towards a conceptual model of procedural knowledge degradation

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Towards a conceptualmodel of proceduralknowledge degradationNathan G. Brannon & Richard J. KoubekPublished online: 26 Nov 2010.

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Towards a conceptual model of procedural knowledge degradation

NATHAN G. BRANNON{* and RICHARD J. KOUBEK{

{ Sandia National Laboratories, PO Box 5800 MS 0830, Albuquerque, NM 87185-0830, USA{ Pennsylvania State University, Pennsylvania, USA

Keywords: Cognitive modelling; Procedural knowledge; Knowledge degradation.

Research has derived a dynamic range of theories associated with the acquisitionof knowledge. In contrast, the current research seeks mechanisms by whichacquired knowledge degrades, as well as mechanisms that could be computation-ally modelled. For procedural tasks, such as product assembly or supervisorycontrol, variables including interference can inhibit the ability to retrieve proce-dural knowledge in di� erent ways than the retrieval of declarative knowledge likephone numbers. High consequence tasks, such as air tra� c control or mainten-ance on nuclear weapons, require pro®ciency to be maintained. Thus, an under-standing of mechanisms that might complicate e� ective performance couldimprove approaches to job design. To explore theoretical underpinnings for pro-cedural knowledge degradation, three areas of literature were explored. The areasincluded theoretical, computational, and ecological research. The literature servesas input for a conceptual model of procedural knowledge degradation.

1. IntroductionIn Operation Desert Storm, reserve units were called upon to support the mission inthe Middle East. Concerns were raised that the skills provided by these units haddeteriorated or decayed (Wisher et al. 1991). While the skills had not been forgotten,what caused this knowledge to degrade? This issue is not unlike those associated withmanufacturing environments where workers rotate through various tasks and retrie-val of the skill they previously demonstrated pro®ciency in has become cumbersomeor less e� cient. While factors like time, age or environmental factors (e.g. heat orlighting) might interfere with retrieval, what cognitive mechanisms exist that maycontribute to the degradation of knowledge?

Research literature addressing knowledge degradation is limited in comparisonto that available for knowledge acquisition. Most research that approaches the topicof knowledge degradation typically explores the area with the immediate interest ofimproving knowledge acquisition (Kyllonen and Tirre 1988, Ram et al. 1995). Thisresearch is pursuing mechanisms that amplify knowledge degradation. An antici-pated outcome of this paradigm is an enhanced ability to understand and controlknowledge degradation.

While the degradation of knowledge can have negative outcomes such as errorsor a general reduction in performance, there are positive outcomes as well. Forexample, companies introducing new or revised products may want to foster useracceptance by degrading the knowledge of older or competing systems. Militaryapplications could include the provision of information to enemy forces that

THEOR. ISSUES IN ERGON. SCI., 2001, VOL. 2, NO. 4, 317±335

Theoretical Issues in Ergonomics Science ISSN 1463±922X print/ISSN 1464±536X online # 2001 Taylor & Francis Ltdhttp://www.tandf.co.uk/journals

DOI 10.1080/1463922011010837 2

* Author for correspondence. e-mail: [email protected]

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corrupts their performance, thereby increasing the probability and magnitude ofsupremacy. The role and in¯uence of information is a signi®cant issue with respectto national security (USAF Scienti®c Advisory Board 1995).

The literature review is organized into three areas including the theoretical per-spective, the computational perspective, and ®nally the ecological perspective (see®gure 1). Each area of research provides useful elements in exploring the topic ofknowledge degradation. Ecological research provides data from task analyses andcase studies that highlight the outcomes of knowledge degradation. Computationalresearch brings much-needed tools to model cognition in a uni®ed architecture.Finally, mechanisms derived from psychological theories provide the neededmechanisms that in¯uence the state of retained knowledge.

The literature review also depicts missing elements associated with each perspec-tive. However, as shown in ®gure 1, the integration of the three areas of researchenables each provision to cover weaknesses in other areas. The result is an integratedobjective of deriving a conceptual model of procedural knowledge degradation thatcan be simulated in computational models of real-world tasks.

2. Background2.1. Theoretical/experimental perspectiveMemory has been described as having three operationally distinct phases: acquisi-tion, storage, and retrieval (Melton (1963) originally refers to the phases as traceformation, trace storage, and trace utilization). In attempting to draw concise rela-

318 N. G. Brannon and R. J. Koubek

OBJECTIVE

Derive a conceptual model of procedural knowledge degradation that can be simulated in computational models of real-world tasks.

PROVISIONS

MISSING ELEMENTS

Knowledge Degradation Mechanisms

Theoretical

Perspective

· Procedural Data for Validation

· Computational Tools

Procedural Knowledge

Degradation Data

Ecological

Perspective

· Computational Tools · Predictive

Mechanisms

Knowledge Degradation

Modeling Tools

Computational

Perspective

· Procedural Data for Validation

· Predictive Mechanisms

Figure 1. Conceptual overview of related research.

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tionships between the vast ®eld of memory research and the focal topic of knowledgedegradation, the phases of memory were determined to be a useful means of orga-nizing the discussion as they each have a role to play in knowledge degradation.

2.1.1. Knowledge acquisition: Arguments have been made that the likelihoodknowledge will be lost is related to the e� ectiveness of knowledge acquisition(Fleishman and Parker 1962, Mensink and Raaijmakers 1988, Rose 1989).Koubek et al. (1999) provide not only a comprehensive review of knowledge ac-quisition concepts, but an integration of these theories as well. The model pro-posed by Koubek et al. (1999) includes several variables drawn from otherresearch (see table 1).

While the model proposed by Koubek et al. (1999) incorporates many relevantvariables to knowledge acquisition, the role of these variables is not discussed withregards to the loss of knowledge. Can the variables be used in an equal and oppositemanner to represent knowledge degradation? Some mechanisms may be useful, butthe degree with which the variables explain the loss of knowledge is uncertain.

Mensink and Raaijmakers (1988) proposed a memory model that predicts theloss of knowledge in relation to many variables, including the number of encodingtrials. The model was compared to several classic theories associated with memorystorage and retrieval. Although several mechanisms of knowledge degradation wereexamined, the mechanisms are rooted in descriptive factors such as time. Prescriptiveexplanations and de®nitions for interference mechanisms were not found.

The knowledge that is acquired and stored in memory can take many forms. Theknowledge associated with riding a bike is arguably di� erent than a bank accountnumber. Tulving (1972) addressed the question, `What exactly do we mean bymemory?’ Tulving attempted to reduce the ambiguity associated with the termmemory by proposing a distinction between knowledge associated with experiencesor episodes (episodic memory) versus knowledge associated with meaning or de®ni-tions (semantic memory). Tulving felt that di� erent laws of operation govern episo-dic and semantic memories.

Anderson (1976) agreed that there are di� erent laws of operation that governmemory and proposed a distinction between declarative and procedural memory.Declarative knowledge is associated with facts such as a home phone number or thedetails of an auto accident. Therefore, declarative knowledge includes episodic andsemantic knowledge (Halpern 1996). Procedural memory involves the use of declara-tive knowledge for tasks such as programming. Anderson and Lebiere (1998) providea current and in-depth description of the role of declarative knowledge in the com-pilation of procedural (i.e. `how to’) knowledge.

Conceptual model of procedural knowledge degradation 319

Table 1. Factors included in the knowledge acquisition framework (Koubeck et al. 1999).

Learning factors Sources

Levels of learning GagneÂ (1985)Knowledge structure at di� erent skill level Koubek and Salvendy (1991)Level and types of abilities required Fleishman and quaintance (1984)Learning in terms of production rules Anderson (1982)Cognitive resources Wickens (1992)Automatization of task components Shi� rin and Schnewider (1977)

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The work by Tulving (1972) and Anderson (1976) relate to each other in asigni®cant way with respect to knowledge degradation. Tulving (1972) not onlyproposed a distinction between episodic and semantic memory, but also proposedthat they, `. . . di� er in their susceptibility to transformation and loss of storedinformation’ (p. 391). In a similar sense, Anderson’s research suggests that declara-tive and procedural knowledge di� er in their susceptibility to the loss of information.

2.1.2. The dynamics of memory storage: In storage, memory loss has been attrib-uted to the passage of time and is referred to as decay (Conrad 1957, Brown1958). Wickelgren (1976) attempted to isolate the storage/retention phase as therelevant phase in memory research. Wickelgren (1976) not only provides a sum-mary of decay theories, but a review of interference theories as well. An appealingcharacteristic of the time-based decay hypothesis is the accuracy of retention func-tions. Time-based mathematical models of forgetting have used decay for over 100years (Rubin and Wenzel 1996). Rubin and Wenzel (1996) examined 210 data setsthat conform to Jost’s Second Law, which states that the rate of forgetting conti-nually decreases with increasing time since learning. The most common function,adopted by researchers such as Anderson and Lebiere (1998), is the power func-tion (Wixted and Ebbesen 1991, 1997). The power functions used by researcherssuch as Wixted and Ebbesen typically account for over 95% of the variance.

Anderson and Tweney (1997) argued that an exponential function was moreappropriate. However, Wixted and Ebbesen (1997) quickly responded to criticismby validating their original research. Furthermore, McBride and Dosher’s (1997)examination of memory loss concurred with Wixted and Ebbesen’s power function.

E� orts have been made to ®nd interference e� ects outside the laboratory, such asbefore or after experimentation (Underwood and Postman 1960). It is believed that alarge amount of forgetting occurs outside the laboratory and, if this could be empiri-cally con®rmed, then the results would provide signi®cant support for interferencetheories. However, as reviewed by Keppel (1968), signi®cant results were not founddespite extensive e� orts. As a result, e� orts to support interference theories haveindirectly provided support for a more parsimonious explanation using time-baseddecay.

2.1.3. Retrieval: In a survey of psychologists studying learning and memory,most of the participants indicated a belief that retrieval failures accounted formost cases of forgetting (Loftus and Loftus 1980). As noted earlier, Wickelgren(1976) argued that the loss of memory should be examined in the storage/retentionphase. However, researchers such as Anderson and Neely (1996) support the sur-vey results from Loftus and Loftus (1980), shifting the focus of psychological re-search to retrieval factors.

The shift toward the retrieval phase has been accompanied by an agreement thatdecay theories only provide a descriptive rather than a prescriptive explanation forthe loss of knowledge (Gazzaniga et al. 1998). A ®tting analogy has been drawn thatdecay theory is analogous to attributing the rusting of metal to the passage of time(Nairne 1996). Just as oxidation provides a deeper explanation of rust, interference isa more explanatory mechanism of knowledge degradation. Nevertheless, the factremains that until researchers can purely pause cognitive processing, it is hard todiscount interference in favour of decay (Houston 1991).


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2.1.4. Classical interference research: The classical period of interference researchhas been proposed to be the period prior to Tulving’s (1972) paper, which pro-posed the distinction between episodic and semantic memory (Anderson andNeely 1996). A common research approach in the classical era included paired as-sociates for verbal learning studies.

As the term implies, a paired associate refers to the cognitive associationsbetween pairs of information. The pairs contain a stimulus and a response (e.g.dog (stimulus)±cat (response)) . Paired associate researchers typically abbreviatethe information in describing the research approach, such as A±B representingDog±Cat for example. Often, subjects would be asked to memorize lists of pairedassociates at varying lengths (e.g. A±B list: Dog±Cat, Bark±Tree, Bag±Tomato, etc.)and then given a second list to memorize using the same stimulus with a di� erentresponse (e.g. A±C list: Dog±Walk, Bark±Floor, Bag±Paper, etc.). This approachwould be called an A±B, A±C research protocol. Researchers can manipulate thepairs in lists provided to subjects in many ways such as the A±B, C±D paradigm thatrequires subjects to memorize two unique lists of paired associates.

Following learning, subjects were tested on their memory of the lists in threeprimary ways. First, subjects might be given the stimulus and asked to give the ®rstresponse that comes to mind. For example, in the A±B, A±C paradigm, the subjectmight be given Dog and asked for one response. Experimenters were interested inknowing from which list the response would come. This testing technique is knownas modi®ed free-recall (MFR). A variation of this test asks subjects to recall allresponses associated with the stimulus, so for stimulus Dog, the subject wouldreply Cat, and Walk. This test format is called modi®ed free-recall (MMFR).Finally, subjects may be provided a list of paired associates and asked to distinguishlearned associations from unlearned associations. This test format is typicallyreferred to as associative recognition. The possible testing procedures coupled withthe research protocols generated a wealth of experimental variations.

2.1.5. A review of negative transfer: As noted earlier, the A±B, A±C approachwas e� ective for retroactive interference and it was e� ective for negative transferas well (Singley and Anderson 1989). An even more e� ective technique for trigger-ing negative transfer was described (in paired associate terminology) as A±B, A±Br (`r’ representing `repaired’). This approach would take the initial task items andrearrange the same items, switching the stimulus response relationships. All stimuliwould remain stimuli and all responses would remain responses, except their pair-ings would be rearranged.

For example, an experiment by GagneÂ et al. (1950) involved colour of light as astimulus and pressing a button as a response. In what was called the `completereversal’ condition, the ®rst task had subjects learn the following relationship: redlight±right key; green light±left key. In the second task, the association involved: redlight±left key; green light±right key. The complete reversal condition resulted in thegreatest magnitude of negative transfer.

Instead of a perceptual-motor task, Porter and Duncan (1953) used two-syllableadjective associations. A±B, A±C pairs were arranged as well as A±B, A±Br pairs.Although negative transfer was detected for A±B, A±C pairs, a greater magnitude ofnegative transfer was found with respect to A±B, A±Br pairs.

Polson and Kieras (1985) and Polson et al. (1987) examined proceduralknowledge transfer associated with text editing. The main focus was looking at


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the relationship between transfer and the number of production rules required tolearn. Although negative transfer was not explicitly examined, Polson and Kierasnoted that transfer was reduced with inconsistency across methods.

Singley and Anderson (1989) reported their use of the A±B, A±Br approachcoupled with a text editing task that delivered the inconsistency mentioned byPolson and Kieras (1985). Participants switched text editors and, in particular, aswitch was made between EMACS and `perverse-EMACS’. Perverse-EMACSinvolved reversing text-editing procedures with the appropriate keystroke combina-tions (in A±B, A±Br style). The experimental design also allowed testing of an A±B,A±C type task to keystroke relationship. In this case, the same text editing pro-cedures required new keystroke combinations (i.e. not applied in the ®rst task). Inthe condition GagneÂ et al. (1950) called `complete reversal’, negative transfer wasdetected. Interestingly, the A±B, A±C condition resulted in signi®cant positive trans-fer. Singley and Anderson also had subjects return to the original text editor (testingretroactive interference) and none of the conditions exhibited retroactive interfer-ence. However, the subjects who experienced negative transfer continued to lagbehind other subjects in performance through the test trial.

Note that Singley and Anderson’s (1989) results di� ered from previous declara-tive memory research. Their results provided support for the contention that declara-tive and procedural knowledge di� er in their susceptibility to acquisition and loss.

2.1.6. Recent approaches to interference: Tulving’s (1972) article triggered an in-creasing amount of research associated with episodic memory. Lawyers beganusing results from a ®eld of research known as directed forgetting, which seeks thesuppression of prior information to avoid proactive interference. MacLeod (1998)provides a well-composed summary of this ®eld with respect to its history andscope. Juries could, for example, be literally instructed to forget information suchas testimonies so that those testimonies would not return in subsequent decision-making. Directed forgetting instructions are most e� ective when they follow im-mediately after the to-be-forgotten material and if new study material is encodedsoon after the instruction.

Although intentional forgetting research is consistent to a degree with this study,the theoretical underpinnings do not deviate from other literature already discussed,in that the loss of memory is due to decay and/or interference. Furthermore, the liststhat subjects are provided involve declarative knowledge as opposed to proceduralknowledge. Research in the area of directed forgetting does not seem to be pursuingprocedural knowledge in the near future (Bjork 1998).

Up to this point, much of the literature reviewed has examined declarativeknowledge. However, Campbell (1987) attempted to derive procedural operationsbehind interference associated with mathematical problems. Campbell (1987) did not®nd procedural operations governing mental multiplication. Campbell concludedthat fact retrieval was the dominant process, meaning that multiplication tablesare essentially large paired associate lists. Campbell’s later research has not includede� orts to derive productions. However, Campbell has continued to research factretrieval issues in mathematical related memory including transfer e� ects(Campbell and Clark 1989, Campbell and Tarling 1996) and interference(Campbell and Timm 2000).

Lovett and Anderson (1994) examined procedural retroactive interference associ-ated with geometry problem solving tasks. Drawing on the idea that standardized


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stimuli and diversi®ed responses yield the greatest level of retroactive interference(Osgood 1949), Lovett and Anderson (1994) examined diagrammatic similarityversus logical similarity. Consistent with Osgood (1949), results indicated that thegreatest level of retroactive interference occurred when participants solved a geome-try problem where the interfering task had the same diagram with di� erent logiccompared to the target task. Lovett and Anderson suggested that the diagram wascentral to problem solving memories and, in general, humans are more attentive tographical/diagrammatic characteristics of procedural tasks.

2.2. Computational perspectiveOne promising area of research where engineering tools may be derived is in com-putational models of human information processing (Laird et al. 1987, Just andCarpenter 1992, Kieras and Meyer 1997, Anderson and Lebiere 1998). Several pro-duction system architectures will be reviewed and are summarized in table 2.

Before going into detail concerning production systems, it should be noted thatseveral models of cognition exist outside of production system architectures.Examples include Meta Trouble Shooter (Meta-TS; Ram et al. 1995), and theOperator Function Model (OFM; Mitchell 1987). Tools such as the OFM andMeta-TS place more emphasis on actions than atomic levels of cognition. Mitchellet al. (1986), for example, note that the OFM is not intended to model the innerworkings of the mind. This study has adopted a production system approach that


Table 2. Production system architectures of cognition.

3CAPS EPIC SOAR ACT-R

Purpose Problem solving Simulate multitask Problem solving Problem solvingand language performance and and learning and learningcomprehension human information

processing

Theoretical Individual GOMS and the Problem space ACT (Andersonunderpinnings di� erences and Model Human operators (Newell 1976) and ACTand preceding capacity theory Processor (Card and Simon 1972) (Anderson 1982)framework (Just and et al. 1983) and OPS (Forgy

Carpenter 1992) 1995)

Learning Production ®ring in None Chunking Productionmechanisms connectionist compilation and

networks sub-symboliclearning

Degradation Displacement None None Decay andmechanisms competition

Knowledge Level of activation Reaction time Chunks Expected gain andmetrics strength

Validation Simulation vs. Simulation vs. Varied Varied,human data for human data (Rosenbloom et al. particularly inreading tasks measuring reaction 1993) mathematics(Haarman et al. time (Kieras et al. education1997). 1997) (Anderson and

Lebiere 1998)andprogramming(Anderson et al.1984)

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begins with a representation of cognitive processes and extrapolates the mechanismsto external tasks.

2.2.1. 3CAPS: Just and Carpenter (1992) introduced the production systemknown as 3CAPS which stands for Concurrent, Capacity-constrained , Activation-based, Production System. The use of the model is intended for modelling lan-guage comprehension and problem solving. A central feature of the model is therepresentation of individual di� erences in working memory capacity. The greaterworking memory capacity is, the greater the ability to comprehend language.Working memory in 3CAPS is organized in a connectionist network. Knowledgeis measured by the level of activation and production ®rings control the ¯ow ofactivation. Typical means of validating the model include reading tasks wherehuman data is compared with 3CAPS data (Just and Carpenter 1992, Haarmannet al. 1997).

With regards to knowledge degradation mechanisms, 3CAPS uses a conceptknown as displacement. Displacement means that if the level of activation exceedsworking memory capacity, knowledge is lost and information-processing speedsdecrease as well. The older the knowledge, the more likely it will be forgottenshould the level of activation exceed working memory capacity.

The use of displacement as a general mechanism of interference has experiencedcriticism (Nairne 1996). Nairne argues that, since working memory is not necessarily®xed, the displacement mechanism has `fallen out of favour’ (p. 77) with researchers.However, if the working memory capacity of an individual is accounted for (asperformed by Just and Carpenter (1992)), then displacement is a viable mechanismof interference.

2.2.2. EPIC: A tool developed for modelling human information processing isGOMS (Goals, Operators, Methods, and Selection Rules) (Card et al. 1983).GOMS provides a means of describing human performance through a sequence ofoperators (Eberts 1994). Operators are cognitive, perceptual, or motor acts thatchange the user’s mental state (Card et al. 1983). EPIC (Executive ProcessInteractive Control (Meyer and Kieras 1997a)) was built upon the GOMS frame-work.

EPIC possesses several useful features. From an applied perspective, an attractivecharacteristic of EPIC is the computational modelling of multiple-task performance(Meyer and Kieras 1997b). EPIC took an early lead in the e� ort to account for therelationship of sensory/perceptual processing and cognitive processing. Perceptualprocessors included in EPIC include: visual sensory, visual perceptual, auditoryperceptual, and tactile perceptual processors. EPIC also accounts for motor pro-cessing with manual motor, ocular motor and vocal motor processors. EPIC pri-marily measures knowledge using factors such as reaction time.

Unlike other production system models, EPIC does not provide mechanisms forlearning and degradation. However, other production systems such as ACT-R havebeen augmented with EPIC-like perceptual motor characteristics and one version isknown as ACT-R/PM (perceptual motor (Byrne 2000)). ACT-R/PM unites thehuman information processing strengths of EPIC with the learning mechanisms ofACT-R.


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2.2.3. SOAR: From their inception, tools such as ACT-R (Anderson 1982,Anderson and Lebiere 1998) and SOAR (State, Operator, and Result (Laird et al.1987, Newell 1990)) have included mechanisms for knowledge acquisition. SOARpossesses a re®ned structure enabling knowledge acquisition and e� cient informa-tion processing. All tasks are formulated in what is referred to as a `problemspace’ (Newell and Simon 1972). Elements of decision making, for example, caninclude current states and means by which a desired state is reached. Goals arestrictly generated through an automatic sub-goaling mechanism. The sub-goalingmechanism is a fundamental component of SOAR’s learning capability. SOARstores long-term memory simply as productions, rather than distinguishing de-clarative and procedural knowledge. Long-term memory supports problem solv-ing. For example, in decision-making, a production can be recalled from long-term memory that helps to quickly make a choice among alternatives.

SOAR primarily learns through chunking. The learning process begins whenSOAR is provided a task and chooses a problem space. Within the problem space,active operators can be chosen to change the current state to a desired state. If aunique activation cannot be derived, SOAR has reached an impasse. Sub-goals arethen automatically generated to resolve the impasse. If the impasse is still notresolved, more sub-goals are generated until the impasse is resolved. The resultsare permanently cached as productions (analogous to `chunking’ in human cogni-tion). These productions store the means by which the impasse was resolved.Therefore, when the same goal is set in the future, the stored production ®res withoutthe need for generating sub-goals. This results in more e� cient performance.

The re®ned structure of SOAR has enabled extensive applications for design andvalidation (Rosenbloom et al. 1993, Pew and Mavor 1998). Although the simpli®ednature is advantageous from an applied standpoint, this limits the scope of cognitivevariables that can be computationally modelled (Anderson 1993). Newell (1990) hasconceded this point stating ` . . . the assertion that chunking is a su� cient mechanismshould be considered a speculative and a priori unlikely hypothesis’ (p. 309).

As discussed earlier with regards to Koubek et al. (1999), there are several vari-ables associated with learning. In order to adequately model cognition, computa-tional models must explore multiple mechanisms of learning. A model that has madeprogress in this respect is ACT-R.

2.2.4. ACT-R: The basic structure of a production in ACT-R (Adaptive Controlof ThoughtÐRational) (Anderson 1993, Anderson and Lebiere 1998) consists ofthree stages; the goal condition, chunk retrieval, and goal transformation. Tofurther assist the reader in understanding ACT-R, a ¯ow diagram was developed(®gure 2). The three stages are depicted with their respective elements. Goals areorganized in a stack. The term `stack’ is appropriate due to the ®rst in, last out(FILO) organization of goals in ACT-R. The process initiates with a goal beingpushed (lower left-hand corner; `Start Cycle with Goal’). This tells ACT-R tofocus its attention on a speci®c goal. Once a goal is chosen, productions are de-rived based on matching the goal. If there are no productions that match the goal,the goal is `popped’ with failure. To pop a goal is to remove it from the focus ofACT-R’s attention. A result of `failure’ simply means ACT-R cannot currentlyachieve the goal and this typically results in the return to a higher-level goal thatset it.


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Normally a production is available and, if so, productions are ranked based ontheir expected gain (E). The following is the formula for expected gain:

E ˆ PG ¡ C

In a sense, this is the di� erence of bene®t (PG) and cost (C). P is the probability thatthe goal will be achieved if that production rule is chosen. P is derived from par-ameters associated with the probability of successful execution of the production andthe likelihood the related goal will be achieved should the production be executedsuccessfully.

The parameter G is de®ned as the value of the goal. The calculation of G issubjective and is related to the time ACT-R should be interested in achieving thegoal. Anderson and Lebiere (1998) admit ACT-R has `little to say’ (p. 63) withregards to the initial value of G. However, once experience accrues, G can play agreater role in the expected gain formula. For example, one of the outcomes of aproduction is the generation of sub-goals. It is possible that deeper and deeper levelsof sub-goals are created and as these levels grow deeper the goal becomes associatedwith cumbersome requirements to achieve the goal. Consequently, the parameter Gdecreases, thereby decreasing the overall expected gain (E) of the top goal. E� orts toquantify supporting parameters exist, but are in early stages (Belavkin and Ritter2000).

If the production is chosen, there is an associated cost (C) of achieving the goal.Cost is derived from factors such as the expected e� ort. The time needed to retrievedeclarative knowledge and actual performance times used to determine the expectede� ort. Other sub-symbolic variables used to derive cost include prior successes orfailures.


Does production

match goal?

Another production available?

> 1 production candidate?

Add to list of production candidates

NO Rank pr oductions in

order of expected gain (E)

YES NO

Any productions m atching the

goal? Pop goal with failure

Choose highest ranked production

E > 0?

YES NO

Chunk activation >

retrieval threshold?

NO

Select chunks based on match score

YES

Goal modifica tion?

St ack change?

NO

External action NO

Push on stack?

YES

Result encoded in declarative

memory

Stack change?

Push on stack?

YES

YES

Modify goal

Modify goal

NO

NO

Pop goal

Activate subgoal

Modify goal YES

NO

Maintain focus on current goal

Return to higher goal

YES

NO

YES

YES

Start cycle with goal

NO

YES

More chunks?

NO

YES

Explicit Steps Implicit Steps Parallel Steps Cyclical Steps

Figure 2. ACT-R 4.0 production cycle.

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Returning to ®gure 2, the expected gain helps determine the optimum productionthat matches the goal. However, the highest ranked production is not necessarily theproduction that will be executed. If expected gain is less than zero (i.e. cost is greaterthan the bene®t), the goal will pop with failure. Another context in which the highestranked production will not be chosen is due to di� culties associated with chunkretrieval.

Productions consist of chunks, and the likelihood of retrieving the chunk is

determined, in part, by a chunk’s level of activation. A parameter fundamental tothe calculation of activation is base level activation. The recency and frequency withwhich a chunk is used determines the base level of activation. Strength of associationbetween a chunk and possible elements as well as attentional weighting areaccounted for in the level of activation. Attentional weighting has been examinedin relation to working memory capacity (Lovett et al. 1999).

If enough time passes, decay can decrease the level of activation to a point wherethe chunk can no longer be retrieved. ACT-R accounts for this with a threshold forlevel of activation. The probability of exceeding this threshold uses parametersincluding the level of activation, decay rate (typically set at 0.5), and a noise controlparameter. The noise parameter is derived from logistic distributions.

Most chunks do not perfectly match a production and, without an account forthe degree with which the chunk matches and mismatches the production, there is arisk of commission error for chunks above the retrieval threshold. ACT-R provideswhat is called a `match score’. This score is simply the di� erence between the level ofactivation and the degree of mismatch. A chunk pattern contains slots and thenumber of slots in which a chunk mismatches the desired chunk pattern determinesthe degree of mismatch.

Once a chunk is retrieved, the production can be executed. Execution can resultone of six possible outcomes:

(1) No goal modi®cation. No change in the goal stack. The purpose of this type ofproduction is to generate an external action. As described by Anderson andLebiere (1998), there has been active development of ACT-R/PM (PerceptualMotor) (Byrne 2000) to account for interactions with an external environ-ment. This version of ACT-R contains components and mechanisms similarto EPIC (Kieras et al. 1997).

(2) Goal modi®cation. No change in the goal stack. In this case, the goal will bemodi®ed, but the current goal will remain the focus of attention upon com-pletion of production ®ring.

(3) No goal modi®cation. Push on stack (sub-goal initiated). Often sub-goals areneeded to solve more speci®c problems. This type of production initiates asub-goal, which is similar to sub-goaling in SOAR (Rosenbloom et al. 1993).In ACT-R, once the sub-goal is executed, the focus returns to the higher goalthat called it without the goals being modi®ed. An interesting di� erenceworth noting between SOAR (Rosenbloom et al. 1993) and ACT-R is thatSOAR stores the result of sub-goals as productions, where ACT-R storesresults of sub-goals in the form of chunks.

(4) Goal modi®cation. Push on stack (sub-goal initiated). A goal can be modi®edbefore a sub-goal is initiated. When the sub-goal is completed, it is sometimesuseful to have a di� erent goal to return to so that the same production rule isnot ®red repeatedly.


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(5) No goal modi®cation. Pop stack. It is useful to terminate a recursive loop ofproductions, as in the case of adding columns in mathematics. There couldbe a production called Do-Add-Stop, that, once the condition is met, termi-nates the process by a pop of the goal stack.

(6) Goal modi®cation. Pop stack. This is ofter a useful type of production insimple productions such as the addition of two numbers. The slot for sumcan be modi®ed to the solution before the production pops and the focusreturns to a higher goal.

It is helpful to note that declarative memory receives the details of the outcomes.This is essential to the process of learning and, more speci®cally, productioncompilation.

ACT-R contains two levels of processing and learning; symbolic and sub-sym-bolic. With respect to learning, the symbolic level involves the acquisition of chunksand production rules. The sub-symbolic level of learning involves the acquisition ofparameters that govern the deployment of elements such as chunks and productions.The sub-symbolic level also provides stochastic noise parameters.

Production compilation, where new rules are generated, occurs at the symboliclevel of ACT-R. Anderson and Lebiere (1998) note that production compilation wasone of the last concepts to be included in their work. Furthermore, it is acknowl-edged that validation studies are still being conducted and, therefore, the productioncompilation mechanism of learning remains `somewhat tentative’ (p. 117). Currentchanges being made to the latest version of ACT-R (5.0) are not supposed to contra-dict theories in earlier versions (Lebiere 2000).

2.3. Ecological perspectiveAn active area of research is the examination of human performance in their naturalenvironments. The Army Research Institute (ARI) has conducted a sizeable amountof research investigating procedural knowledge decay. One of the earliest articles ofthis kind was by Siegel et al. (1981). Interestingly, the performance functions exhi-biting degradation resemble the psychological literature mentioned earlier (e.g.Wixted and Ebbesen 1991). The article began over a decade of research in thearea of skill loss. Rose (1989) provided a summary of the human information pro-cessing issues being derived by the research at the Army Research Institute. Rosecited four variables in¯uencing the retention of procedural knowledge: task charac-teristics, degree of original learning, individual di� erences and time (i.e. duration ofretention interval).

Continuing the e� ort to examine skill loss using natural environment data,Macpherson et al. (1989) developed and validated a model of skill retention. Thetask involving procedural knowledge was wheel vehicle maintenance. The variablesof interest used in the model were the length of the retention interval and the com-plexity of the task.

The Army’s interest in this area of research increased during Operation DesertStorm (Wisher et al. 1991). A concern was that the retention interval experienced byIndividual Ready Reserve (IRR) units could inhibit adequate performance. Thestudy by Wisher et al. was built around a survey of reserve units. The surveyprobed their varying military occupational specialties (MOS) to determine theextent of skill decay and the variables associated with the decrement. Conclusionscentred on the length of the retention interval.


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The conclusion that individual di� erences in¯uence retention is interesting, inthat it provides ecological evidence for the theories derived for 3CAPS by Just andCarpenter (1992). Although Rose does not prepare an in-depth explanation of whyindividual di� erences play a role in retention, Just and Carpenter’s capacity theoryof comprehension could provide deeper insight.

The conclusion that the time away from a task can in¯uence knowledge retentionwas not unique compared to earlier research (e.g. Wickelgren 1976). However, thesubstantial contribution was the examination of procedural knowledge instead ofdeclarative knowledge. Prior research in retention primarily examined declarativeknowledge such as lists of adjectives (Wickelgren 1976). ARI examined the dynamicsof procedural knowledge in the retention interval, concluding that time is a validvariable for predicting the decay of procedural knowledge. This research was beingconducted roughly in the same time frame as Anderson (1976, 1983). Here,Anderson was still establishing the relationship and distinction of declarative andprocedural knowledge and proposed measuring procedural knowledge by strength.Anderson proposed that the strength of a production was a function of the timepassing since it was last ®red and the frequency with which it was ®red. The durationof the retention interval as a factor in procedural knowledge dynamics was consistentwith ARI research.

3. Conceptual model of procedural knowledge degradationOf the numerous factors reviewed concerning procedural knowledge performance,three will be focused upon. A conceptual model has been derived to detail themechanisms associated with the variables (®gure 3).

Since several concepts in the illustration are drawn from the ACT-R architecture(Anderson and Lebiere 1998) and the production cycle depicted in ®gure 2, many ofthe steps in ACT-R have been condensed into more general processes. The diagramhas four levels. The top level is essentially the production cycle. However, as noted inthe literature review, there is a symbolic level and sub-symbolic level that supportACT-R performance and knowledge dynamics. The top level in the diagram con-tains a new entry to the production cycle where implicit associations are evaluated.This concept will be elaborated upon shortly.

The symbolic level consists of the primary equation used to support con¯ictresolution. The equation calculates the expected gain of each production. The pro-duction with the highest expected gain is chosen for ®ring. The sub-symbolic levelrecords event data associated with production ®ring and supports parameters in theexpected gain equation.

One variable of interest concerning procedural knowledge is associated with thefrequency with which the production cycle is executed. As productions ®re, thenumber of production cycle iterations is recorded and supports production strengthat the sub-symbolic level. The higher the production strength, the faster will be therate of cycle execution (Anderson and Lebiere 1998). It is proposed that changes inthe number of production cycle iterations will result in a change in cycle executionrate.

At the sub-symbolic level, productions that result in failure are taken intoaccount. Figure 3 depicts output descending from failure into the push for a sub-goal. The evaluation of the failure leads to two outcomes.

If the failure resulted in no e� ect on the task, another approach may beattempted, thereby initiating a new production cycle. This is believed to be what


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occurred in Singley and Anderson’s (1989) text-editing task where previous keymappings were replaced with a new key mapping. The new key mapping requiresa new set of production conditions and actions and, therefore, a separate cycle hascommenced. It is proposed that this separate cycle allows humans to switch back tothe original cycle in the context of test phase in a retroactive interference protocol.

Failure evaluation can result in a second outcome involving an apparent e� ect onthe task. In this case, it is proposed that the correct set of production candidates wascompiled, but an error in implicit association occurred. A sub-goal must be pushedto translate the error in association. The update is fed back into the main productioncycle.

According to the ACT-R architecture, the sub-symbolic parameter for failurewould also serve as a resource of information for the error. However, this factor only


Push Goal Production Candidates Compiled

Implicit Associations

Evaluated

Production Chosen Through Conflict

Resolution

Fire Production

Update Parameters

Expected Gain (E) =

Probability of Successful Goal Achievement (P) *

Value of Current Goal

(G) - Cost of

Achieving Goal (C)

Rate of Cycle

Execution

Strength

Number of Production

Cycle Iterations

Success Failure Time

Push Subgoal

Evaluate Failure

Any Effect? Push

Subgoal

Push Newgoal

Translate Association

Error

Update Implicit Associations

NO

YES

Production Candidates Compiled

Implicit Associations

Evaluated

Figure 3. Conceptual illustration of procedural knowledge degradation.

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results in the same production being less likely to ®re in the future. Therefore, it ispossible the same error could occur again but, as Singley and Anderson (1989)found, repeating the error is an event that is `quickly weeded out’ (p. 119).

The error in implicit associations will require the `translation’ production to ®reand grow in strength to inhibit the wrong rule being chosen in con¯ict resolution.This is similar to response set suppression proposed by Postman et al. (1968). Thesub-goal will increase action latency and result in negative transfer as the new associ-ation is learned. As learning increases, the strength and expected gain will be high. Itis proposed that the level of these parameters for the implicit association translationwill enhance the likelihood of retroactive interference in the context of test phaseperformance in a retroactive interference protocol. This is believed to be a factortriggering the interference e� ects found by Lovett and Anderson (1994), which areseemingly in contradiction with Singley and Anderson’s (1989) ®ndings where impli-cit associations in the text-editing task are believed to be less critical in task perform-ance.

4. Concluding remarksThe primary objective of this research was to develop a conceptual model based oncognitive mechanisms of procedural knowledge degradation. Input was drawn fromtheoretical, computational and ecological elements of literature.

Given that most ergonomic tasks are procedural in terms of knowledge structurerather than declarative, it is essential that research expand its investigation of pro-cedural knowledge dynamics. In addition, it is important that the outcomes bequanti®ed in computational simulations of cognition. As a result, much neededpredictive modelling tools, representative of cognitive performance, can be morerobust.

While some common principles exist between declarative and procedural knowl-edge, it is maintained that signi®cant di� erences exist with respect to their suscept-ibility to interference e� ects. The added dimensions of performance variabilityintroduced by procedural knowledge are complex. The current research representsan important, yet small step, towards an improved understanding of proceduralknowledge degradation.

AcknowledgementsThe work was performed while the authors were at Wright State University.

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About the authorsNathan Brannon is a senior member of technical sta� at Sandia National Laboratories inAlbuquerque, New Mexico, with responsibility for human factors research related to nationalsecurity. He received bachelors and masters degrees in human factors engineering from WrightState University in 1994 and 1998, respectively. He conducted doctoral research under co-author Richard Koubek and in 2001 received his PhD in Engineering with a focus area inhumans and complex systems from Wright State University.

Richard J. Koubek is Professor and Head for the Harold and Inge Marcus Department ofIndustrial and Manufacturing Engineering at The Pennsylvania State University. Prior to thisappointment he held the posts of Professor and Chair of the Department of Biomedical,Industrial and Human Factors Engineering, and Associate Dean for Research and GraduateStudies for the College of Engineering and Computer Science at Wright State University. Heserved six years on the faculty in the School of Industrial Engineering at Purdue University


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and began his academic career as a faculty member in the College of Engineering and Com-puter Science at Wright State University. Professor Koubek’s research focuses on usability,human aspects of manufacturing, and human±computer interaction. He has been editor of theInternational Journal of Cognitive Ergonomics, and is a member of the Editorial Board for theInternational Journal of Human Factors in Manufacturing and International Journal of Human±Computer Interaction. Professor Koubek was Conference Chair for the Fifth InternationalConference on Human Aspects of Advanced Manufacturing and Hybrid Automation and Co-Chair for the Fourth International Conference on Engineering Psychology and CognitiveErgonomics.


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towards a conceptual model of procedural knowledge degradation

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