1. Sources of bias when working with visualisations
David Peebles
University of Hudders

2. eld
November 8, 2014
David Peebles (University of Hudders

3. eld) Biases and visualisations November 8, 2014 1 / 16

4. Where's the `cognitive'?
Using visualisations prime example of
`embodied' cognition (Wilson, 2002).
David Peebles (University of Hudders

5. eld) Biases and visualisations November 8, 2014 2 / 16

6. Where's the `cognitive'?
Using visualisations prime example of
`embodied' cognition (Wilson, 2002).
Situated in real-world
environment. Inherently involves
perception/action.
David Peebles (University of Hudders

7. eld) Biases and visualisations November 8, 2014 2 / 16

8. Where's the `cognitive'?
Using visualisations prime example of
`embodied' cognition (Wilson, 2002).
Situated in real-world
environment. Inherently involves
perception/action.
Time pressured. Under
pressure of real-time interaction
with environment.
David Peebles (University of Hudders

9. eld) Biases and visualisations November 8, 2014 2 / 16

10. Where's the `cognitive'?
Using visualisations prime example of
`embodied' cognition (Wilson, 2002).
Situated in real-world
environment. Inherently involves
perception/action.
Time pressured. Under
pressure of real-time interaction
with environment.
Exploits task environment to
reduce cognitive workload by
holding, representing, and
manipulating information.
Knowledge in world combines
with knowledge in head.
David Peebles (University of Hudders

11. eld) Biases and visualisations November 8, 2014 2 / 16

12. 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

13. eld) Biases and visualisations November 8, 2014 3 / 16

14. Sources of bias when working with visualisations
Bias. A systematic preference for a
particular pattern of behaviour in a
task environment.
David Peebles (University of Hudders

15. eld) Biases and visualisations November 8, 2014 4 / 16

16. Sources of bias when working with visualisations
Bias. A systematic preference for a
particular pattern of behaviour in a
task environment.
Question: How are preferences (and
interpretations) created/shaped by
the embodied cognition-task-artefact
triad?
David Peebles (University of Hudders

17. eld) Biases and visualisations November 8, 2014 4 / 16

18. Sources of bias when working with visualisations
Bias. A systematic preference for a
particular pattern of behaviour in a
task environment.
Question: How are preferences (and
interpretations) created/shaped by
the embodied cognition-task-artefact
triad?
Four sources of bias:
Computational aordances
Emergent/salient features
Gestalt principles of perceptual
organisation
Adaptive behaviour
David Peebles (University of Hudders

19. eld) Biases and visualisations November 8, 2014 4 / 16

20. Computational aordances (Peebles Cheng, 2003)
10
9
8
7
6
5
4
3
2
1
0
Amount of Silver and Gold Produced (in tonnes)
by ‘Precious Metals Plc.’ in 1996
Gold
Jan FebMar AprMayJun Jul AugSepOctNovDec
Amount Produced
Month
Silver
Informationally equivalent
Computationally inequivalent.
Require dierent procedures.
10
9
8
7
6
5
4
3
2
1
0
Amount of Silver and Gold Produced (in tonnes)
by ‘Precious Metals Plc.’ in 1996
0 1 2 3 4 5 6 7 8 9 10
Gold
Silver
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
When gold is 4, what is the
value of silver?
Which two months have the
same values of silver and gold?
David Peebles (University of Hudders

21. eld) Biases and visualisations November 8, 2014 5 / 16

22. Emergent/salient features
David Peebles (University of Hudders

23. eld) Biases and visualisations November 8, 2014 6 / 16

24. Emergent features (Peebles, 2008)
Tasks: local (1 feature) and global (all features) comparison.
Accuracy and latency of comparison judgements aected by:
Representation used.
Value being compared.
Emergent features created by arrangements of values.
David Peebles (University of Hudders

25. eld) Biases and visualisations November 8, 2014 7 / 16

26. Emergent features aect distance perception
(a) Transport = 1:91 (b) Transport = 2:14
David Peebles (University of Hudders

27. eld) Biases and visualisations November 8, 2014 8 / 16

28. Gestalt principles (Ali Peebles, 2013)
Laws of perceptual organisation (e.g., proximity, similarity, continuity,
connectedness, common fate) aect grouping of graphical elements.
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
Percent Error as a function of Experience and Time of Day Percent Error
Low High
100
90
80
70
60
50
40
30
20
10
Experience
Time of Day
Day
Night
Line: Novices focus primarily on legend variable (connectedness and
similarity).
Bar: Novices' attention balanced between legend and x-axis variables.
David Peebles (University of Hudders

29. eld) Biases and visualisations November 8, 2014 9 / 16

30. Using emergent features and Gestalt principles
AA AB
500
450
400
350
300
250
200
150
100
50
Response Time as a function of Task and Stimulus Type
Response Time
Task
Stimulus Type
Words
Pictures
(a) Crossover interaction
100
90
80
70
60
50
40
30
20
10
Percent Error as a function of Experience and Time of Day
Percent Error
Low High
Experience
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 eective
representations (Ali Peebles, 2013).
David Peebles (University of Hudders

31. eld) Biases and visualisations November 8, 2014 10 / 16

32. Basic elements of visualisation behaviour
Cognitive processes:
Goal setting and monitoring, initiate visual search, retrieval from LTM,
generating inferences, etc.
David Peebles (University of Hudders

33. eld) Biases and visualisations November 8, 2014 11 / 16

34. 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.
David Peebles (University of Hudders

35. eld) Biases and visualisations November 8, 2014 11 / 16

36. 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

37. xations, mouse clicks and cursor
movements,

38. nger taps and pinches etc.
Interface manipulations: Selection/highlighting with mouse; dragging,
realigning, rotating, deleting; zooming in and out
David Peebles (University of Hudders

39. eld) Biases and visualisations November 8, 2014 11 / 16

40. Interactive routines
Basic cognitive, perceptual and motor operators combined into
interactive 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 (see
CPM-GOMS model below; Gray Boehm-Davis, 2000).
David Peebles (University of Hudders

41. eld) Biases and visualisations November 8, 2014 12 / 16

42. Unit tasks and strategy selection
Unit tasks: Combinations of
interactive routines that perform
subtasks.
Typical execution time: between 3s
and 30s.
Select and visually mark subset
of data.
Locate variable value(s)
according to some criterion
(e.g., max, min, median etc.).
Attend click object
Look for 'Done' button
Click on 'Done' button
Encode object features seek next object
Yes No
Attend click object
Encode object features request retrieval
Blended location retrieved,
attend to location
Relocate object seek next object
Relocation
Presentation
Current phase?
Unattended object?
Yes No
Unattended object?
David Peebles (University of Hudders

43. eld) Biases and visualisations November 8, 2014 13 / 16

44. Unit tasks and strategy selection
Unit tasks: Combinations of
interactive routines that perform
subtasks.
Typical execution time: between 3s
and 30s.
Select and visually mark subset
of 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
Yes No
Attend click object
Encode object features request retrieval
Blended location retrieved,
attend to location
Relocate object seek next object
Relocation
Presentation
Current phase?
Unattended object?
Yes No
Unattended object?
David Peebles (University of Hudders

45. eld) Biases and visualisations November 8, 2014 13 / 16

46. Adaptive behaviour
Interactive routine changes
reduce interaction cost
optimise control system resource
allocation. Milliseconds matter
(Gray Boehm-Davis, 2000)
Attend click object
Look for 'Done' button
Click on 'Done' button
Encode object features seek next object
Yes No
Attend click object
Encode object features request retrieval
Blended location retrieved,
attend to location
Relocate object seek next object
Relocation
Presentation
Current phase?
Unattended object?
Yes No
Unattended object?
David Peebles (University of Hudders

47. eld) Biases and visualisations November 8, 2014 14 / 16

48. Adaptive behaviour
Interactive routine changes
reduce interaction cost
optimise control system resource
allocation. Milliseconds matter
(Gray Boehm-Davis, 2000)
Ballard et al. (1995) increased
information access cost from eye
movement to head movement {
users shifted from display based
access to memory retrieval.
Attend click object
Look for 'Done' button
Click on 'Done' button
Encode object features seek next object
Yes No
Attend click object
Encode object features request retrieval
Blended location retrieved,
attend to location
Relocate object seek next object
Relocation
Presentation
Current phase?
Unattended object?
Yes No
Unattended object?
David Peebles (University of Hudders

49. eld) Biases and visualisations November 8, 2014 14 / 16

50. Adaptation as a source of bias
Adoption and adaptation determined by:
Cost structure of the task environment (i.e., how quick/easy is it to
execute the task).
The representational eciency of the visualisation.
History of success with the strategy.
Adaptation typically beneath conscious awareness and deliberate
control.
Do users always adapt to an optimal interaction?
Not necessarily { only when local (interactive routine level) optimum
coincides with global task level optimum (Fu Gray, 2004).
Suboptimal interaction and interpretation may result from strategy
selection pressures resulting from unconscious choices made at the
embodiment level.
David Peebles (University of Hudders

51. eld) Biases and visualisations November 8, 2014 15 / 16

52. Summary
Interpretation of data graphics can be shaped/biased by
number of factors:
Visual and computational properties of the representation.
Adaptive behaviour of the user seeking to minimise eort.
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 are
being developed.
Vital for design of most appropriate representations/task
environments for dierent users and tasks and to reduce error.
Important role for cognitive science theories and methods in
this research (e.g., computational cognitive modelling)
David Peebles (University of Hudders

53. eld) Biases and visualisations November 8, 2014 16 / 16