Introduction
Previous lecture introduced production rule system models:
HCI Lecture 9:
Cognitive Models II
Decompose task goals into methods consisting of operations
Express as IF-THEN rules that determine what the user will do,
depending on their working memory content
Include some co-ordination/selection mechanism
Run the user model to make predictions about the interface
Barbara Webb
These have several limitations, in particular:
Require complex hand design of large rule sets even for simple
Key points:
tasks (e.g. close a window, find a menu item)
CPM-GOMS
Situated cognition & EPIC
High level models
Models user as a disembodied, rational, sequential actor
Three examples of recent work that address some of these
issues:
Parallel processes using CPM-GOMS, automated in APEX
EPIC sensorimotor models
High level formalism for ‘cognitively plausible’ behaviour
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CPM-GOMS
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CPM-GOMS
Recall the ‘Model
Expressed as a PERT chart, e.g. “slow move click”
Human Processor’
Cognitive, Perceptual,
Motor (CPM-) GOMS
recognises that the
subsystems can work
in parallel, subject to
constraints of
information flow
Total time to do a task
will depend on these
interdependencies
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CPM-GOMS
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CPM-GOMS + APEX
Still difficult to do by hand, particularly if want to interlace
Can store common actions (like mouse click) as templates
For higher level task, can link together series of templates
subtasks (i.e. allow later tasks to use free resources)
showing interdependencies
Can then calculate total time for ‘critical path’
Automated approach (John et al 2002) comes from using
reactive planner architecture APEX
Limited parallel resources (e.g. perceptual, motor, cognitive)
Procedure description language (= methods & operations)
Selection operator
Specify preconditions (task A must complete before task B can
start) or priority (if task C and D are competing for same resource)
APEX then generates the sequencing, with appropriate
interleaving
Dynamic task scheduling, i.e. can respond to environmental change
Precoded low level procedures can be called by high level (the
designer does not need to know the psychological details)
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Example: ATM withdrawal
Example: ATM withdrawal
Models fits well with actual time data (for practiced performers)
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Situated cognition
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EPIC
Having the right representation of a task or problem can allow
EPIC architecture includes some sensorimotor detail
direct solution by our perceptual system
Cognition is not just ‘in the head’ but depends on our sensory
and motor interfaces
‘Situated’, ‘embodied’ and ‘distributed’ cognition
Standard cognitive models tend to assume categories (‘target’,
‘card slot’) but “what we perceive as properties and events is
constructed in the course of coordinated activity” Clancy 1994
I.e. need physical and social experience of cards and slots to
recognise a card slot
Suggests cognitive models in HCI need to include at least some
details (beyond time lags) of our sensor and motor interfaces
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EPIC
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EPIC
For visual processing, models field of view and eye movements;
Knowledge of different object properties depends on location of
Used to model visual search for different layouts (n.b. guided
vs. bottom-up attention) and compared to eye-tracking results
(Hornof & Halverson, 2003)
target (e.g. only know text content in fovea)
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Formalising cognitively plausible behaviour
EPIC
Results supply concrete
Do cognitive models have to include more and more detail to be
justification for a number of layout
recommendations:
People use hierarchy and will focus on
one level at a time: so support search
with salient labels and ease of eye
movements between them, e.g. by
use of white space
Can examine more than one item in a
fixation: hence use vertical lists and
left justify
Ocular motor system is primed for (or
even anticipates) response to visual
onset: added reason why slow
response times are annoying for
users.
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useful for HCI?
“The best model of a cat is another, or preferably the same, cat”
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Formalising cognitively plausible behaviour
Non-deterministic temporally guarded action rules
Provide set of plausible behaviours that might be executed next
Several types, e.g.:
(lack of feedback)
No option termination: interaction finished if apparently nothing can
Omission – termination of behaviour after goal achieved but without
communication goal (insert money first or make choice first?)
subtask completion (have ticket, but forget to retrieve credit card)
do next (e.g. if machine doesn’t say to wait)
(e.g. put coins in visible slot)
Communication goal behaviour: knowledge of subgoals means will
perform behaviour to achieve them given opportunity
Mental triggers: commitment to performing a behaviour that cannot
be revoked
They also suggest design rules:
Make sure goal is not achieved till entire task complete
Either use forcing functions (do not give reactive cue or opportunity
to discharge communication goal except at correct moment OR be
permissive about the ordering of events.
Give immediate feedback to prevent repetition or no option
termination
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Summary
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References
Gray & Boehm-Davis (2000) Milliseconds matter: an introduction
to microstrategies and to their use in describing and predicting
interactive behavior. Journal of Experimental Psychology:
Applied, 6: 322-335
John, B.E. et al. (2002) Automating CPM-GOMS. Proceedings of
CHI 2002.
Clancy, W.J. (1997) Situated cognition. Cambridge University Press
(or see short paper at http://cogprints.org/661/0/133.htm)
Hornof, A. & Halverson, T. (2003) Cognitive strategies and eye
movements for searching hierarchical computer displays. ASM
CHI 2003: Conference on Human Factors in Computing
Systems, 249-256
Curzon, P. & Blandford, A. (2002) From a formal user model to
design rules. Lecture Notes in Computer Science, 2545:1-15.
See also:
Dix et. al. sections 1.4, 5.7,12.6
Cognitive models should provide theoretical foundations for HCI
decisions
From level of comparing small changes in specific systems
To level of justifying generic design principles
In this lecture have discussed several advances that aim to:
Go beyond simple production rule systems to include parallel,
embodied, non-deterministic features of human action
Repetition – if device continues to provide cue for reactive response
Reversing order – in response to opportunity to discharge
Reactive: stimulus or message from artifact prompts behaviour
Combined, these predict several varieties of error, e.g.
Termination: if goal achieved, interaction is finished (e.g. if have
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Formalising cognitively plausible behaviour
ticket, leave the machine)
Alternative approach (Curzon and Blandford, 2002) provides
simple, formal descriptions of some generic characteristics of
user behaviour
Potential for providing direct proofs for some design rules
Make the models themselves more usable
However:
There is still much we don’t know about human cognition, and any
model can only include a small part of what we do know
Heuristic, historic or pragmatic principles and guidelines still
dominate HCI design
Evaluation with real users is still essential
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