decisive workshop introduction
TRANSCRIPT
Sources of bias when working with visualisations
David Peebles
University of Huddersfield
November 8, 2014
David Peebles (University of Huddersfield) Biases and visualisations November 8, 2014 1 / 16
Where’s the ‘cognitive’?
Using visualisations prime example of‘embodied’ cognition (Wilson, 2002).
Situated in real-worldenvironment. Inherently involvesperception/action.
Time pressured. Underpressure of real-time interactionwith environment.
Exploits task environment toreduce cognitive workload byholding, representing, andmanipulating information.Knowledge in world combineswith knowledge in head.
David Peebles (University of Huddersfield) Biases and visualisations November 8, 2014 2 / 16
Where’s the ‘cognitive’?
Using visualisations prime example of‘embodied’ cognition (Wilson, 2002).
Situated in real-worldenvironment. Inherently involvesperception/action.
Time pressured. Underpressure of real-time interactionwith environment.
Exploits task environment toreduce cognitive workload byholding, representing, andmanipulating information.Knowledge in world combineswith knowledge in head.
David Peebles (University of Huddersfield) Biases and visualisations November 8, 2014 2 / 16
Where’s the ‘cognitive’?
Using visualisations prime example of‘embodied’ cognition (Wilson, 2002).
Situated in real-worldenvironment. Inherently involvesperception/action.
Time pressured. Underpressure of real-time interactionwith environment.
Exploits task environment toreduce cognitive workload byholding, representing, andmanipulating information.Knowledge in world combineswith knowledge in head.
David Peebles (University of Huddersfield) Biases and visualisations November 8, 2014 2 / 16
Where’s the ‘cognitive’?
Using visualisations prime example of‘embodied’ cognition (Wilson, 2002).
Situated in real-worldenvironment. Inherently involvesperception/action.
Time pressured. Underpressure of real-time interactionwith environment.
Exploits task environment toreduce cognitive workload byholding, representing, andmanipulating information.Knowledge in world combineswith knowledge in head.
David Peebles (University of Huddersfield) Biases and visualisations November 8, 2014 2 / 16
The embodied cognition-task-artefact triad
Embodied
CognitionTask
Artefact
Interactive
Behaviour
Interactive behaviour:
Emerges from dynamic interaction of goal-directed, task-drivencognition/perception/action with designed task environment.A complex combination of bottom-up stimulus driven and top-downgoal and knowledge driven processes.
Makes little sense to consider and investigate cognition in isolation.
David Peebles (University of Huddersfield) Biases and visualisations November 8, 2014 3 / 16
Sources of bias when working with visualisations
Bias. A systematic preference for aparticular pattern of behaviour in atask environment.
Question: How are preferences (andinterpretations) created/shaped bythe embodied cognition-task-artefacttriad?
Four sources of bias:
Computational affordances
Emergent/salient features
Gestalt principles of perceptualorganisation
Adaptive behaviour
David Peebles (University of Huddersfield) Biases and visualisations November 8, 2014 4 / 16
Sources of bias when working with visualisations
Bias. A systematic preference for aparticular pattern of behaviour in atask environment.
Question: How are preferences (andinterpretations) created/shaped bythe embodied cognition-task-artefacttriad?
Four sources of bias:
Computational affordances
Emergent/salient features
Gestalt principles of perceptualorganisation
Adaptive behaviour
David Peebles (University of Huddersfield) Biases and visualisations November 8, 2014 4 / 16
Sources of bias when working with visualisations
Bias. A systematic preference for aparticular pattern of behaviour in atask environment.
Question: How are preferences (andinterpretations) created/shaped bythe embodied cognition-task-artefacttriad?
Four sources of bias:
Computational affordances
Emergent/salient features
Gestalt principles of perceptualorganisation
Adaptive behaviour
David Peebles (University of Huddersfield) Biases and visualisations November 8, 2014 4 / 16
Computational affordances (Peebles & Cheng, 2003)
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Jan FebMar AprMayJun Jul AugSep Oct NovDec
Am
ount P
rodu
ced
Month
Amount of Silver and Gold Produced (in tonnes)by ‘Precious Metals Plc.’ in 1996
Silver
Gold
Informationally equivalent
Computationally inequivalent.
Require different procedures.
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0 1 2 3 4 5 6 7 8 9 10
Gold
Silver
Amount of Silver and Gold Produced (in tonnes)by ‘Precious Metals Plc.’ in 1996
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
“When gold is 4, what is thevalue of silver?
“Which two months have thesame values of silver and gold?
David Peebles (University of Huddersfield) Biases and visualisations November 8, 2014 5 / 16
Emergent/salient features
David Peebles (University of Huddersfield) Biases and visualisations November 8, 2014 6 / 16
Emergent features (Peebles, 2008)
Tasks: local (1 feature) and global (all features) comparison.
Accuracy and latency of comparison judgements affected by:
Representation used.Value being compared.Emergent features created by arrangements of values.
David Peebles (University of Huddersfield) Biases and visualisations November 8, 2014 7 / 16
Emergent features affect distance perception
(a) Transport = 1.91 (b) Transport = 2.14
David Peebles (University of Huddersfield) Biases and visualisations November 8, 2014 8 / 16
Gestalt principles (Ali & Peebles, 2013)
Laws of perceptual organisation (e.g., proximity, similarity, continuity,connectedness, common fate) affect grouping of graphical elements.
Low High
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Percent Error as a function of Experience and Time of Day
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rce
nt
Err
or
Experience
Time of Day
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Night
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Percent Error as a function of Experience and Time of Day
Pe
rce
nt
Err
or
Experience
Time of Day
Day
Night
Line: Novices focus primarily on legend variable (connectedness andsimilarity).
Bar: Novices’ attention balanced between legend and x-axis variables.
David Peebles (University of Huddersfield) Biases and visualisations November 8, 2014 9 / 16
Using emergent features and Gestalt principles
AA AB
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Response Time as a function of Task and Stimulus Type
Re
sp
on
se
Tim
e
Task
Stimulus Type
Words
Pictures
(a) “Crossover” interaction
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Percent Error as a function of Experience and Time of Day
Pe
rce
nt
Err
or
ExperienceLow High
Time of Day
Day
Night
(b) “Colour-match” graph
(a) Experts learn emergent features for rapid pattern recognition.
(b) Knowledge of Gestalt principles can be used to design more effectiverepresentations (Ali & Peebles, 2013).
David Peebles (University of Huddersfield) Biases and visualisations November 8, 2014 10 / 16
Basic elements of visualisation behaviour
Cognitive processes:
Goal setting and monitoring, initiate visual search, retrieval from LTM,generating inferences, etc.
Perceptual processes.
Judgement of length, direction, area, position on common scale(Cleveland & McGill, 1984).Visual comparisons of length, colour, shape, quantity.
Physical actions:
Eye movement saccades and fixations, mouse clicks and cursormovements, finger taps and pinches etc.Interface manipulations: Selection/highlighting with mouse; dragging,realigning, rotating, deleting; zooming in and out
David Peebles (University of Huddersfield) Biases and visualisations November 8, 2014 11 / 16
Basic elements of visualisation behaviour
Cognitive processes:
Goal setting and monitoring, initiate visual search, retrieval from LTM,generating inferences, etc.
Perceptual processes.
Judgement of length, direction, area, position on common scale(Cleveland & McGill, 1984).Visual comparisons of length, colour, shape, quantity.
Physical actions:
Eye movement saccades and fixations, mouse clicks and cursormovements, finger taps and pinches etc.Interface manipulations: Selection/highlighting with mouse; dragging,realigning, rotating, deleting; zooming in and out
David Peebles (University of Huddersfield) Biases and visualisations November 8, 2014 11 / 16
Basic elements of visualisation behaviour
Cognitive processes:
Goal setting and monitoring, initiate visual search, retrieval from LTM,generating inferences, etc.
Perceptual processes.
Judgement of length, direction, area, position on common scale(Cleveland & McGill, 1984).Visual comparisons of length, colour, shape, quantity.
Physical actions:
Eye movement saccades and fixations, mouse clicks and cursormovements, finger taps and pinches etc.Interface manipulations: Selection/highlighting with mouse; dragging,realigning, rotating, deleting; zooming in and out
David Peebles (University of Huddersfield) Biases and visualisations November 8, 2014 11 / 16
Interactive routines
Basic cognitive, perceptual and motor operators combined intointeractive routines that take between 0.3 to 1 second to execute:
Direct attention to object and encode features/location.
Move mouse cursor to graphical object and click on it (seeCPM-GOMS model below; Gray & Boehm-Davis, 2000).
David Peebles (University of Huddersfield) Biases and visualisations November 8, 2014 12 / 16
Unit tasks and strategy selection
Unit tasks: Combinations ofinteractive routines that performsubtasks.
Typical execution time: between 3sand 30s.
Select and visually mark subsetof data.
Locate variable value(s)according to some criterion(e.g., max, min, median etc.).
Strategies: Sequences of unit tasks.
Attend & click object
Look for 'Done' button
Click on 'Done' button
Encode object features & seek next object
Blended location retrieved,attend to location
Attend & click object
Encode object features & request retrieval
Relocate object & seek next object
Relocation
Presentation
Yes No
Current phase?
Unattended object?
Unattended object?NoYes
David Peebles (University of Huddersfield) Biases and visualisations November 8, 2014 13 / 16
Unit tasks and strategy selection
Unit tasks: Combinations ofinteractive routines that performsubtasks.
Typical execution time: between 3sand 30s.
Select and visually mark subsetof data.
Locate variable value(s)according to some criterion(e.g., max, min, median etc.).
Strategies: Sequences of unit tasks.
Attend & click object
Look for 'Done' button
Click on 'Done' button
Encode object features & seek next object
Blended location retrieved,attend to location
Attend & click object
Encode object features & request retrieval
Relocate object & seek next object
Relocation
Presentation
Yes No
Current phase?
Unattended object?
Unattended object?NoYes
David Peebles (University of Huddersfield) Biases and visualisations November 8, 2014 13 / 16
Adaptive behaviour
Interactive routine changesreduce interaction cost &optimise control system resourceallocation. Milliseconds matter(Gray & Boehm-Davis, 2000)
Ballard et al. (1995) increasedinformation access cost from eyemovement to head movement –users shifted from display basedaccess to memory retrieval.
Attend & click object
Look for 'Done' button
Click on 'Done' button
Encode object features & seek next object
Blended location retrieved,attend to location
Attend & click object
Encode object features & request retrieval
Relocate object & seek next object
Relocation
Presentation
Yes No
Current phase?
Unattended object?
Unattended object?NoYes
David Peebles (University of Huddersfield) Biases and visualisations November 8, 2014 14 / 16
Adaptive behaviour
Interactive routine changesreduce interaction cost &optimise control system resourceallocation. Milliseconds matter(Gray & Boehm-Davis, 2000)
Ballard et al. (1995) increasedinformation access cost from eyemovement to head movement –users shifted from display basedaccess to memory retrieval.
Attend & click object
Look for 'Done' button
Click on 'Done' button
Encode object features & seek next object
Blended location retrieved,attend to location
Attend & click object
Encode object features & request retrieval
Relocate object & seek next object
Relocation
Presentation
Yes No
Current phase?
Unattended object?
Unattended object?NoYes
David Peebles (University of Huddersfield) Biases and visualisations November 8, 2014 14 / 16
Adaptation as a source of bias
Adoption and adaptation determined by:
Cost structure of the task environment (i.e., how quick/easy is it toexecute the task).
The representational efficiency of the visualisation.
History of success with the strategy.
Adaptation typically beneath conscious awareness and deliberatecontrol.
Do users always adapt to an optimal interaction?
Not necessarily – only when local (interactive routine level) optimumcoincides with global task level optimum (Fu & Gray, 2004).
Suboptimal interaction and interpretation may result from strategyselection pressures resulting from unconscious choices made at theembodiment level.
David Peebles (University of Huddersfield) Biases and visualisations November 8, 2014 15 / 16
Summary
Interpretation of data graphics can be shaped/biased bynumber of factors:
Visual and computational properties of the representation.Adaptive behaviour of the user seeking to minimise effort.
Research on simpler data graphics must be extended to:
Larger and more complex data sets.Broader class of visualisations.Cover the increasing variety of interactions and manipulations that arebeing developed.
Vital for design of most appropriate representations/taskenvironments for different users and tasks and to reduce error.
Important role for cognitive science theories and methods inthis research (e.g., computational cognitive modelling)
David Peebles (University of Huddersfield) Biases and visualisations November 8, 2014 16 / 16