Cognitive Models

1. Cognitive Models Material from Authors of Human Computer Interaction Alan Dix, et al

2. Overview Cognitive models represent users of interactive systems • hierarchical - user’s task and goal structure • linguistic – user-system grammar • physical and device – human motor skills • architectural – underlie all of above

3. Cognitive models • They model aspects of user as they interact: • understanding • knowledge • intentions • processing • Common categorization: • Competence – represent kinds of behavior expected of user • Performance – allow analysis of routine behavior in limited applications

4. Goal and task hierarchies Solve goals by solving subgoals - Mental processing as “divide-and-conquer” produce report gather data . find book names . . do keywords search of names database …further sub-goals . . sift through names and abstracts by hand …further sub-goals . search sales database ..further sub-goals layout tables and histograms ..further sub-goals write description ..further sub-goals

5. Issues for goal hierarchies • Granularity • Where do we start? • Where do we stop – how far to subdivide? • Get down to a routine learned behavior, not problem solving - the unit task • Conflict • More than one way to achieve a goal • Treatment of error

6. Techniques • Goals, Operators, Methods and Selection (GOMS) • Cognitive Complexity Theory (CCT) • can represent error behavior

7. GOMS • Goals - what the user wants to achieve • Operators- basic actions user performs (granularity) • Methods - decomposition of a goal into sub goals/operators • may be more than one way or method to do that • Selection - means of choosing between competing methods (GOMS attempts to predict)

8. GOMS example GOAL: ICONIZE-WINDOW [select GOAL: USE-CLOSE-METHOD MOVE-MOUSE-TO-WINDOW-HEADER POP-UP-MENU CLICK-OVER-CLOSE-OPTION GOAL: USE-L7-METHOD PRESS-L7-KEY] For a particular user Sam: Rule 1: Select USE-CLOSE-METHOD unless another rule applies. Rule 2: If the application is GAME, select L7-METHOD.

9. GOMS as a measure of performance • selection rules can be tested for accuracy against user traces • stacking depth of goal structure can estimate STM requirements • good for describing how experts perform routine tasks • not for comparing across tasks • not for predicting training time

10. Cognitive Complexity Theory - CCT • basic premises of goal decomposition • provides more predictive power Two parallel descriptions: • User - production rules of the form: if condition then action • Device - generalized transition networks covered under dialogue models

11. Example: editing with vi Production rules are in long-term memory - 4 rules in the text on page 425 User sees a mistake - Model contents of working memory as attribute-value mapping (GOAL perform unit task (TEXT task is insert space) (TEXT task is at 5 23) (CURSOR 8 7)

12. Example: editing with vi Rules are pattern-matched to working memory, e.g., LOOK-TEXT task is at %LINE %COLUMN is true, with LINE = 5 COLUMN = 23. Four rules model inserting a space –1st one only one that can fire: SELECT-INSERT-SPACE //bind to location INSERT-SPACE-DONE//finished - unbind INSERT-SPACE-1 //move cursor INSERT-SPACE-2 //hit insert key and space

13. Example: editing with vi When fired, binds the LINE and COL to 5 and 23 respectively and adds to working memory (GOAL insert space) (NOTE executing insert space) (LINE 5) (COLUMN 23) NowINSERT-SPACE-1 will fire

14. Notes on CCT • Rules don’t fire in order written, may repeat • Parallel model – rules can fire simultaneously • Novice versus expert style rules • Error behavior can be represented • Measures • Depth of goal structure • Number of rules (more means interface more difficult to learn) • Comparison with device description

15. Problems with goal hierarchies • description can be enormous • a post hoc technique – risk is that it is defined by the computer dialog and not user • expert versus novice • Simple extensions possible • goal closure (makes sure subgoal satisfied) • eg. ATM example

16. Linguistic notations • User’s interaction with a computer is often viewed in terms of a language. • Backus-Naur Form (BNF) • Task-Action Grammar (TAG)

17. BNF • Very common notation from computer science • A purely syntactic view of the dialogue Basic syntax: nonterminal ::= expression An expression contains terminals and nonterminals combined in sequence (+) or as alternatives (|). Terminals lowest level of user behavior CLICK-MOUSE, MOVE-MOUSE Nonterminals ordering of terminals; higher level of abstraction select-menu, position-mouse

18. draw line ::= select line + choose points + last point select line ::= pos mouse + CLICK MOUSE choose points ::= choose one | choose one + choose points choose one ::= pos mouse + CLICK MOUSE last point ::= pos mouse + DBL CLICK MOUSE pos mouse ::= NULL | MOVE MOUSE + pos mouse

19. Measurements with BNF • Number of rules or number of + and | operators • Complications • same syntax for different semantics • reflects user’s actions, not user's perception of system responses • enforcement of consistency in rules • Extensions • include “information-seeking actions” in grammar • parameterized grammar rules

20. Task-Action Grammar - TAG • Making consistency in language more explicit than in BNF • Encoding user's world knowledge • (eg. up is opposite of down) • Accomplished by • Parameterized grammar rules • Nonterminals are modified to include additional semantic features

21. Consistency in TAG In BNF, three UNIX commands would be described as copy ::= cp + filename + filename | cp + filenames + directory move ::= mv + filename + filename | mv + filenames + directory link ::= ln + filename + filename | ln + filenames + directory

22. Consistency in TAG • In TAG, this consistency of argument order can be made explicit using a parameter, or semantic feature for file operations.

23. file op[Op] ::= command[Op]+ filename + filename | command[Op]+ filenames + directory command[Op = copy] ::= cp command[Op = move] ::= mv command[Op = link] ::= ln

24. Notes • Ignore system output • (there are extensions to BNF and TAG) • Hierarchical and grammar-based techniques initially developed when systems were mostly command-line or keyboard and cursor based.

25. Physical and device models • Based on empirical knowledge of human motor system • User's task: acquisition, then execution. • These models only address execution • Models are complementary with goal hierarchies • Models • The Keystroke Level Model (KLM) • Buxton's 3-state model

26. Keystroke Level Model - KLM Six execution phase operators Physical motor K keystroking P pointing H homing D drawing Mental M mental preparation System R response Times are empirically determined. Texecute = TK + TP + TH + TD + TM + TR

27. Example GOAL: ICONISE-WINDOW [select GOAL: USE-CLOSE-METHOD MOVE-MOUSE-TO-WINDOW-HEADER POP-UP-MENU CLICK-OVER-CLOSE-OPTION GOAL: USE-L7-METHOD PRESS-L7-KEY]

28. Models so far GOMS – cognitive processing involved in deriving subgoals to carry out a task to achieve a goal CCT – distinction between LTM (rules) and STM (working memeory) Linguistic (BNF and TAG) – focus on syntactic KLM – motor and mental operators

29. Architectural models All of cognitive models make assumptions about the architecture of the human mind. • Problem spaces – behavior viewed as sequence of agent/environment states (can predict erroneous behavior) • Interacting Cognitive Subsystems • provides model of perception, cognition, and action • 9 subsystems (5 physical, 4 mental) • view of user as information processing machine • concerned with determining how easy particular procedures of action sequences become

30. Last notes • Cognitive models attempt to represent users as they interact with the system • Three categories – what were they? • Most cognitive models do not deal with user observation and perception. • Some techniques have been extended to handle system output, but problems persist. • Issues: • Level of granularity • Exploratory interaction versus planning