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A cognitive architecture-based modelling approach to 
understanding biases in visualisation behaviour 
David Peebles 
Univ...
eld 
November 8, 2014 
David Peebles (University of Hudders
eld) Cognitive architectures and visualisations November 8, 2014 1 / 16
Outline of the talk 
Two aims of the research: 
To understand at a very detailed level of analysis the processes by 
which...
cial agents that can provide expert interpretations of 
data presented in external representations (AI). 
Structure of the...
eld) Cognitive architectures and visualisations November 8, 2014 2 / 16
The embodied cognition-task-artefact triad 
Embodied 
Cognition 
Task 
Artefact 
Interactive 
Behaviour 
Interactive behav...
eld) Cognitive architectures and visualisations November 8, 2014 3 / 16
Understanding behaviour with visualisations 
Requires detailed understanding of the wide range of factors that 
aect human...
eld) Cognitive architectures and visualisations November 8, 2014 4 / 16
Human performance modelling 
Requires the development of two connected models: 
Functional task environment: 
Speci
es elements of the physical environment relevant to the agent's 
goals and particular cognitive, perceptual and motor char...
c knowledge that the agent 
must possess. 
Specify the control mechanisms (i.e., interactive routines, methods and 
strate...
eld) Cognitive architectures and visualisations November 8, 2014 5 / 16
Cognitive architectures 
Theories of how the permanent computational structures and 
processes that underlie intelligent b...
ed theories of cognition (Newell, 1990) 
Three main symbolic cognitive architectures: Soar (Newell, 1990; 
Laird, 2012), E...
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A cognitive architecture-based modelling approach to understanding biases in visualisation behaviour

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Title: "A cognitive architecture-based modelling approach to
understanding biases in visualisation behaviour". A talk given at the "Dealing with Cognitive Biases in Visualisations (DECISIVe 2014) workshop at IEEE VIS, Paris, November 2014.

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A cognitive architecture-based modelling approach to understanding biases in visualisation behaviour

  1. 1. A cognitive architecture-based modelling approach to understanding biases in visualisation behaviour David Peebles University of Hudders
  2. 2. eld November 8, 2014 David Peebles (University of Hudders
  3. 3. eld) Cognitive architectures and visualisations November 8, 2014 1 / 16
  4. 4. Outline of the talk Two aims of the research: To understand at a very detailed level of analysis the processes by which people interact with external representations of information (cognitive science). To develop arti
  5. 5. cial agents that can provide expert interpretations of data presented in external representations (AI). Structure of the talk: Human performance modelling. Cognitive architectures. ACT-R cognitive architecture. A recent example: An ACT-R model of graph comprehension. David Peebles (University of Hudders
  6. 6. eld) Cognitive architectures and visualisations November 8, 2014 2 / 16
  7. 7. The embodied cognition-task-artefact triad Embodied Cognition Task Artefact Interactive Behaviour Interactive behaviour: Emerges from dynamic interaction of goal-directed, task-driven cognition/perception/action with designed task environment. A complex combination of bottom-up stimulus driven and top-down goal and knowledge driven processes. Makes little sense to consider and investigate cognition in isolation. David Peebles (University of Hudders
  8. 8. eld) Cognitive architectures and visualisations November 8, 2014 3 / 16
  9. 9. Understanding behaviour with visualisations Requires detailed understanding of the wide range of factors that aect human performance. Internal to the agent: Domain and graphical representation knowledge (expertise) Working memory capacity Strategies Learning/adaptation to task environment External to the agent: Computational aordances Emergent/salient features Gestalt principles of perceptual organisation Task and environment complexity David Peebles (University of Hudders
  10. 10. eld) Cognitive architectures and visualisations November 8, 2014 4 / 16
  11. 11. Human performance modelling Requires the development of two connected models: Functional task environment: Speci
  12. 12. es elements of the physical environment relevant to the agent's goals and particular cognitive, perceptual and motor characteristics (Gray, Schoelles Myers, 2006). Human performance model: Incorporate current theories of the cognitive, perceptual and motor processes. Specify the modular architecture of the mind, the functions and limitations of the various modules, and the connections between them. Specify the task-general and task-speci
  13. 13. c knowledge that the agent must possess. Specify the control mechanisms (i.e., interactive routines, methods and strategies) that determine performance. David Peebles (University of Hudders
  14. 14. eld) Cognitive architectures and visualisations November 8, 2014 5 / 16
  15. 15. Cognitive architectures Theories of how the permanent computational structures and processes that underlie intelligent behaviour are organised. Uni
  16. 16. ed theories of cognition (Newell, 1990) Three main symbolic cognitive architectures: Soar (Newell, 1990; Laird, 2012), Epic (Kieras Meyer, 1997), ACT-R (Anderson, 2007) All three based on production system architecture. All can interact with simulated task environments to model interaction with external representations. David Peebles (University of Hudders
  17. 17. eld) Cognitive architectures and visualisations November 8, 2014 6 / 16
  18. 18. The ACT-R cognitive architecture Visual Module Environment ACT−R Buffers Pattern Matching Production Execution Motor Module Problem State Declarative Memory Procedural Memory Control State Hybrid architecture with symbolic and subsymbolic components Production system model of procedural memory cognitive control Semantic network model of declarative memory Activation-based learning, memory retrieval forgetting mechanisms Simulated eyes hands for interacting with computer-based tasks David Peebles (University of Hudders
  19. 19. eld) Cognitive architectures and visualisations November 8, 2014 7 / 16
  20. 20. Graph comprehension Initial familiarisation stage prior to other tasks involving: Identi
  21. 21. cation classi
  22. 22. cation of variables into IV(s) and DV Association of variables with axes and representational features (e.g., colours, shapes, line styles) Identi
  23. 23. cation of relationship(s) depicted May be an end in itself or a prerequisite for other tasks David Peebles (University of Hudders
  24. 24. eld) Cognitive architectures and visualisations November 8, 2014 8 / 16
  25. 25. Interaction graphs Low High 100 90 80 70 60 50 40 30 20 10 Percent Error as a function of Experience and Time of Day Percent Error Experience Time of Day Day Night Students more likely to misinterpret (Zacks Tversky, 1999) or inadequately interpret line graphs (Peebles Ali, 2009, Ali Peebles, 2013) Percent Error as a function of Experience and Time of Day Low High 100 90 80 70 60 50 40 30 20 10 ● ● Percent Error Experience Time of Day Day Night Line graphs better at depicting common relationships for experts (Kosslyn, 2006) Interpretation facilitated by recognition of familiar patterns David Peebles (University of Hudders
  26. 26. eld) Cognitive architectures and visualisations November 8, 2014 9 / 16
  27. 27. An example expert verbal protocol Glucose Uptake as a function of Fasting and Relaxation Training High Low 250 225 200 175 150 125 100 75 50 25 0 l l l l Glucose Uptake Fasting Relaxation Training Yes No 1 (Reads) Glucose uptake as a function of fasting and relaxation training 2 Alright, so we have. . . you're either fasting or you're not. . . 3 You have relaxation training or you don't. . . 4 And so. . . not fasting. . . er. . . 5 So there's a big eect of fasting. . . 6 Very little glucose uptake when you're not fasting. . . 7 And lots of glucose uptake when you are fasting. . . 8 And a comparatively small eect of relaxation training. . . 9 That actually interacts with fasting. David Peebles (University of Hudders
  28. 28. eld) Cognitive architectures and visualisations November 8, 2014 10 / 16
  29. 29. Stages of comprehension Glucose Uptake as a function of Fasting and Relaxation Training High Low 250 225 200 175 150 125 100 75 50 25 0 l l l l Glucose Uptake Fasting Relaxation Training Yes No Comprehension proceeds in the following order: 1 Read title. Identify variable names and create declarative chunks. 2 Seek variable labels, identify what they are by their location and if required, associate with label levels 3 Associate variable levels with indicators (position or colour) 4 Look at plot region and attempt to interpret distances. If a highly salient pattern exists (e.g., cross, large gap) process that
  30. 30. rst 5 Continue until no more patterns are recognised David Peebles (University of Hudders
  31. 31. eld) Cognitive architectures and visualisations November 8, 2014 11 / 16
  32. 32. Videos of the model David Peebles (University of Hudders
  33. 33. eld) Cognitive architectures and visualisations November 8, 2014 12 / 16
  34. 34. An example model protocol Chick Weight as a function of Diet and Hormone Supplement Artificial Natural 50 45 40 35 30 25 20 15 10 5 0 l l l l Chick Weight Diet Hormone Supplement MPE GCE text at top of display. . . [chickweight] [= variable] [as] [a] [function] [of] [diet] [= variable] [and] [hormonesupplement] [= variable] text at bottom of display. . . [diet] at [bottom] [= IV] look to nearest text. . . [natural] is a level of [diet] [natural] is [right] [arti
  35. 35. cial] is a level of [diet] [arti
  36. 36. cial] is [left] text at far right of display. . . [hormonesupplement] at [far-right] [= IV] look to nearest text. . . [mpe] is a level of [hormonesupplement] [gce] is a level of [hormonesupplement] David Peebles (University of Hudders
  37. 37. eld) Cognitive architectures and visualisations November 8, 2014 13 / 16
  38. 38. An example model protocol Chick Weight as a function of Diet and Hormone Supplement Artificial Natural 50 45 40 35 30 25 20 15 10 5 0 l l l l Chick Weight Diet Hormone Supplement MPE GCE objects in plot region. . . a [green] [line] no memory for [green] look to legend. . . [green] [rectangle]. look for nearest text. . . [green] represents [gce] [blue] [rectangle]. look for nearest text. . . [blue] represents [mpe] text at far left of display. . . [chickweight] at [far-left] [= DV] look to pattern. . . substantial dierence between legend levels. . . [0.2] di [blue] = [small] eect [mpe] [0.2] di [green] = [small] eect [gce] compare [blue] and [green] levels. . . [moderate] di: [gce] greater than [mpe] [moderate] [main] eect [hormonesupplement] David Peebles (University of Hudders
  39. 39. eld) Cognitive architectures and visualisations November 8, 2014 14 / 16
  40. 40. An example model protocol Chick Weight as a function of Diet and Hormone Supplement Artificial Natural 50 45 40 35 30 25 20 15 10 5 0 l l l l Chick Weight Diet Hormone Supplement MPE GCE identify x-axis levels. . . [0.4] di [left] = [moderate] eect [arti
  41. 41. cial] [0.4] di [right] = [moderate] eect [natural] compare [left] and [right] levels. . . [small] di [natural] [arti
  42. 42. cial] [small] [main] eect [diet] compare left and right patterns. . . [0.0] di between points. [neither] bigger [no] di [same] order = [no-interaction] for [arti
  43. 43. cial], [gce] [mpe] for [natural], [gce] [mpe] David Peebles (University of Hudders
  44. 44. eld) Cognitive architectures and visualisations November 8, 2014 15 / 16
  45. 45. Summary ACT-R is a powerful, empirically veri
  46. 46. ed, tool for creating models of interactive behaviour with complex task environments. Model output (errors, task completion times, eye movements, verbal protocols) can be compared with human data. Contains learning mechanisms to model adaptation to task environment and development of expertise. Can be used as a platform for creating human-level arti
  47. 47. cial agents to interpret graphical representations and visualisations. David Peebles (University of Hudders
  48. 48. eld) Cognitive architectures and visualisations November 8, 2014 16 / 16

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