the beliefs themselves, we will be able to notice this and

respond appropriately. Otherwise—if there is nothing wrong—we can go on

about with our daily activities without questioning our epistemic standing

with respect to every single of the millions (possibly billions?) of beliefs we

enjoy in the course of our days.

On a first pass, this probably sounds sketchy, but focusing on

contemporary epistemology should allow us to both understand what this

unreflective sense of cognitive responsibility amounts to and why it is so

important.

2.2 Cognitive integration in epistemology

To start with, consider epistemic internalism, which takes an approach to

epistemic responsibility that is very different from the one suggested here.

According to traditional forms of internalism, one should always be able, at

least in principle, to access the reasons that justify one’s beliefs, by reflection

alone.3 This may initially sound as a reasonable demand, but the problem is

that it creates serious complications with respect to our perceptual and

empirical beliefs. Specifically, it poses the requirement that there be necessary

3 For classical defenses of this view see Chisholm (1977) and Bonjour (1985, ch. 2). See alsoSteup (1999), Pryor (2001, §3), Bonjour (2002), Pappas (2005), and Poston (2008).



'

support relations between one’s empirical and perceptual beliefs and one’s

evidence for holding them (such that one can be in a position to justify one’s

empirical and perceptual beliefs by reflection alone). As Hume’s problem of

induction demonstrates, however, this is impossible. Accordingly, it has been

traditionally assumed that Hume’s arguments lead to skepticism about our

empirical knowledge.4

Contemplating on the Humean problematic, however, Greco (1999)

argues that this is too fast. Hume’s arguments should not be directly

considered as skeptical ones. Instead, the immediate conclusion to be drawn

from them is that there are no necessary support relations between our

empirical beliefs and their evidence; that if the evidence for our empirical

beliefs is reliable, then it is at most contingently reliable. This realizationalone, however, cannot automatically lead to skepticism. Only after we

embrace the internalist understanding of knowledge, such that there always

be necessary support relations between one’s evidence and one’s beliefs do

we face skepticism.

In other words, in order to avoid skepticism about empirical and

perceptual knowledge, we must allow knowledge to be grounded on

evidence that is merely contingently reliable, and so we must give up the

requirement that one’s beliefs should always be internally—i.e., by reflection

alone—justified. Any adequate epistemology must be able to account for the

fact that merely contingently reliable evidence can give rise to knowledge (Greco

1999 , 273).

Now, in order to accommodate the above realization, contemporary

epistemologists have put forward process reliabilism; viz., the idea that

knowledge is true belief that is the product of reliable belief-forming

processes, where a reliable process is a process that results in a

4 The problem of induction is well known. We form our beliefs about unobserved matters offact and the external world on the basis of evidence provided by past and presentobservations and sensory appearances, respectively. In order, however, for the supportrelations between our empirical and perceptual beliefs and the evidence offered in theirsupport to be necessary, we also need the further assumptions that the future will resemblethe past and that sensory appearances are reliable indications to reality, respectively. Theproblem, however, is that both of these assumptions rely for their support on what theyassert. Consequently, given that circular reasoning is invalid, there are no necessary supportrelations between our empirical beliefs and the evidence offered in their support.Accordingly, the conclusion that has been traditionally drawn is that our empirical and

perceptual beliefs cannot amount to knowledge. For more details on a reconstruction ofHume’s skepticism along these lines, see (Greco 1999).



(

preponderance of true over false beliefs. Moreover, in direct response to the

Humean problematic, this approach “denies that one must know that one’s

evidence is reliable”, by making “de facto reliability the grounds of positive

epistemic status” (Greco 1999, 284-5). Accordingly, process reliabilism is an

externalist approach to knowledge, because—contrary to the traditional

account of knowledge as internally justified true belief—on this view, in order

to know, one does not need to know, be justified in believing (by reflection

alone, or any other means), or even believe that one’s beliefs are formed in a

reliable fashion. So long as one employs an objectively reliable process, one is

justified in holding the resulting belief.

Process reliabilism, therefore, has the resources to overcome the

Humean skepticism. There are, however, two serious complications with theview. The first one is that process reliabilism, as it stands, is too weak a

condition on knowledge because it allows any reliable belief-forming process

to count as knowledge-conducive, and this, as we shall see, is intuitively

incorrect. The second complication is that by making “de facto reliability the

grounds of positive epistemic status” process reliabilism misses a very

important dimension of our epistemic nature. While it is true that in order to

know we do need the way of forming our beliefs to be objectively reliable,

this sort of objective justification is not sufficient in its own. What we further

need is that we be subjectively justified in the sense that we must be somehow

sensitive to the reliability of our evidence.5 Process reliabilism, however,

ignores this dimension of our epistemically sentient nature altogether, to the

extent that it has been even criticized that it equates us to mere stimulus-

response automata (Fuller 2012).6 We can better appreciate these two

problems by taking a look at a few (eccentric, yet informative) examples.

Consider Hercules first:

5 Remember, however, that if, as Hume’s skeptical arguments demonstrate, the relation between evidence and belief is not necessary (see also fn. 4), then it is far from obvious how aperson can be subjectively justified, especially in externalist approaches such as processreliabilism. If a condition of ‘subjective sensitivity to the reliability of one’s evidence’ must besatisfied, then this should better be accomplished in a way that will not require knowledge of or

even beliefs about the said reliability (otherwise Hume’s skepticism will strike back).6 “Epistemic zombies” would probably be the name that David Chalmers would give to suchcreatures. Given, however, the present discussion I don’t think they could really exist.



)

Hercules (Adapted from Pritchard’s Temp (2009, 48))

Hercules tosses a drachma whenever he wants to form a belief about theweather outside. If it is heads, he forms the belief that it is sunny; if it is tailshe believes it is cloudy; and if it balances in between, he believes it is rainy.As it happens, Hercules’ way of forming his weather beliefs is perfectly

reliable, because Zeus, who wants to save Hercules from the embarrassmentof forming false weather beliefs, has an eye on him; every time he seesHercules tossing the coin arranges the world accordingly.

Hercules’ beliefs are formed in a highly reliable way. So, according to process

reliabilism, Hercules has knowledge of the weather conditions. Intuitively,

however, this is incorrect. There is a problem with the direction of fit between

his beliefs and the facts. In cases of knowledge, we want our beliefs to be true

because they correspond to the facts, and not because the facts comply withour beliefs; when one knows, one’s true beliefs are about the world, not the

other way around. In Hercules’ case, however, his beliefs are not true because

they are formed in a way that detects the facts. Instead, he first forms his

beliefs in an arbitrary way—he makes no efforts to ensure they will come out

true—and then Zeus takes over so that the facts will comply with Hercules’

beliefs. This, however, is not knowledge; it is the ‘luck of the gods’. If one day

Zeus had a fight with Hera, Hercules’ beliefs would cease coming out true.

Notice, however, that if Hercules used his cognitive abilities—say by

taking a look at the sky—to form his weather beliefs, then he would not run

into any such problems. If he didn’t form his beliefs in an arbitrary way, but

on the basis of his cognitive abilities, he would not need Zeus to tweak the

world so that his beliefs could systematically turn out true. If one’s beliefs are

the product of one’s cognitive abilities, then if they turn out to be true it will

be because they are sensitive to the facts; the direction of fit will be the correct

one.So, it may be proposed that the way to restrict the reliable belief-

forming processes to those that get the direction of fit correctly—such that

they can be knowledge-conducive—is to identify them with one’s cognitive

abilities, or, in other words, with those processes that can be intuitively

thought of as cognitive ones. But can all prima facie cognitive processes count

as cognitive abilities and thereby produce knowledge? The answer, as we

shall now see, must be a negative one, because there are reliable processes

that we might be inclined to categorize as cognitive ones, but which fail to



*

deliver knowledge, exactly because they disallow the agent to be subjectively

justified in employing them. Think about the Serendipitous Brain Lesion first:

Serendipitous Brain Lesion (Greco 2010, 149)

Suppose that S has a rare brain lesion, one effect of which is to reliably causethe true belief that one has a brain lesion. Even if the process is perfectlyreliable, it seems wrong that one can come to have knowledge that one has a

brain lesion on this basis.

Again, the unfortunate agent’s way of forming his true belief about the brain

lesion on the basis of his brain lesion is reliable. Process reliabilists, therefore,

must accept that he can gain knowledge in this way. As Greco claims,

however, this does not sound correct. Why not? Mainly because the way theagent forms his belief is so strange from his point of view that he cannot

accept he can gain knowledge in this way (Greco 1999, 2010). Accordingly,

there might be reliable in-the-head processes (such that we may be inclined to

call them cognitive ones) that we wouldn’t like to claim they are knowledge-

conducive cognitive abilities, because from the agent’s point of view they are

strange. More precisely, the underlying intuition here is that for a process to

be eligible to count as a cognitive ability it must not be strange, in the sense

that it must not be at odds with the rest of the agent’s cognitive system.7 The

reason is that if the process is strange, then, in light of the rest of his cognitive

system, the agent will reject both the process and its deliverances despite the

fact that they are in fact reliable—from the agent’s point of view, they aren’t.

So, in order for a process to be a candidate for qualifying as a cognitive ability

such that it can be knowledge-conducive it must not be inconsistent with the

rest of the agent’s beliefs and his methods of producing them. In other words,

it must be such that it can become part of, or be integrated into, the rest of the

7 An anonymous referee points out that strangeness is description-relative. Take vision forexample. We are all familiar with acquiring knowledge through seeing things. But learningabout the physiological and neural underpinnings of vision will surely seem strange to some;couldn't such a person say "This is really strange, and I don't really see how it works, but, Iguess, this is how I know the color of my shoes"? I think this is right, but this examplewouldn’t be problematic for the following two reasons. First, even though the explanationmay seem ‘strange’ to the agent (in the sense of being difficult to understand) it is not ‘at oddswith the rest of the agent’s cognitive system’. Second, the requirement that the process not bestrange does not refer to a reflective-explanatory understanding of the process (as in thereferee’s example), but to the presence of the process itself. Think about the analogy of astrange (i.e., eccentric) person who is nevertheless not a stranger: the requirement that the

process not be strange allows for the process to be ‘strange’ in the first sense, but not in thesense of being a ‘stranger’. I am thankful to the referee for bringing this ambiguity to myattention.



"+

agent’s cognitive system (Greco 2010, 152). And clearly, this is not the case

with the serendipitous brain lesion. The process is a cognitive malfunction,

and even more crucially, its output is so odd that no epistemic agent could

accept as true. In other words, the serendipitous brain lesion cannot count as

knowledge-conducive because it is so strange that it cannot become part of

the rest of the agent’s cognitive system and so cannot count as a cognitive

ability.8

Nevertheless, even a reliable process that is normal enough to become

part of the agent’s cognitive system cannot yet count as a cognitive ability that

can produce knowledge. Consider a further example:

Careless Math Student (Greco 2010, 149)

Suppose that S is taking a math test and adopts a correct algorithm forsolving a problem. But suppose that S has no understanding that thealgorithm is the correct one to use for this problem. Rather, S chooses it on awhim, but could just as well have chosen one that is incorrect. By hypothesis,the algorithm is the right one, and so using it to solve the problem constitutesa reliable process. It seems wrong to say that S thereby knows the answer tothe problem, however.

The careless student’s algorithm for solving the problem is also reliable. But

again, we cannot attribute knowledge to her. Why not? The reason is that sheemployed the right method on a whim, such that she could have very easily

employed another, incorrect method. Her reliable process is a fleeting one. It

is not a habit or a disposition of hers. Given the same circumstances, she

could have employed an inappropriate method, thereby, ending up with a

falsehood. If, however, the student had habitually invoked the correct

8 An anonymous referee insists that the brain lesion can yield knowledge, thereby implyingthat process reliabilism is a sufficient condition on knowledge. While it may be possible tomake the case for the sufficiency of process reliabilism, the orthodox view within mainstreamepistemology goes against this prospect. For classical rejections of the sufficiency of processreliabilism on the basis of thought experiments very similar to the Serendipitous BrainLesion, see Bonjour (1980), Lehrer (1990) and Plantinga (1993b). The main idea is thatreliability might be necessary for knowledge but what is further required is satisfaction of theinternalist intuitions with respect to the possession of subjective justification (as wementioned above, one of the problems for process reliabilism is that by making de factoreliability the grounds of positive epistemic status, it fails to capture the intuition that,somehow, we must also be sensitive to the reliability of our evidence). Internalists, typicallyrequire the possession of reflectively accessible reasons for said reliability. Here, followingGreco’s intuitions (1999; 2010, 149-155), we opt for a weaker condition of subjective

justification according to which the agent must lack beliefs against the reliability of his belief-forming process, where the process being strange from the agent’s point of view would count

as just one such defeating belief. For very similar intuitions on how the strangeness of theorigin of the relevant beliefs acts as a defeater in Bonjour (1980) and Lehrer’s (1990) thoughtexperiments see Goldman (1986, 111–112).



""

algorithm when the problems called for it, then we would indeed be inclined

to claim that she could gain knowledge on its basis. The reason for this is that

if a process is a disposition or a habit of the agent, then the agent will be able

to become aware of the circumstances in which it can become unreliable.

Otherwise, it seems arbitrary that the agent employed it in an appropriate,

but isolated case, and so cannot gain knowledge on its basis. In other words, a

reliable process that is normal—such that it can, in principle, become part of

the rest of one’s cognitive system—won’t be a candidate for qualifying as a

cognitive ability, unless it is also a disposition or a habit of the agent. Why is

this so? The intuition is that abilities, in general, are habits or dispositions

possessed by agents.9 But apart from such intuitions we have also noted that

in order for a reliable process to count as a cognitive ability it must be suchthat it can become part of (or be integrated into) the rest of the agent’s

cognitive system. One requirement for this, we have noted, is that the process

not be strange such that it won’t be inconsistent with the rest of the agent’s

cognitive system. What is further required, however, is that it also be coherent

with her cognitive system in the following sense: The agent must be able to

become aware that the process is unreliable in certain circumstances, because

this will allow her to non-accidentally endorse its deliverances in the rest of the

circumstances, even if she lacks any positive beliefs for its reliability. And in the

absence of any explicit reasons that are accessible through reflection alone—

recall the Humean problematic—the only realistic way for the agent to so

9 An anonymous referee is worried that I should not use ‘dispositions’ and ‘habits’ assynonyms. Specifically, not all dispositions are habits; someone or something, for example,may be disposed to act in a certain way—should the appropriate conditions obtain—even ifthe relevant person or thing has never behaved in that way before. Accordingly, the worryfurther goes, the fact that a cognitive ability is a disposition does not mean it will also be ahabit. In response, even though it is true that in one sense of the term, ‘dispositions’ are notalways going to be habits, there is another sense of the term that they are; according to thissecond sense of the term, to claim that cognitive abilities are dispositions means that abilitiesare character traits , or habitual behaviors that the agent tends to exhibit. A strong indicationthat this is how we should understand the dispositional nature of cognitive abilities has to dowith the fact that abilities can only be acquired and sustained through practice, whereasdispositions, in the other meaning of the term, can be possessed by an entity even if they arenever actually manifested (e.g., a vase may be fragile even if it has never been broken). As weshall see below, Greco appears to concur with this understanding of abilities as he claims they

are the stable traits of the agent’s cognitive character; a behavior can be in character only if it ishabitually manifested. See also (Greco 2010, 150). I am thankful to the referee for pressing thispoint.



"#

become epistemically responsible in employing a process is that it be a

disposition or habit of hers.10,11

So to summarize what we have gathered from the three examples

above, in cases of knowledge, we want one’s beliefs to be responsive to the

facts. Accordingly, we claimed that only prima facie cognitive processes can be

knowledge-conducive, but not all of them will do. The process must be a

cognitive ability, meaning that the agent will be able to be conscientious and

thereby subjectively justified in employing it. And in the absence of positive

reasons (that are accessible through reflection alone) for the reliability of the

cognitive process, this last condition can be satisfied in a realistic way only if

the relevant process is a normal disposition or habit of the agent such that it

can become part of (or be integrated into) the rest of her cognitive system.Notably then, the general idea, which all the above considerations are

alluding to, is that for a reliable process to be knowledge-conducive it must be

a cognitive ability. This idea has also appeared in the literature as the ability

intuition on knowledge and can be summarized by stating that knowledge is belief

that is true in virtue of cognitive ability.12 Now the end result of the above

considerations is that they fill in the details of which reliable processes may

plausibly count as cognitive abilities by demonstrating that it is only normal,

dispositional or habitual cognitive processes that the agent can be subjectively

justified in employing (and thereby able to gain knowledge from).

Now, building on considerations very similar to the above ones, Greco

(1999) has proposed a virtue reliabilist account of knowledge, which

emphasizes that when we assess whether some agent knows, we shouldn’t be

focusing on the reliability of isolated (cognitive) belief-forming processes, but

on the reliability of the overall agent, conceived of as a stable, interconnected

system of such belief-forming processes. 13

It is this interwoven totality of

10 I here say ‘the only realistic way’ because we can imagine, for instance, a case of a benevolent mentalist who hypnotizes the agent to trust a newly acquired process, and trust itonly in the appropriate conditions (thereby allowing him to be epistemically responsible inemploying it), despite the fact that the process is not a disposition of hers.11 For further discussion of the above intuitions on the Serendipitous Brain Lesion andCareless Math Student cases see (Greco 1999) and (Greco 2010, 149-155). For the discussion ofsimilar thought experiments and intuitions see (Bonjour 1980), (Goldman 1986), (Lehrer1990), and (Pantinga 1993b).12 The idea that knowledge must be grounded in cognitive abilities can be traced back to thewritings of Sosa (1988; 1993) and Plantinga (1993a). For more recent approaches to this

intuition, see Greco (1999; 2004; 2007) and Pritchard (2009; forthcoming; 2010a; 2010b).13 Any theory of knowledge that places in its center the ability intuition on knowledge willfall under the general trend of Virtue Reliabilism (abilities are normally understood as virtues



"$

cognitive abilities that give rise to one’s sense of epistemic self (or in Greco’s

terms to one’s ‘cognitive character’), and which should be the focus of our

epistemic assessment.

To make apparent the motivation for this change of epistemic focus,

remember that in order to avoid the Humean problematic we must

accommodate subjective justification in a way that does not involve

knowledge or even beliefs about reliability. At this point, Greco (1999, 289)

suggests that a promising strategy for doing so is to claim that “a belief p is

subjectively justified for a person S (in the sense relevant for having

knowledge) if and only if S’s believing p is grounded in the cognitive

dispositions that S manifests when S is thinking conscientiously” (i.e., when S

is motivated to believe what is true). In this way, the agent will employ hisreliable cognitive processes only in circumstances that have not been

problematic in the past, and he will be able to do so without even having any

beliefs about their reliability.14 Greco, then, goes on to further claim that the

dispositions/habits that a person manifests when she is thinking

conscientiously intertwine with each other and give rise to what we may call

one’s cognitive character (Greco 1999, 290). So, overall, “a belief p has a positive

epistemic status for a person S just in case S’s believing p results from the

stable and reliable dispositions that make up S’s cognitive character” (ibid.,

287-8).

and vice versa). To accentuate the features of his account, Greco calls his view AgentReliabilism , but it is clearly a version of Virtue Reliabilism—one that emphasizes theimportance of the overall agent in the manifestation of the relevant intellectual virtues. Foralternative, robust as well as weaker formulations of Virtue Reliabilism see Sosa (1993; 2007)and Pritchard ( forthcoming; 2010a; 2010b), respectively.14 The fact that people manifest highly specific, finely tuned dispositions to form their beliefsin certain ways but not in others amounts to an implicit awareness of the reliability of thosedispositions.

For example suppose that it seems visually to a person that a cat is sleeping on thecouch, and on this basis she believes that there is a sleeping cat on the couch. Supposealso that this belief manifests a disposition that the person has, to trust this sort ofexperience under these sorts of conditions, when motivated to believe the truth.Now, suppose that much less clearly, it seems visually to the person that a mouse hasrun across the floor. Not being disposed to trust this kind of fleeting experience, theperson refrains from believing until further evidence comes in. The fact that theperson, properly motivated, is disposed to trust one kind of experience but not theother, constitutes sensitivity on her part that the former is reliable. There is a clearsense in which she takes the former experience to be adequate to her goal of believingthe truth, and takes the latter experience not to be. And this is so even if she has no

beliefs about her goals, her reliability, or her experience (Greco 1999, 290 ) .

A similar argument can be found in (Sosa 1993, 60-63).



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In this way, Greco can do away both with strange and fleeting

processes. Strange processes cannot be part of the agent’s cognitive character

because they are not the kind of processes that a conscientious agent would

employ. Fleeting processes are also excluded. First, because they are not

dispositions or habits—so they cannot really count as character traits. And,

second (I may add), because, in the absence of reasons to believe that the

relevant process is reliable, it is only dispositions or habits that one can

become aware they are unreliable in certain circumstances and, so—without

relying on any beliefs about their reliability—use them conscientiously in the

rest of the circumstances.15

So in order to gain knowledge on the basis of a process the agent must

be able to employ a conscientious attitude towards that process. And in orderfor that to be the case, the relevant process must be a cognitive ability of the

agent, meaning that it must have been integrated into the agent’s cognitive

character. Now, despite the previous points on the importance of the

normality and dispositionality of the relevant process in order for it to count

as a genuine part of one’s cognitive character, Greco attempts to further

accentuate and shed some light on the integrated nature of our cognitive

characters by noting that the process of “cognitive integration is a function of

cooperation and interaction, or cooperative interaction with other aspects of

the cognitive system” (2010, 152). So, how exactly should we think about the

required conditions for a process to count as knowledge-conducive?

In general, every knowledge-conducive process must be a cognitive

ability such that the agent will be subjectively justified in employing it, which

requires that the process be integrated into the agent’s cognitive character by

cooperatively interacting with it. Accordingly, we may say that the only

necessary and sufficient condition for a process to count as knowledge-conducive is that it cooperatively interacts with the rest of the agent’s

cognitive character. Now, apparently, this makes the normality and

dispositionality criteria seem redundant—which strictly speaking they are—

but they may still have a role to play; normality and dispositionality of the

relevant process seem to be practical preconditions for the agent to be able to

15 Apart from the example given in the previous footnote, Greco has not attempted to provide

an account of how the process of subjective justification works. I assume, however, that hewouldn’t reject this falsificationist approach, as I cannot see how else subjective justificationcould be accommodated in an externalist way.



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cooperatively interact with it. The extent, however, to which each one of these

criteria may need to be satisfied will differ from case to case. An agent, for

example, may be subjectively justified in employing a process in the

appropriate conditions not because it is a normal disposition of hers but

because a benevolent mentalist hypnotized her to do so. In most realistic

cases, however, normality and dispositionality will still have a significant

guiding effect. The decisive effect of cognitive integration, however—on the

basis of which the agent can be conscientious and thereby subjectively

justified—will only be ensured if the agent’s cognitive character mutually

interacts with the relevant process. So, all we need to accept that a process is

knowledge-conducive is that it be integrated into the agent’s cognitive

character by cooperatively interacting with it. On the basis of this mutualinteraction with the rest of his cognitive system the agent will be able to

employ the relevant process conscientiously by merely lacking any beliefs

that it is unreliable (at least not in the circumstances in which he employs it),

or if the employment is involuntary, conscientiously accept its deliverances

when he lacks any beliefs that the conditions that gave rise to them were

unreliable.

Moreover, as we saw previously in the discussion of the three

examples, this process of cognitive integration gives rise to a coherentist effect

both on the level of processes (how the beliefs are generated) and on the level

of content (how the beliefs themselves combine). Also, it ensures that at least

directly related belief-generating mechanisms and their resulting beliefs will

be consistent.

Overall then, the epistemic importance of cognitive integration is that it

allows epistemic agents to satisfy the condition of subjective justification in a

minimalist way, which is nevertheless sufficient for acquiring knowledgeeven if one cannot—not even in principle—offer any explicit reasons in favor

of one’s beliefs. Stated explicitly, this minimalist condition of subjective

justification is that conscientious epistemic agents ought to accept the

deliverances of, and employ their cognitive abilities, only when they lack any

doubts that they are unreliable, given the conditions they employ them in.16 In

16 As an anonymous referee has pointed out I should make clear that this condition should be

restricted to reliable processes. We should not allow, for example, to an agent who forms his beliefs on the basis of astronomical considerations or wishful thinking to count as subjectively justified merely by lacking any doubts about the unreliability of his belief-forming processes.



"'

other words, cognitive integration allows epistemic agents to be conscientious

in the sense that in cases where there is something wrong with the way they

form their beliefs they will be able to spot this and respond appropriately.

Otherwise, they can go on formulating their beliefs, without worrying

whether they can actually offer reasons for every single one of them or for

their reasons for holding them.17

2.3. Cognitive integration in epistemology and philosophy of mind

In the introduction, I mentioned that the topic of cognitive integration has

been explored in epistemology also in reference to the possibility of cognitive

extension. The general idea is that the knowledge-conducive cognitiveabilities and their relation to one’s cognitive character as discussed within

virtue epistemology is particularly apt for an interpretation along the lines

suggested by the hypothesis of extended cognition. To make this clear it

should be helpful to repeat, for the last time, what are the important features

of a knowledge-conducive belief-forming process.

In general, the process must be a cognitive ability. In order for that to

be the case the process must be a cognitive process. This will guarantee the

correct direction of fit between the belief and the fact. Also, we want the

belief-forming process to be objectively reliable, where a reliable process is

Given, however, that the epistemic agent must be conscientious (i.e., motivated to believewhat is true) this qualification is actually redundant. If the agent is motivated to believe whatis true, he will not employ astronomical considerations or wishful thinking because he willhave noticed that such processes were notably unreliable in the past. For the same reason,they won’t even be parts of his (conscientious) cognitive character.17 Further to footnotes 14 and 15, in providing this sort of account of subjective justification, Ihave relied for the most part on phenomenological intuitions about how we seem to go aboutour beliefs in everyday life. Nevertheless, such phenomenological intuitions seem to alreadyentertain a certain degree of scientific support. Specifically, within cognitive psychology,there have been several studies indicating that subjects engage in analytic reasoning onlywhen they experience the metacognitive effect of the lack of ‘fluency’:

“Fluency is not a cognitive operation in and of itself but, rather, a feeling of easeassociated with a cognitive operation, it can be generated by nearly any form ofthinking. If a percept is blurry, we are aware that it was hard to see. If a word isphonemically irregular, we recognize the challenge in processing it. We knowwhether we had to struggle to bring a memory to mind and whether we had a hardor easy time solving a riddle. Because the metacognitive experience of fluency can begenerated by so many cognitive processes and is nearly effortless to access, it canserve as a cue toward judgments in virtually any situation”. (Oppenheimer

forthcoming).

For an overview on the metacognitive feeling of fluency see (Oppenheimer forthcoming) ,(Alter & Oppenheimer 2007) and (Unkelbach & Greifeneder 2013).



"(

one that tends to produce true rather than false beliefs. Recall, however, that

according to reliabilism, the agent does not need his evidence to be

necessarily reliable, such that he can be internally justified in holding his

belief; if forming a belief on a certain kind of evidence constitutes a reliable

belief-forming process, it does not matter that one’s evidence is only

contingently reliable; the agent, on his part, does not need to know or even

have any beliefs about the reliability of his way of forming beliefs. Instead, the

agent can be subjectively justified simply by forming his belief on the basis of

a process that is integrated into his cognitive character, which he employs

when he is thinking conscientiously. Now, in order for a process to be a

candidate for inclusion to the agent’s conscientious cognitive character, we

noted that it will probably have to be neither strange nor fleeting. In mostrealistic scenarios the process (1) will have to be normal so that the agent

won’t reject it when conscientious and (2) will have to be a disposition or a

habit of the agent, because (barring scenarios such as the mentalist case) it is

only dispositions or habits that one can become aware they are unreliable in

certain circumstances, and so—without relying on any beliefs about their

reliability—be able to employ them conscientiously in the rest of the

circumstances. As we further noted, however, even though normality and

dispositionality will, in most cases, be practical preconditions, they are neither

necessary nor sufficient for a process to count as integrated into the agent’s

cognitive character. Instead, the only thing that is required is that the process

be integrated into the agent’s cognitive character, by engaging in cooperative

interaction with the rest of the agent’s cognitive system. Accordingly, no

matter what the practical preconditions for this interactive process to be

achieved are, once it is in place, it will guarantee both that the relevant belief-

forming process is a cognitive process, and that it is indeed part of the agent’scognitive character such that he can be conscientious in employing it. So

putting all the above points together: a belief-forming process counts as a

cognitive ability and thereby as knowledge-conducive if and only if it is a

reliable belief-forming process that is integrated into the agent’s cognitive

character, on the basis of a process of cooperative interaction with it.18

18 Many externalist epistemologists would reject the above biconditional on the grounds that

in order for a process to be knowledge-conducive it should also be safe (where a safe processis one that could not have easily being wrong). Consider for example Anti-Luck VirtueEpistemology: S knows that p if and only if S’s safe belief that p is the product of her relevant cognitive



")

Now to see why we might get the impression of a close fit between

virtue epistemology and the hypothesis of extended cognition, here are three

common-sense functionalist criteria, which Clark (2010) suggests must be

satisfied by non-biological candidates in order to be included into an

individual’s cognitive system:

1) “That the process be reliably available and typically invoked”.

That is, the agent should habitually and easily invoke the external resource. In

other words, its employment must be a disposition/habit of the agent’s

overall cognitive mechanism.

2) “That any information thus retrieved be more-or-lessautomatically endorsed. It should not usually be subject to criticalscrutiny. […] It should be deemed about as trustworthy assomething retrieved clearly from biological memory”.

That is, the information in the resource must be regarded as normal and

reliable and not be necessarily reliable. It suffices that its employment result

into an equally trustworthy belief-forming process as the one of forming

beliefs on the basis of one’s own biological memory.19

At this point, however, one might object that being reliable is not the

same as being trustworthy (i.e., being regarded as reliable). But, in response,

notice first that Clark identifies the notion of trustworthiness of a process with

the idea of being “more-or-less automatically endorsed” or in other words

“not usually subject to critical scrutiny”. That is, the target process must not

have been (for the most part) problematic in the past. Moreover, the processes

under consideration are also supposed to be cognitive dispositions or habits

of the agent that he has repeatedly employed in the past, and so had they been problematic the agent would have noticed that and responded

appropriately. Accordingly, a trustworthy belief-forming process in Clark’s

account, will be one that tends to produce true rather than false beliefs, which

abilities (such that her safe cognitive success is to a significant degree creditable to her cognitiveagency) (Pritchard forthcoming , 20). Again, in (Pritchard 2010a, 76) we can read: “ knowledge issafe belief that arises out of the reliable cognitive traits that make up one’s cognitivecharacter, such that one’s cognitive success is to a significant degree creditable to one’scognitive character”. For a defense of the claim that the safety condition is not necessary forvirtue reliabilism to account for knowledge see (Palermos forthcoming)

19 That is, the process does not need to be, due to underlying logical or quasi-logical relations,100% reliable. Notice that memory is supposed to be reliable even though one maymisremember.



"*

is to say that it will be objectively reliable in the virtue reliabilist’s sense. What

the agent will deem reliable will be that which is objectively reliable, i.e., that

which has not been (for the most part) problematic in the past.

Furthermore, notice this negative way of deeming processes reliable

with which Clark concurs (i.e., that a trustworthy process is one that is not

usually subject to critical scrutiny such that it is more-or-less automatically

endorsed). What this means is that the agent does not need to have any beliefs

about why or whether his belief-forming process is trustworthy; it suffices

that it has not repeatedly caught his negative attention in the past. This is in

good agreement with the proposed minimalist understanding of subjective

justification according to which one does not need to rely on any beliefs but

simply on one’s motivation to believe the truth. For example, one will trustone’s vision in appropriate circumstances, just because vision has not been

notably problematic in the past (in those circumstances). By being motivated

to believe the truth one will thereby employ the belief-forming process that

has not in the past (notably) failed to be conducive towards that end, and

crucially, one will do so without even thinking about it.

3) “That information contained in the resource should be easily

accessible as and when required”.

That is, the agent must be able to employ it as if it was part of his organismic

cognitive mechanism. In other words, the resource must be integrated into the

agent’s overall cognitive mechanism.

So we see that the same features of a process that epistemologists deem

important in order for a process to be knowledge-conducive are required by a

common-sense functionalist understanding of cognition in order for a process

to count as part of one’s mind. This is a promising observation.

Notice, however, that even if there were no such close fit between these

broad features, we would still be able to show that the two theories are

essentially connected. The reason, as we noted before, is that some of the

above features (e.g., normality and dispositionality of the process) may be

conducive towards the process being knowledge-conducive, but they are

neither necessary nor sufficient for that end. Instead, the only requirement is

that the process be integrated into the agent’s cognitive character.



#+

Now in relation to this, philosophy of cognitive science has recently

started shifting its focus away from the above common-sense functionalist

criteria for including an external resource into one’s cognitive system. Instead,

Chemero (2009), Froese et al. (2013) and I (Palermos 2014) have suggested that

the only requirement for an external element to count as a constitutive part of

the agent’s cognitive system is that it be non-linearly related to the rest of the

agent’s cognitive system. The motivation for this is that, according to

dynamical systems theory, these non-linear relations give rise to an overall

non-decomposable system that consists of all the contributing parts. And two

reasons for postulating the overall system are that these non-linear

interactions (1) give rise to new systemic properties that belong only to the

overall system and to none of the contributing systems alone (therefore wehave to postulate the overall extended system) and (2) prevent us from

decomposing the two systems in terms of distinct inputs and outputs from

the one subsystem to the other (therefore we cannot but postulate the overall

system).20 What is even more interesting to our present purposes, however, is

that just as Greco holds that cognitive integration is a matter of interaction

and cooperation between cognitive processes, so those non-linear relations

that allow us to talk about integration within philosophy of mind and

cognitive science emerge only on the basis of cooperative feedback loops between

the contributing elements of the overall system.

Therefore both in epistemology and philosophy of mind and cognitive

science the same criterion (cooperative interaction with the rest of the agent’s

cognitive system) is required for a process to be integrated into an agent’s

cognitive system and thereby count as knowledge-conducive. This, however,

should not really come as a surprise. Given that virtue reliabilism holds that

knowledge must be the product of cognitive ability (however that ability may be realized) and that the hypothesis of extended cognition sets out to reveal

which processes can count as cognitive abilities (wherever they may be

located), this close fit between the two theories seems to be as it should be.

The conclusion that follows, then, is that there is no principled

theoretical bar disallowing extended belief-forming processes from counting

as knowledge-conducive cognitive abilities. Given that virtue reliabilism

20 For a detailed explanation of why the existence of non-linear relations that arise out of themutual interactions between agents and their artifacts ensures the existence of extendedcognitive systems see (Palermos 2014).



#"

makes no specifications as to whether knowledge-conducive cognitive

abilities should be located within the agent’s head, then, provided that the

condition of cognitive integration is met, the epistemic agent may extend his

knowledge-conducive cognitive character beyond his organismic cognitive

abilities by incorporating epistemic artifacts to it.

3. IS THIS NECESSARY FOR EPISTEMOLOGY AND WHAT ARE THERAMIFICATIONS?

Obviously, the possibility of knowledge-conducive cognitive characters that

may nevertheless be extended beyond our organismic cognitive capacities can

generate interesting ramifications both for traditional and social

epistemology, which may allow us to think about knowledge in new ways.

Focusing, however, on the integrated nature of our extendable cognitive

characters may not only be important for moving forward, but also necessary

for accounting for knowledge as we already think about it.

According to Greco (1999, 287), in addition to one’s organismic

cognitive abilities of the brain/central nervous system, a person’s cognitive

character may also consist of “acquired skills of perception and acquired

methods of inquiry including those involving highly specialized training oreven advanced technology”. The reason for this move is that we need to

account for advanced cases of knowledge where one’s believing the truth is

the product of the operation of epistemic artifacts such as telescopes,

microscopes, tactile visual substitution systems and so on.21 The problem,

however, is that in the traditional conception, cognition takes place strictly

within the agent’s head and so artifacts cannot be parts of one’s cognitive

character.

One way to sidestep this problem for virtue reliabilism, could be to

claim that, in such cases, it is merely the agent’s training and skill of using the

artifact, as mirrored in the agent’s neural/bodily architecture, that is the most

salient factor in the causal explanation of the agent’s cognitive success (i.e.,

believing the truth). Notice, however, that when an agent employs an

epistemic tool, his true belief arises as the product of the interaction between

his internal processes and the artifact. According to dynamical systems

21 See Bach-y-Rita and Kercel (2003) for a recent review on tactile visual substitution systems.



##

theory, then, the cognitive process that allows the agent to detect the truth is

not merely ‘aided’ or ‘assisted’ by the artifact but is, instead, constituted by it

as it arises out of the ongoing mutual interaction between the agent and the

artifact.22 Therefore, in a causal explanation of how the agent acquired his true

belief, it will be impossible to disentangle the agent’s training and skill of

using the artifact from his actual engagement with it. 23

But even if such decomposition were possible, notice in addition that

the part of the process that allows the agent to detect the truth, or in other

words to be sensitive to the facts, is the external component. To illustrate this,

consider, on one hand, an untrained agent in possession of a properly

working artifact. In that case, it is obvious that even though the agent will

initially be unable to form any (true or false) beliefs, eventually—providedthat he gains sufficient experience such that he can interact with it—not only

will he form beliefs, but he will also reliably enjoy cognitive success. On the

other hand, think about a well-trained agent, but in possession of a faulty

artifact. In this case, despite the agent’s excellent internal skills, it is evident

that he would be unable to reach any (non-lucky) true beliefs, no matter how

much he tried. It therefore seems that in such cases the most (and maybe the

only) significant factor that explains the truth-status of the agent’s belief is the

epistemic artifact. In other words, since the agent’s belief is true in virtue of

the artifact, the virtue reliabilist must account for it being part of the agent’s

cognitive system. Given, however, that cognition is normally supposed to

take place within the agent’s head, virtue reliabilists can only account for such

cases by wedding their view to the hypothesis of extended cognition.

Accordingly, combining the extended cognition hypothesis with virtue

reliabilism on the basis of their close fit does not seem to be just an available

option for epistemologists, but also necessary for dealing with advanced cases

22 It should be here noted that not every case of the employment of an artifact is a case ofcognitive extension, but only when the agent mutually interacts with it. For an objectivecriterion of constitution and on what may count as a genuine case of cognitive extension, see(Palermos 2014).23 Remember that according to virtue reliabilism and the underlying ability intuition onknowledge, knowledge is belief that is true in virtue of cognitive ability, where, according toGreco, “in virtue of” must be understood in causal explanatory terms. Even though severalproponents of virtue reliabilism agree on this general causal-explanatory understanding of

the view, there is disagreement on whether the relevant cognitive ability should be the “mostsalient” (Greco 2010) or merely a “significant” (Pritchard 2010b) factor in the causalexplanation of how the agent acquired his true belief.



#$

of knowledge where the latter is the product of the employment of epistemic

artifacts that the agent mutually interacts with.

Apart from the necessity of introducing the extended cognition

hypothesis within epistemology, however, this move can also generate

interesting ramifications, especially for social epistemology. For instance, it

can lead to the further claim that there could be cognitive characters that do

not just extend beyond an agent’s organismic capacities, but which are

instead distributed amongst several agents along with their epistemic artifacts.

The hypothesis of distributed cognition, which has been developed in parallel

to the hypothesis of extended cognition (Hutchins 1995 , Theiner et al. 2010,

Sutton et al. 2008, Wilson 2005, Heylighen et al. 2007), differs to the latter

position only in that this time the cognitive system extends to includeepistemic artifacts as well as other agents. And interestingly, most proponents

of the view (Sutton et al. 2008, Theiner et al. 2010, Wegner 1985, Tollefsen &

Dale 2011) point out again that it is the existence of non-linear cooperative

interactions between the contributing members and their artifacts that is the

criterion by which we can judge whether we have an integrated distributed

cognitive system. Accordingly, there could be knowledge-conducive cognitive

characters, which may nevertheless be distributed.

This is an interesting possibility, because it can allow us to combine an

individualistic approach to knowledge, such as virtue reliabilism, with the

hypothesis of distributed cognition in order to account for epistemic group

agents: Groups of individuals who exist and gain knowledge in virtue of a

shared, common cognitive character that mainly consists of a distributed

cognitive ability—a collective cognitive ability that emerges out of the

members’ mutual (socio-epistemic) interactions and which is not reducible to

the cognitive abilities possessed by the individual members, thereby allowingus to speak of a group agent in itself. This is important, because by

recognizing a group of people as a self-standing agent in itself, we can then

use an individualistic approach to knowledge to account for knowledge that

is collectively produced and which is, thereby, distinctively social. In other

words we can make sense of the claim that p is known by S (the group agent),

even though it is not known by any individual alone.24

24 For a more detailed explanation of how virtue reliabilism may be applied to epistemicgroup agents see (Palermos & Pritchard 2013).



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Remarkably, such group agents have already started being studied

within cognitive science. Consider for example transactive memory systems

(TMSs)—i.e., groups of two or more individuals who collaboratively encode,

store and retrieve information. The reason why TMSs are good candidates for

distributed cognitive systems—and thereby for epistemic group agents—is

that, as Sutton et al. observe (2008), such systems are likely to involve skillful

interactive simultaneous coordination of people who can thereby count as a

single integrated cognitive system. Therefore, we can use TMSs in order to

“conceptualize how people in close relationships may depend on each other

for acquiring, remembering, and generating knowledge” (Wegner et al. 1985 ,

253):

Ordinarily psychologists think of memory as an individual’s store ofknowledge, along with the processes whereby that knowledge is constructed,organized, and accessed. […] With transactive memory we are concernedwith how knowledge enters the dyad, is organized within it, and is madeavailable for subsequent use by it (ibid., 256).

Apart from incorporating already existing research from cognitive

science, however, the combination of virtue reliabilism with the hypotheses of

extended and distributed cognition can generate new avenues for research,

some of which have for a long time been inaccessible. An interesting exampleis the intersection between epistemology and the field of history and

philosophy of science. These two intimately related fields have so far been at

odds—an awkward situation owing to the fact that the former discipline has

traditionally being individualistic whereas the latter has for the most part

been socially oriented (hardly anyone could deny the social nature of the

scientific process, especially after the publication of Kuhn’s The Structure of the

Scientific Revolutions , in 1962). The present approach, however, could now

provide a useful link between the two fields. Science is primarily performed

by individual scientists employing their hardware and software epistemic

artifacts or by research teams operating within scientific labs that are uniquely

tailored to fit their purposes. Accordingly, the concepts of extended cognitive

characters and epistemic group agents could become very handy for a

mainstream epistemological analysis of the scientific progress. As Giere and

Moffat (2003, 308) note in their discussion of the scientific revolution of the

16th century,



#&

“No ‘new man’ suddenly emerged sometime in the sixteenth century....Theidea that a more rational mind...emerged from darkness and chaos is toocomplicated a hypothesis” [Latour 1986, 1]. We agree completely. Appeals tocognitive architecture and capacities now studied in cognitive sciences aremeant to explain how humans with normal human cognitive capacitiesmanage to do modern science. One way, we suggest, is by constructing

distributed cognitive systems that can be operated by humans possessingonly the limited cognitive capacities they in fact possess.

4. CONCLUSION

The topic of cognitive integration is a far-reaching multidisciplinary theme

that touches upon a wide range of disparate questions with the potential of

bringing several research areas together. Within epistemology, the

phenomenon of cognitive integration can reveal how knowers may be

subjectively justified despite the absence of positive reasons in favor of their

true beliefs, pointing towards a minimalist yet informative epistemic norm:

provided that one’s belief-forming process has been integrated into one’s

cognitive system/character and that one is motivated to believe what is true,

one can be justified in holding the resulting belief merely by lacking any

doubts that his way of forming his belief, or that the belief itself, is

inappropriate. Within philosophy of mind, cognitive integration can explain

how it is possible to extend our cognitive capacities beyond our organisms tothe artifacts we employ or other individuals we may interact with. And

combining philosophy of mind with epistemology by using the process of

cognitive integration as the connecting point provides the necessary means to

account for advanced cases of knowledge where the known belief is true in

virtue of the operation of epistemic artifacts or even the activity of

collaborative groups. Quite likely, however, such an account of knowledge

won’t be valuable just within epistemology, philosophy of mind and their

intersection. Focusing on the essentially technological and collaborative

nature of the scientific process, such an approach to knowledge could finally

provide a strong link between the related but so far persistently isolated fields

of philosophy of mind, philosophy of science, and epistemology.



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Acknowledgments

I am very thankful to John Greco and an anonymous referee for detailed

comments on previous versions of this paper. Research in the area of this

paper was carried out as part of the AHRC-funded ‘Extended Knowledge’

research project (AH/J011908/1).

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Theoretical Neutrality’’, Philosophy of Science 55: 188-198.Froese, T., Gershenson, C., and Rosenblueth, D., A. (2013). ‘The Dynamically

Extended Mind’. http://arxiv.org/abs/1305.1958

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Epistemology: A Journal of Knowledge, Culture and Policy 26, 267-83.

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the Social Merge’. Social Studies of Science. 33/2, pp. 1-10.

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Ridgeview Press, pp. 273-296.

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Perspectives from Ethics and Epistemology. M. DePaul & L. Zagzebski

(eds.), Oxford: Oxford University Press.

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—— (2010). Achieving Knowledge: A Virtue-Theoretic Account of Epistemic

Normativity. Cambridge University Press.

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Oxford: Clarendon Press.

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and Contemporary Readings (3rd Ed.), (ed.) L. Pojman, 310-21, Belmont,

CA: Wadsworth.

Sutton, J., Barnier, A., Harris, C., Wilson, R. (2008). ‘A conceptual andempirical framework for the social distribution of cognition: The case

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(Eds.), The Experience of Thinking (pp. 11-32). New York: Psychology

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Mind Thesis’. Cognitive Processing , Vol. 6, Issue 4, pp. 227-236.



Radically Enactive Cognition in Our Grasp

Daniel D. Hutto

University of Hertfordshire

Òthe question is not to follow out a more or less valid theory

but to build with whatever materials are at hand.

The inevitable must be accepted and turned to advantageÓ

- Napoleon Bonaparte

Abstract

Radically Embodied/Enactive accounts of Cognition, REC, propose to fundamentally

shift the way cognitive scientists think about the basic nature of mentality. This paper

argues that focusing on the sophisticated but unplanned character of human manual

activity enables such accounts to address a standard worry about their scope and reach. A

counter proposal for handling such cases by defenders of Conservative

Embodied/Enactive account of Cognition, CEC, is examined and found wanting. CEC

accounts make appeal to Action Oriented Representations (AORs) to do the work that

fans of REC argue is done without representational mediation. It is argued that

naturalistically inclined defenders of CEC face a crippling dilemma.



Radically Enactive Cognition in Our Grasp 2

2




3

1. Reckoning with REC

For those working in the sciences of the mind these are interesting times. Revolution is,

yet again, in the air. This time it has come in the form of new wave thinking about the

basic nature of mind of the sort associated with radically embodied or enactive

approaches to cognition; REC for short. REC approaches are marked out by their

uncompromising and thoroughgoing rejection of intellectualism about the basic nature of

mentality. As Varela, Thompson and Rosch (1991) saw it the defining characteristic of

this movement is its opposition to those theories of mind that Òtake representation as their

central notionÓ (p. 172). The most central and important negative claim of REC is its

denial that all forms of mental activity depend on the construction of internal models of

worldly properties and states of affairs by means of representing its various features on

the basis of retrieved information.

Not since the ousting of behaviourism with the advent of the most recent cognitive

revolution has there been such a root and branch challenge to widely accepted

assumptions about the very nature of mentality. In a remarkable reversal of fortune, it is

now a live question to what extent, if any, representational and computational theories of

the mind Ð those that have dominated for so long Ð ought to play a fundamental role in

our explanatory framework for understanding intelligent activity. Defenders of REC

approaches argue that representation and computation are neither definitive of, nor

provide the basis of, all mentality.




4

From the side-lines, interested onlookers might be forgiven for thinking the revolution

is already over; embodied and enactive ways of thinking are already comfortably

ensconced, having established deep roots in a number of disciplines. Far from merely

being at the gates, the Barbarians are, it seems, now occupying the local cafŽs and wine

bars in the heart of the city. Even those who most regret this development are prepared to

acknowledge that, de facto, there has been a major sea change. Lamenting the rise of a

pragmatist trend in cognitive science, Fodor (2008) acknowledges that REC-style

thinking in cognitive science is now the ÔmainstreamÕ. He puts this down to an infectious

disease of thought (Ôa bad coldÕ Ð as he puts it p. 10). Others are edgily aware of the

spectre of REC approaches Òhaunting the laboratories of cognitive scienceÓ (Goldman

and de Vignemont 2009, p. 154). ÔPervasive and unwelcomeÕ is the verdict of these

authors: REC may be everywhere but is something to be cured or exorcised, as soon as

possible.

Despite their growing popularity, which some hope is nothing more than a short-lived

trend, REC approaches remain hotly contested. Certainly, it is true that there has yet to be

a definitive articulation of the core and unifying assumptions of embodied and enactive

approaches to cognition Ð EC approaches Ð radical or otherwise. Indeed, there is some

reason to doubt that it will be possible to group together all of the offerings that currently

travel under the banner of EC by identifying their commitment to a set of well-defined

core theoretical tenets (see Shapiro 2011, p. 3). Nevertheless, if REC approaches, in

particular, are to maintain credibility and avoid charges of simply riding the crest of a

fashionable wave, at a bare minimum, serious objections from the old guard should be

convincingly answered.




5

Some criticisms are easier to deal with than others. One line of argument draws on

observations about the proper order and requirements of cognitive explanations. Fodor

(2008) hopes to dispatch REC with one fell blow by observing that positing of

representationally-based thinking is the minimum requirement for explaining any and all

activity that deserves the accolade Ôintelligent respondingÕ Ð an observation predicated on

the assumption that we can draw a principled bright line between what is properly

cognitive and what is not. Accordingly, he insists that we have no choice but to accept

that Òthe ability to think the kind of thoughts that have truth-values is, in the nature of the

case, prior to the ability to plan a course of action. The reason is perfectly transparent:

Acting on plans (as opposed to, say, merely behaving reflexively or just thrashing about)

requires being able to think about the worldÓ (p. 13).

In a nutshell, this is FodorÕs master argument for thinking that pragmatist REC-style

approaches to the mind must be false: for to think the kinds of thoughts that have truth-

values is to think thoughts with representational content and, presumably, to make plans

requires manipulating these representations (and their components) computationally.1 So,

in short, if all bona fide intelligent action involves planning, and all bona fide planning

involves computing and representation then this is bad news from the frontline for REC

rebels.

Without a doubt some problems, indeed, perhaps whole classes of problems, are best

addressed through advanced careful planning Ð planning of the sort that requires the rule-

governed manipulation of truth-evaluable representations. Sometimes it is not only

advisable, but utterly necessary to stand back and assess a situation in a relatively

detached manner, drawing explicitly on general background propositional knowledge of




6

situations of a similar type and using that knowledge to decide Ð say, by means of

deduction and inference Ð what would be the correct or most effective approach. This can

be done before initiating any action or receiving any live feedback from the environment.

This is the preferred strategy for dealing with situations, such as defusing bombs, in

which a trial and error approach is not advisable. Making use of remote representations

also works equally even for more mundane types of tasks Ð those that include, for

example, figuring out the best route from the train station to oneÕs hotel in a foreign city

where one seeks to do this in advance from the comfort of oneÕs office, long before

boarding a plane.

Such intelligent pre-planning can be done at a remove, and reliably, if it is possible to

exploit and manipulate symbolic representations of the target domain on the assumption

that one has the requisite background knowledge and can bring that knowledge to bear.

This will work if the domain itself is stable over time since that will ensure that any

stored representations remain up to date and accurate. By using representations of a well-

behaved domainÕs features and properties, and having a means of knowing,

determinately, what to expect of it if it changes under specific modifications and

permutations, it is possible for a problem solver to plan how to act within it without ever

having to (or indeed ever having had to) interact with it in a first-hand manner. This is, of

course, the ideal end state of high theoretical science.

As linguistic beings, humans are representation mongers of this sort and thus regularly

adopt this basic strategy to solve problems. Our cultural heritage provides us with a store

of represented knowledge Ð in many and various formats Ð that enables us to do so,

successfully, under the sorts of conditions just mentioned. But it hardly follows that this




7

type of cognitive engagement is the basis of, required for, or indeed suitable for, all sorts

of tasks Ð always and everywhere. Echoing Ryle (1949), No‘ (2009) hits the nail on the

head, noting that, Òthe real problem with the intellectualist picture is that it takes rational

deliberation to be the most basic kind of cognitive operationÓ (p. 99, emphasis added).

Intellectualism of this unadulterated kind Ð of the sort that assumes the existence of

strongly detached symbolic representations of target domains Ð has fallen on hard times.

It finds only a few hard-core adherents in todayÕs cognitive sciences. Indeed, if anything

has promoted the fortunes of REC it has been the dismal failure of this sort of rules and

representation approach when it comes to dealing with the most basic forms of intelligent

activity. This is the headline-grabbing lesson of recent efforts in robotics and artificial

intelligence, which have provided a series of existence proofs against strong

representationalism about basic cognition.

Pioneering work by Brooks (1991a, 1991b), for example, reveals that intellectualism

is a bad starting point when thinking about how to build robots that actually work. There

are important lessons to learn by paying attention to architectonic requirements of robots

that are able to complete quite basic sorts of tasks, such as navigating rooms while

avoiding objects or recognizing simple geometrical forms and shapes. Inverting standard

intellectualist thinking, Brooks famously rejected the Sense-Model-Plan-Act approach,

and built robots that dynamically and frequently sample features of their local

environments in order to directly guide their responses, rather than going through the

extra steps of generating and working with descriptions of those environments. These

first generation behaviour-based robots, and those that followed after them, succeed

precisely because the robotsÕ behaviours are guided by continuous, temporally extended




8

interactions with aspects of their environments rather than working on the basis of

represented internal knowledge about those domains, knowledge that would presumably

be stored somewhere wholly in the robotsÕ innards. The guiding principle behind BrooksÕ

so-called subsumption architectures is that sensing is directly connected with appropriate

responding without representational mediation. Crucially, the great success of these

artificial agents demonstrates that it is possible for a being to act intelligently without

creating and relying on internal representation and models. Very much in line with

theoretical worries raised by the frame problem, it may even be that, when it comes to

basic cognition, this is the only real possibility.

Not just artifice but nature too provides additional support for the same conclusion.

Cricket phonotaxis (Webb 1994, 1996) is a vivid example of how successful on-line and

successful navigation takes place in the wild, apparently without the need for

representations or their manipulation. Female crickets locate mates by attending to the

first notes of male songs, frequently adjusting the path of their approach accordingly.

They only manage this because the male songs that they attune to have a unique pattern

and rhythm Ð one that suits the particular activation profiles of the female interneurons.

The capacity of these animals to adjust their behaviour when successfully locating mates

requires them to engage in a continuous interactive process of engagement with the

environment. In doing so they exploit special features of their non-neural bodies Ð

including the unique design of their auditory mechanism Ð as well as special features of

the environment Ð the characteristic pattern of the male songs. In this case a beautiful

cooperation arises because of the way the cricketÕs body and wider environment features




9

enable successful navigational activity Ð activity that involves nothing more than a series

of dynamic and regular embodied interactions.

For reasons of space I will not rehearse in full detail precisely how behaviour-based

robots or insects make their way in the world. These cases are well known and much

discussed (for excellent summaries in greater detail, including other examples see

Wheeler 2005, ch. 8 and Shapiro 2011, ch. 5). For the purposes of this essay it suffices to

note that when bolstered by the articulation of a supporting theoretical framework, one

easily provided by dynamical systems theory, these observations offer a serious and well-

known challenge to the representationalist assumptions of intellectualist cognitive science

(Beer 1998, 2000, Thompson 2007, Garz—n 2008, Chemero 2009).

In sum, what the foregoing reflections teach us is that there are cases in which bodily

and environmental factors play ineliminable and non-trivial parts in making certain types

of cognition possible. A familiar intellectualist response to these sorts of examples is to

try to cast these wider contributions as playing no more than causal supporting roles that,

even if necessary to enable cognition do not constitute or form part of it. For reasons that

should be obvious from the foregoing discussion, it is not clear how one might motivate

this interpretation and make it stick with respect to the sorts of cases just described.

In rejecting representationalism, REC takes at face value what attending to the

architectonic details of how these agents work suggests Ð i.e. that the specified bodily and

environmental factors are fully equal partners in constituting the embodied, enactive

intelligence and cognition of these artificial and natural agents. Accordingly, although for

certain practical purposes and interventions it may be necessary to carve off and focus on

specific causally contributing factors in isolation, the cognitive activity itself cannot be




10

seen as other than Òa cyclical and dynamic process, with no nonarbitrary start, finish, or

discrete stepsÓ (Hurley 2008, p. 12, see also Garz—n 2008, p. 388). Or, put otherwise,

when it comes to understanding cognitive acts Òthe agent and the environment are non-

linearly coupled, they, together constitute a nondecomposable systemÓ (Chemero 2009, p.

386).

In promoting this sort of line, REC flags up the Ôreal dangerÕ that Òthe explanatory

utility of representation talk may evaporate altogetherÓ (Wheeler 2005, p. 200). As

Shapiro (2011) notes, the interesting question is whether an anti-representationalist

paradigm has real prospects of replacing intellectualist cognitive science altogether. And,

as indicated above, he is right to suppose that the two main, complementary Òsources of

support for Replacement come from (i) work that treats cognition as emerging from a

dynamical system and (ii) studies of autonomous robotsÓ (p. 115). While this is a

potentially powerful cocktail, it remains to be seen just how far it might take us. For to

make a convincing case for their far-reaching revolutionary ambitions, proponents of

REC must take the next step and Òargue that much or most cognition can be built on the

same principles that underlie the robotÕs intelligenceÓ (Shapiro 2011, p. 116, emphasis

added).

Rather than denying that there can be no such thing as non-representational cognition,

intellectualists might take heart from this challenge and agree to split the difference,

allowing that very basic forms of cognition Ð of the sort exemplified by robot and insect

intelligence Ð might be suitable for REC treatment but not the rest. This is to adopt a kind

of containment strategy Ð a kind of theoretical kettling or corralling. Intellectualists might

be tempted to concede that supporters of the radical left have a point, up to a point Ð




11

allowing that Òrepresentations are not necessary for coordinated responses to an

environment with which one is dynamically engagedÓ (Shapiro 2011, p. 153). But this

concession would be made in the secure knowledge that it Òwould support only the

conclusion that agents do not require representations for certain kind of activities.

However, a stronger conclusion, for instance that cognition never requires

representational states, does not followÓ (Shapiro 2011, p. 153).

REC approaches would be, accordingly, of limited value on the assumption that they

wonÕt scale up. Call this the Scope objection. It allows one to accept certain lessons

learned from the lab and nature while safe in the knowledge that even if representations

are not needed to explain the most basic forms of cognition that this in no way poses an

interesting threat to intellectualism since the sorts of cases in question Òrepresent too thin

a slice of the full cognitive spectrumÓ (Shapiro 2011, p. 156). This is in line with the oft-

cited claim that some behaviour is too off-line and representation hungry to be explained

without appeal to the manipulation of symbolic representations. In particular, non-

representational cognition, which might do for simple robots and animals, isnÕt capable of

explaining properly world-engaging, human forms of cognition. But should that

assessment prove mistaken Ð if REC approaches were to make substantial in-roads in this

latter domain Ð then the boot might just be on the other foot. For it might turn out that

representationally hungry tasks only make up a very small portion of mental activity;

representationally-based cognition might be just the tip of the cognitive iceberg.




12

2. A Helping Hand

This is where reflection on the special prowess of the human hand comes in handy. It

cannot be denied that a great deal of human manual activity is connected with

sophisticated forms of cognition.

Milner and GoodaleÕs (1995) famous experiments reveal that humans can perform

remarkably demanding manual acts, with precision Ð acts requiring the exercise of very

fine-grained motor capacities, such as posting items through slots with changing

orientations Ð even when they lack the capacity to explicitly report upon or describe

visual scenes they are dealing with.

Nor, with only rare exceptions, is it credible that humans normally learn how to use

their hands in these sorts of ways by means of explicit, representationally mediated

instruction, the rules for which only later becoming submerged and tacit. It is not as if

children are taught by their caregivers through explicit description how to grasp or reach

for items. Far more plausibly, is the hypothesis that we become handy through a

prolonged history of interactive encounters Ð through practice and habit. An individualÕs

manual know how and skills are best explained entirely by appeal to a history of previous

engagements and not by the acquisition of some set of internally stored mental rules and

representations. To invoke the favourite poetic motto of enactivists, this looks,

essentially, to be a process of Ôlaying down a path in walkingÕ or in this case, ÔhandlingÕ.




13

It is possible that the special manual abilities of humans are sophisticated enough to

have provided the platform and spurred on other major cognitive developments. Some

very strong claims have been made about the critical importance of the ways in which we

use our hands in this regard Ð ways that some believe are responsible for enabling the

emergence of other distinctively forms of human cognition, consciousness and culture.

For example, Tallis (2003) regards our special brand of manual activity as the ultimate

source of our awakening to self-consciousness. He tells us, ÒHerein lies the true genius of

the hand: out of fractionated finger movements comes an infinite variety of grips and

their combinations. And from this variety in turn comes choice Ð not only in what we do

É but in how we do it É [and w]ith choice comes consciousness of actingÓ (p. 175).

If Tallis is to be believed, ÒBetween the non-stereotyped prehensions of hominid hand

and the stereotyped graspings of the animal paw there is opened a gap which requires,

and so creates, the possibility of apprehension to cross itÓ (p. 36). These claims are

tempered by the remark that ÒWe may think of the emergence of distinctive capabilities

of the human hand as lighting a fuse on a long process that entrained many other parts of

the human body and many other faculties as it unfoldedÓ (p. 6, emphasis added). This

allows for the possibility that, ÒThe crucially important differences between human and

non-human hands do not alone account for the infinitely complex phenomenon, unique in

the order of the universe, of human culture. It is not so much the differences Ð which are

very important Ð but the ability to make much of the differencesÓ (p. 33).2

Whatever is ultimately concluded about the defensibility of this last set of claims, the

point is that if it should turn out that much human manual activity is best explained

without appeal to the manipulation of rules or representations then defenders of REC will




14

have made significant progress in addressing the scope challenge. REC approaches will

have shown the capacity to advance well beyond dealing with the antics of a behaviour-

based robots and insects, having moved deep in the heart of distinctively human

cognition.

Are there further grounds for thinking that manual activity is best explained in a

representation-free way? TallisÕs (2003) philosophically astute and empirically informed

examination of the hand provides an excellent starting point for addressing this question.

He claims that Òthe hand [is] É an organ of cognitionÓ, and is so Ôin its own rightÕ (p. 28,

p. 31). This is not to say that the hand works in isolation from the brain, indeed, Tallis

stresses that the hand Ð for him, the tool of tools Ð is the ÒbrainÕs most versatile and

intelligent lieutenantÓ (p. 22). Of course, this way of putting things suggests that the hand

is, when all goes well, in some way nothing but a faithful subordinate Ð one that works

under top-down instruction and guidance from above. This underestimates the bi-

directional interplay between manual and brain activity - interplay of the sort that

explains why the distinctive manual dexterity of Homo sapiens, which sets us apart even

from other primates who also have remarkable abilities in this regard, was likely one of

the Ômain driversÕ of the growth of the human brain (p. 22).

These ideas can be taken much further if one fully rejects what Tallis calls the

standard ploy.

While it is perfectly obvious that voluntary activity must be built up out of involuntary

mechanisms, there are profound problems in understanding this. There are particular

problems with the standard ploy invoked by movement physiologists: proposing that




15

the automation incorporates ÔcalculationsÕ that the brain (or part of it) ÔdoesÕ, which

permit customisation of the programs to the singularities of the individual action (p.

65, emphases added)

Invoking the standard ploy amounts to making a hand waving and anthropocentric

appeal to representational contents so as to specify and fill out hypothesized motor plans

and motor programmes that supply instructive orders from on high, lending intelligence

to and directing manual activity. For example, on this view motor plans, intentions and

programs are understood as Òpropositional attitudes with contents of the form Ôlet [my]

effector E perform motor act M with respect to goal-object GÕÓ (Goldman 2009, p. 238).3

The trouble is that even if we imagine that such representational contents exist, it is

difficult to see how they could do the required work. The only chance they could have of

specifying what is to be done, and how it is to be done, would be if they go beyond

issuing very general and abstract instructions of the sort that Goldman gestures at above.

Only very fine-grained instructions would be capable of directing or controlling

specific acts of manual activity successfully. This raises a number of questions. How do

brains decide which general kind of motor act, M, is the appropriate sort of motor act to

use in the situation at hand? This, alone, is no simple business Ð given the incredible

variety of possible manual acts.4

And, even if we put that concern aside, proponents of the view that brains can initiate

and control manual acts by traditional intellectualist means are left with the problem of

explaining how and on what basis brain decides how to execute any given act. A major

problem for traditional forms of intellectualism is that the requirements for successfully




16

performing any particular motor act are tied to a unique and changing context. For

example, even if everything else remains static the speed, angle of approach and grip

aperture need to be altered appropriately at successive stages as one does something as

simple as picking up a coffee cup. In a nutshell:

a particular challenge É has been to explain how cognition and perception processes

regulate complex multi-articular actions in dynamic environments. The problem seeks

to ascertain how the many degrees of freedom of the human motor system (roughly

speaking the many component parts such as muscles, limb segments, and joints) can

be regulated by an internally represented algorithm É and how the motor plan copes

with the ongoing interaction between the motor system and energy fluxes surrounding

the system, e.g., frictional forces and gravitational forces ... Not even the attempt to

distinguish between the motor plan and the motor program has alleviated the problem

in the literature (Summers and Anson, 2009) (Araœjo & Davids 2011, p. 12).

Successful manual activity requires bespoke and on the fly customisation. Hence, it is

deeply implausible that brain can simply identify what is required for successfully

completing a certain type of activity and simply issuing general instructions to be carried

out in form of ordering pre-programmed routines to be carried out. The implausibility of

this suggestion is underscored by the fact that Òmost of the things we do are unique even

though they may have stereotyped componentsÓ (Tallis 2003, p. 67). Not surprisingly,

human manual activity Ð despite its unique complexities Ð seems to depend on

interactions between the brain, body and environmental interactions which involve




17

essentially the same kinds of dynamic interactive feedback and temporally extended

engagements needed to explain the intelligent antics of behaviour-based robots and

insects.

It must be noted that concomitant with abandoning the standard ploy in favour of a

REC based approach comes the admission that what we are dealing in most cases of

manual activity are not strictly speaking actions. This will surely be so if we operate with

a strict concept of action Ð one that insists on a constitutive connection between actions

and intentional states, where the latter are conceived of as requiring the existence of

propositional attitudes of some sort. But all that follows from this, as Rowlands (2006)

observes, is that Òmost of what we do does not count as actionÓ (p. 97).

Respecting the stipulated criterion on what is required for action, many philosophers

acknowledge the existence of non-intentional doings, motivated activities and/or deeds.

For example, Velleman (2000) recognizes the need to:

define a category of ungoverned activities, distinct from mere happenings, on the one

hand, and from autonomous actions, on the other. This category contains the things

one does rather than merely undergoes, but that one somehow fails to regulate in the

manner that separates autonomous human action from merely motivated activity (p.

4).

On the face of it, the great bulk of animal doings takes the form of sophisticated forms

of highly coordinated, motivated activity that falls well short of action if acting requires

forming explicit, if non-conscious, intentions and deliberate planning, at any level.




18

Picking up on FodorÕs earlier remark, far from being mere Ôthrashings aboutÕ or

Ôreflexive behaviorsÕ, such unplanned engagements appear to be quite skillful, and

sometimes even expert, dealings with the world. If REC has the right resources for

explaining the wide class of such doings then it has the potential to explain quite a lot of

what matters to us when it comes to understanding mind and cognition.

3. The Non-Standard Ploy: Representationalism Rescued?

Despite all that has been said in favour of REC, many will baulk at going so far. There

are weaker, and much more conservative and conciliatory ways of taking on board what

is best in embodied and enactive ideas without abandoning intellectualism in a wholesale

way. For example, intellectualists can happily accept that various facts about embodiment

are causally necessary in making certain types of intelligent responding possible and in

shaping its character without this concession in any way threatening the idea that

cognition is wholly constituted by representational facts or properties.

Trivially, it is clearly true that what a creature perceives depends on contingent facts

about the nature of its sensory apparatus Ð thus bats, dolphins and rattlesnakes perceive

the world differently and perceive different things because they are differently embodied.

Moreover, no one denies that what and how we perceive causally depends on what we do

Ð thus, it is only by moving my head and eyes in particular ways that certain things

become visible and audible. Obviously, these truisms in no way threaten intellectualism.

Things can be taken further still without rocking the boat too much. A more daring

thesis, one that several authors have lighted upon, is that extended bodily states and




19

processes might Ð at least on occasion Ð serve as representational or information-carrying

vehicles. As such, they can play unique computational roles in enabling some forms of

cognition (Clark 2008b). Or, in the lingo of Goldman and de Vignemont (2009) perhaps

those attracted to embodied and enactive accounts of cognition should be taken as

claiming that some mental representations are encoded in essentially bodily formats.

These renderings of what enactive and embodied accounts have to offer are conservative

with respect to a commitment to representationalism. They are perfectly compatible with

asserting that Òthe mind is essentially a thinking or representing thingÓ (Clark 2008, p.

149); or that Òthe manipulation and use of representations is the primary job of the mindÓ

(Dretske 1995, p. xiv).

Without breaking faith with intellectualism, Conservative Embodied/Enactive

Cognition, or CEC, still allows one to recognize Òthe profound contributions that

embodiment and embedding makeÓ (Clark 2008b, p. 45). For those who endorse only

CEC and not REC the new developments in cognitive science, far from posing a threat to

the existing paradigm, can be seen as supplying new tools or Ôwelcome accessoriesÕ of

considerable potential value that could augment intellectualist accounts of the mind.

CEC-style thinking is best exemplified by a recent bid to save the representationalist

baby from the embodied bathwater, by arguing for the existence of action-oriented

representations, or AORs. According to Wheeler (2008), who has done more than most to

promote this view, an action-oriented representation is one that is:

(i) action-specific (tailored to a particular behaviour and designed to represent the

world in terms of specifications for possible actions);




20

(ii) egocentric (features bearer-relative content as epitomized by spatial maps in

an egocentric co-ordinate system);

(iii) intrinsically context-dependent (the explicit representation of context is

eschewed in favour of situated special-purpose adaptive couplings that

implicitly define the context of activity in their basic operating principles) (see

also Wheeler 2005, p. 199).

Believing in AORs is consistent with accepting the neural assumption Ð an

assumption that pays homage to the intuition that neural states and processes have a

special cognitive status. Those attracted to this assumption believe it should be respected

because, even though non-neural factors can qualify as representational vehicles, as it

turns out, in most cases they do not. As such, the great majority cognitive explanations

only ever involve representations that are wholly brain-bound. This is so even in cases in

which it is necessary to making appeal to extra-neural but non-representational causal

factors in order to explain the particular way that some particular intelligent activity

unfolds. By accepting this last caveat, defenders of CEC allow that the full explanation of

a given bout of intelligent behaviour need not be strongly instructional in character in the

way demanded by the standard ploy.

Wheeler (2005) highlights the core features of CEC-style thinking, illustrating the role

of AORs by appeal to the architecture of a simple behavior-based robot created by

Francheschini, Pichon and Blanes (1992).




21

The robot has a primary visual system made up of a layer of elementary motion

detectors (EMDs). Since these components are sensitive only to movement, the

primary visual system is blind at rest. What happens, however, is that the EMD layer

uses relative motion information, generated by the robotÕs own bodily motion during

the previous movement in the sequence to build a temporary snap map of the detected

obstacles, constructed using an egocentric coordinate system. Then, in an equally

temporary motor map, information concerning the angular bearing of those detected

obstacles is fused with information concerning the angular bearing of the light source

(supplied by a supplementary visual system) and a directional heading for the next

movement is generated (Wheeler 2005, p. 196, first, second, fifth and sixth emphases

added).

Wheeler (2005) considers and dismisses a number of possible minimal criteria for

being an AOR Ð including appeal to selectionist strategies and decoupleability. After

careful review, he settles on the idea that what is necessary and sufficient to distinguish

behaviour-based systems that operate with AORs from those that do not is that the former

systems exhibit arbitrariness and homuncularity. A system exhibits arbitrariness just

when the equivalence class of different inner elements is fixed Òby their capacity, when

organized and exploited in the right ways, to carry specific items of information or bodies

of information about the worldÓ (p. 218). A system is a homuncular just when (a) it can

be compartmentalized into a set of hierarchically organized communicating modules, and

(b) each of those modules performs a well-defined sub-task that contributes towards the

collective achievement of the overall adaptive solution.




22

For Wheeler, the linchpin holding this account of AORs together is that some

cognitive systems are information processing systems. Thus:

The connection between our two architectural features becomes clear once one learns

that, in a homuncular analysis, the communicating sub-systems are conceptualized as

trafficking in the information that the inner vehicles carry. So certain subsystems are

interpreted as producing information that is then consumed downstream by other

subsystems (p. 218, emphases added).

We can legitimately describe a cognitive system as employing AORs just in case it is

a genuine source of adaptive richness and flexibility and it turns out that its subsystems

use Òinformation-bearing elements to stand in for worldly states of affairs in their

communicative dealingsÓ (Wheeler 2005, p. 219). Satisfaction of the above conditions is

all that is required for the existence of weak or minimal representations. In the end, all of

the weight in this account is placed on the idea that it suffices for minimal representations

to be present in a system, S, if it manipulates and makes use of informational content in

well-defined ways.

This minimal notion of representation is, no doubt, attractive to cognitive scientists.

For anyone in the field it is utterly textbook to be told that information is a kind of basic

commodity Ð the raw material of cognition. After all, Òminds are basically processors of

information; cognitive devices [for] receiving, storing, retrieving, modifying and

transmitting information of various kindsÓ (Branquinho 2001, xii-xiii).

There is great latitude for thinking about the processes that enable this. Thus:




23

Mental representations might come in a wide variety of forms; there being no

commitment in the claim itself to a specific kind of representation or to a particular

sort of representational vehicleÉmental representations might be thought of as

images, schemas, symbols, models, icons, sentences, maps and so on (Branquinho

2001, xiv).

Accordingly, representations or representational vehicles Òare items in the mind or

brain of a given system that in some sense ÔmirrorÕ, or are mapped onto, other items or

sets of items É in the worldÓ (Branquinho 2001, xiv). But what makes something into a

vehicle, the essence of representing, is that they bear or possess content. Content is key.

Thus:

The whole thrust of cognitive science is that there are sub-personal contents and sub-

personal operations that are truly cognitive in the sense that these operations can be

properly explained only in terms of these contents (Seager 1999, p. 27, emphasis

added).

Dietrich and Markman (2003) define representations as Òany internal state that

mediates or plays a mediating role between a systemÕs inputs and outputs in virtue of that

stateÕs semantic content. We define semantic content in terms of information causally

responsible for the state, and in terms of the use to which that information is putÓ (p. 97,




25

favours CEC must face up to the hard problem of content Ð and I suggest that REC is a

small price to pay to allow one to avoid that problem.

Before turning to that issue, it is worth highlighting an immediate concern about CEC,

and its reliance on AORs. Appeal to AORs seems to secure the fate of minimal

representations Ð winning a key metaphysical battle Ð only at the cost of losing a wider

explanatory war. For, on the assumption that the AORs need not be decoupled in order to

qualify as representations Ð an assumption Wheeler explicitly defends (2005, 2008), and

with good reason (see Chemero 2009, ch. 3), defenders of CEC face the charge that Òtalk

of representations in coupled systems may be too cheap, or too arbitrary, and thus adds

little or nothing to an explanation of how these systems workÓ (Shapiro 2011, p. 147).

Chemero (2009) too voices this exact worry, noting that:

the representational description of the system does not add much to our understanding

of the system É [thus] despite the fact that one can cook up a representational story

once one has the dynamical explanation, the representational gloss does not predict

anything about the systemÕs behaviour that could not be predicted by dynamical

explanation alone (p. 77).

Although initially cast as a purely explanatory concern it is clear that this issue cannot

be kept wholly free of metaphysical considerations. For instance, Chemero goes on to

note that Òin terms of the physics of the situation, the ball, the outfielder, and the

intervening medium are just one connected thing. In effective tracking, that is, the

outfielder, the ball, and the light reflected from the ball form a single coupled system. No




26

explanatory purchase is gained by invoking representation here: in effective tracking, any

internal parts of the agent that one might call mental representations are causally coupled

with their targetsÓ (Chemero 2009, p. 114).

Part of the trouble here is that there does not appear to be any clean cut way to decide,

with precision, which systems actually satisfy the relevant conditions for being minimally

representational systems. For example, there are diverging opinions about whether WattÕs

much discussed centrifugal governor Ð a device originally designed to ensure a constant

operating speed in rotative steam engines Ð qualifies as a representational device. This is

despite the fact that the relevant parties in the debate are fully agreed about the

characteristics of the governorÕs internal design, which are quite elegant and simple. The

positions of the deviceÕs spindle arms interact with and modifies the state of a valve

which controls the engineÕs speed Ð when the arm is high the valve slows the engine,

when the arm is low the valve increases engine speed.

In line with the criteria laid out in the previous section, Chemero (2009) concludes

that:

It is possible to view the governorÕs arms as [noncomputational] representations É

It is the function of particular arm angles to change the state of the valve (the

representation consumer) and so adapt it to the need to speed up or slow down. The

governor was so designed É to play that role É it is both a map and a controller . It is

an action oriented representation, standing for the current need to increase or

decrease speed (p. 71, emphases added).5




28

Put as starkly as this, one might be forgiven for failing to see what changes so

dramatically when it comes to the operation of thermostats as opposed to Watt governors.

A thermostat regulates the systemÕs temperature, maintaining it at a desired point. Its

mechanisms exploit the properties of the bimetallic strip that Ð when all is well Ð

responds in reliable ways to temperature changes, bending one way if heated and the

opposite way if cooled.

The important difference between the two types of systems is not that there are more

mechanisms or steps involved in regulating temperature by this means. Rather, the crucial

distinction is meant to be that the bimetallic strips in thermostats have the systemic

function of indicating specific desired temperatures to other subsystems that use those

indications to regulate their behaviour. It is because they function in this special way that

devices of this general type are representational Ð they exploit pre-existing indication

relations giving them the function to indicate how things stand externally and use those

indications in particular ways.6 Following Dretske (1995) if such devices were naturally

occurring then they would Òhave a content, something they say or mean, that does not

depend on the existence of our purposes and intentions É [They would] have original

intentionality, something they represent, say, or mean, that they do not get from usÓ (p. 8,

emphasis added).

To qualify as representational, an inner state must play a special kind of role in a

larger cognitive economy. Crudely, it must, so to speak, have the function of saying or

indicating that things stand thus and so, and to be consumed by other systems because it

says or indicates in that way. Only then will an internal state or structure meet RamseyÕs

(2007) job description challenge.




29

It is plausible that many of the states (or ensembles of states) of systems that enable

basic cognition are merely (1) reliably caused by (or nomically depend upon) the

occurrence of certain external features, and (2) disposed to produce certain effects (under

specific conditions), and (3) have been selected because of their propensities for (1) and

(2). Yet states or structures that only possess properties 1-3 fail to meet the job

description challenge. They fail to qualify as truly representational mental states having

the proper function of saying Ôthings stand thus and soÕ, rather they Ð like the Watt

governor Ð only have the proper function of guiding a systemÕs responses with respect to

specific kinds of worldly offerings.

Exactly, what else is required to be a representation-using system? Wheeler (2005)

speaks of the need for communicative transactions between homuncular subsystems. This

informational dealing is the basis of true cognition Ð nonetheless, he stresses that this

does not imply that the sub-systems Òin any literal sense understand that informationÓ (p.

218).

Fair enough, but even if they literally lack understanding, it might be thought that at

least such subsystems must be literally trading in informational content Ð using and

fusing it Ð even if they donÕt understand what it says. But talk of using and fusing

contents, although quite common, cannot be taken literally either. For it is not as if

informational content is a kind of commodity that gets moved about and modified in

various ways; information is not Òlike a parcel in the mailÓ (Shapiro 2011, p. 35).

This being so it seems that bona fide cognitive systems are not special because they

literally use and manipulate informational content (not even content that they donÕt




30

understand). They are special, because it is their function to convey informational content

without actually manipulating it as such.

We are now getting down to brass tacks. For this story to work Ð there must at least be

content that these subsystems have the special function to convey Ð there must be

something that it is their function to say even if they donÕt understand what they are

saying or what is said. But exactly what is informational content?

Dretske (1981) speaks about informational content as Òthe what-it-is-we-can-learn

from a signal or message in contrast to how-much-we-can-learnÓ (p. 47). He makes clear

that he understands a signalÕs informational content to be a kind of propositional content

of the de re variety. Propositions or propositional contents have special properties Ð

minimally, they are bearers of truth. Assuming that informational contents are

propositional is presumably what allows Dretske to hold that when signals carry

information to the senses they tell Òus truly about another state of affairsÓ (p. 44).

This is, of course, quite compatible with holding that informational content lacks

fully-fledged representational properties. Thus one can hold that informational content is

supplied by the senses, which is not representational content, and that more is required

for informational content to be properly representational.

It is at this juncture that defenders of AORs and CEC Ð at least those who subscribe to

an explanatory naturalism Ð face a dilemma. Since so much hangs on this it is worth

going very slowly over some familiar ground. In the opening passage of DretskeÕs

Knowledge and the Flow of Information (1981) we find the locus classicus and

foundational statement on how to understand information processing systems in a way




31

that is both required by the CEC story and that expresses a commitment to explanatory

naturalism.

In the beginning there was information. The word came later ... information (though

not meaning) [is] an objective commodity, something whose generation, transmission

and reception do not require or in any way presuppose interpretative processes. One is

therefore given a framework for understanding how meaning can evolve, how genuine

cognitive systems Ð those with the resources for interpreting signals, holding beliefs,

acquiring knowledge Ð can develop out of lower-order, purely physical, information-

processing mechanisms ... Meaning, and the constellation of mental attitudes that

exhibit it, are manufactured products. The raw material is information (p. vii,

emphases added).

Any explanatory version of naturalism seeks to satisfy what Wheeler (2005)

charmingly calls the Muggle constraint: ÒOneÕs explanation of some phenomenon meets

the Muggle constraint just when it appeals only to entities, states and processes that are

wholly nonmagical in character. In other words, no spooky stuffÓ (p. 5).

It is widely supposed that the informational theory of content comfortably meet this

constraint. At least, its defenders have attempted to convince us that, when promoting it,

there is nothing up their sleeves. This is because, as Jacob (1997) emphasizes:

the relevant notion of information at stake in informational semantics is the notion

involved in many areas of scientific investigation as when it is said that a footprint or a




32

fingerprint carries information about the individual whose footprint or fingerprint it is.

In this sense, it may also be said that a fossil carries information about a past

organism. The number of tree rings in a tree trunk carries information about the age of

the tree (p. 45, emphasis added).

This picks out the relevant notion by means of examples. We can call it the notion of

information-as-covariance. Although theorists quibble about the strength and scope of the

degree of covariance required in order for informational relations to exist, there is

consensus that sÕs being F Ôcarries information aboutÕ tÕs being H iff the occurrence of

these states of affairs lawfully, or reliably enough, covary.

But hereÕs the rub. Anything that deserves to be called content has special properties Ð

e.g. truth, reference, implication Ð that make it logically distinct from and irreducible to

mere covariance relations holding between states of affairs. While the latter notion is

surely scientifically respectable, it isnÕt able to do the required work of explaining

content. Put otherwise, if information is nothing but covariance then it is not any kind of

content Ð at least not if content of the sort defined in terms of its truth bearing properties.

The number of a treeÕs rings can covary with its age; this does not entail that the first

state of affairs says or conveys anything true about the second, nor vice versa. The same

goes for states that happen to be inside agents and which reliably correspond with

external states of affairs Ð these too, in and of themselves, do not ÔsayÕ or ÔmeanÕ

anything in virtue of instantiating covariance relations. Quite generally, covariation in

and of itself neither suffices for, nor otherwise constitutes content, where content




33

minimally requires the existence of truth bearing properties. Call this the Covariance

doesnÕt Constitute/Confer Content (or CCC) principle.

The CCC undermines the assumption that covariation is the worldly source of

informational content. There is no doubt the idea of information-as-covariance is widely

used in sciences; hence, it is not a hostage to fortune for explanatory naturalists. But if the

CCC is true, there is a gaping explanatory hole in the official story propounded by those

who follow DretskeÕs lead. Anyone peddling such an account is surely violating the

Muggle constraint and ought to be brought to the attention of the Ministry of Magic.7

One might opt for the first horn of this dilemma and retain the scientifically

respectable notion of information-as-covariance, and thus retain oneÕs naturalistic

credentials while relinquishing the idea there is such a thing as informational content.

That is the path I recommend, but it requires giving up on CEC since Ð as argued in the

previous section Ð the minimal requirement for distinguishing informational processing

systems is that they make use of AORs which are defined as content-bearing vehicles.

But the distinction between vehicles and contents falls apart, at least at the relevant level,

if there are no informational contents to bear.

To avoid this one might opt to be impaled on the second horn. This would be to accept

that contentful properties exist even if they donÕt reduce to, or cannot be explained in

terms of, covariance relations. If contentful properties and covariance properties are

logically distinct they might still be systematically related. Hence, it might be hoped that

contentful properties can be naturalistically explained by some other means (e.g. by some

future physics). Alternatively, they could be posited as explanatory primitives Ð as

metaphysical extras that might be externally related to covariance properties. Thus they




34

might have the status that ChalmerÕs (2010) still assigns to qualia Ð they might require us

to expand our understanding of the scope of the natural. Contentful properties might pick

out properties that Ð like phenomenal properties Ð are irreducible to and exist alongside

basic physical properties. If so, the explanatory project of naturalism with respect to

them would look quite different Ð it would be to discover the set of fundamental bridging

laws that explain how contentful properties relate to basic physical properties. That

would be the only way to solve what we might call the hard problem of content.

Of course, one might try to avoid both horns by demonstrating the falsity of the CCC

by showing how contentful properties Ð e.g. truth bearing properties Ð reduce to

covariance properties (Good luck with that!). A more plausible dilemma-avoiding move

would be to show that the notion of information that is in play in these accounts is, in

fact, meatier than covariance but is nonetheless equally naturalistically respectable.

After all, Dretske talks of indication relations not covariance relations, though the two

are often confused. Tellingly, in continuing the passage cited above Jacob (1997) remarks

that ÒIn all of these cases, it is not unreasonable to assume that the informational relation

holds between an indicator and what it indicates (or a source) independently of the

presence of an agent with propositional attitudesÓ (p. 45, emphasis added). In making this

last point, he stresses that Òthe information or indication relation is going to be a relation

between states or factsÓ (p. 49-50).

However, following Grice, Dretske is wont to think of indication as natural meaning Ð

as in ÔsmokeÕ means ÔfireÕ. But smoke means fire only if it indicates fire to someone. It

makes no sense to talk of indication in the absence of a user. Indication is, at least, a

three-place relation whereas covariance by contrast, is a two-place relation. To think of




35

indication as the basis for informational semantics therefore is already to tacitly assume

that there is more going on than mere covaration between states of affairs.8 This raises

questions about how exactly the notion of information-as-indication relates to its

scientifically respectable cousin, the notion of information-as-covariance. Moreover, we

might wonder if this notion has independent naturalistic credentials of its own. Until

these questions are answered promoters of AORs and CEC Ð those that rely on the

existence of informational content to distinguish genuine cognitive systems from all

others Ð havenÕt really got off the starting blocks with their theorizing.

5. Epilogue: Decoding Information

The ÔcodeÕ metaphor is rife in the cognitive sciences but the cost of taking it seriously is

that one must face up to the Hard Problem of Content! In light of the problems with

CEC-style stories highlighted above, we have reason to think that on-line sensory signals

Ôcarry informationÕ (in one sense) but not that they Ôpass onÕ meaningful or contentful

messages Ð contentful information that is used and fused to form inner representations.

Unless we assume that pre-existing contents exist to be received through sensory contact

the last thread of the analogy between basic cognitive systems and genuinely

communicating systems breaks down at a crucial point.

In line with REC, there are alternative ways to understand cognitive activity as

involving a complex series of systematic Ð but not contentfully mediated Ð interactions

between well-tuned mechanisms (see, e.g., Hutto 2011a, 2011b; Hutto and Myin in

preparation). RECers press for an understanding of basic mentality as literally constituted




36

by, and to be understood in terms of, concrete patterns of environmentally situated

organismic activity, nothing more or less. If they succeed, the above arguments should

encourage more cautious CEC types Ð those trying to occupy the mid-left Ð to take a walk

on this wild side.9

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Brooks, R. (1991a). ÒNew Approaches to Robotics.Ó Science 253: 1227-1232.

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Chalmers, D. (2010) The Character of Consciousness. Oxford, Oxford University Press.

Chemero, A. (2009). Radical Embodied Cognitive Science. Cambridge, MA, MIT Press.

Clark, A. (2002). ÒSkills, Spills and the Nature of Mindful Action.Ó Phenomenology and

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Clark, A. (2008a). Supersizing the Mind: Embodiment, Action, and Cognitive Extension

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Clark, A. (2008b). ÒPressing the Flesh: A Tension in the Study of the Embodied,

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Dietrich, E. and A. Markman (2003). ÒDiscrete Thoughts: Why Cognition Must Use

Discrete Representations.Ó Mind and Language 18: 95-119.




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1 This intellectualist way of understanding the basic nature of minds taps into a

long tradition stretching back at least as far as Plato; it was revived by Descartes

in the modern era, and regained ascendency, most recently, through the work of

Chomsky during the most recent cognitive revolution. As No‘ (2009) observes:




40

ÒWhat these views have in common Ð and what they have bequeathed to cognitive

science Ð is the idea that we are, in our truest nature, thinkers. It is this

intellectualist background that shapes the way cognitive scientists think about

human beingsÓ (p. 98).

2 There are clear connections that might be forged between this view and DonaldÕs

(1999) claim that when it comes to understanding human cognition Òthe most

critical element is a capacity for deliberately reviewing self-actions so as to

experiment with them É It would be no exaggeration to say that this capacity is

uniquely human, and forms the background for the whole of human culture,

including languageÓ (p. 142).

3 It is perhaps understandable that in seeking to make sense of this cognitive

activity we are naturally inclined to assume the existence of representations that

Òinclude not only ÔcommandsÕ and ÔcalculationsÕ, but also Ôif-thenÕ and other

logical operations. This shows how it seems impossible to make sense of cerebral

control Ð requisition and modification Ð of motor programs, to describe them in

such a way that they deliver what is needed while avoiding anthropomorphismsÓ

(Tallis 2003, p. 65). The problem is that Òattributing to the brain, or parts of it, or

neural circuits, the ability to do things that we, whole human beings, most

certainly cannot do, seems unlikely to solve the puzzleÓ (Tallis 2003, p. 65)




41

4 At a pinch, one could give a short list of these, which could include: Ògrasping,

seizing, pulling, plucking, picking, pinching, pressing, patting, poking, prodding,

fumbling, squeezing, crushing, throttling, punching, rubbing, scratching, groping,

stroking, caressing, fingering, drumming, shaping, lifting, flicking, catching,

throwing, and much besidesÓ (Tallis 2003, p. 22).

5 Notably, Chemero holds that the centrifugal governor is not a computer even

though it can be regarded as a representational device and in the respect he does

not break faith with the conclusion of Van GelderÕs original analysis when he first

introduced the example into the literature (Van Gelder 1995).

6 Thus ÒIf we suppose that, through selection, an internal indicator acquired a

biological function, the function to indicate something about the animalÕs

surroundings, then we can say that this internal structure representsÓ (Dretske

1988, p. 94).

7 To make vivid what is at stake it is worth noting that early analytic philosophers

were at home with the view that the world is ultimately and literally composed, at

least in part, by ÔpropositionsÕ. These were conceived of as bedrock Platonic

entities Ð mentionable ÔtermsÕ which, when standing in the right complex

relations, constitute judgeable objects of thought. In commenting on RussellÕs

version of this idea, Makin underscores the features that parallel many of the

properties that Dretske demands of informational content. He stresses that: Òwith




propositions, it is crucial to bear in mind that they are not, nor are they abstracted

from, symbolic or linguistic or psychological entities É On the contrary, they are

conceived as fundamentally independent of both language and mind . Propositions

are first and foremost the entities that enter into logical relations of implication,

and hence also the primary bearers of truth ... Ôtruth'Õ and 'implication' apply, in

their primary senses, to propositions and only derivatively to the sentences

expressing themÓ (Makin 2000, p. 11, emphasis added).

8 Others too have noticed this. For example, Ramsey (2007) comments on the

peculiar features of the quasi-semantic indication relation as follows: ÒDretske

and many authors are somewhat unclear on the nature of this relation. While it is

fairly clear what it means to say that state A nomically depends upon state B, it is

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