Cognitive Models Material from Authors of Human Computer

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

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

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

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

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

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

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

GOMS example GOAL: ICONIZE-WINDOW [select GOAL: USE-CLOSE-METHOD MOVE-MOUSE-TO-WINDOW-HEADER POP-UP-MENU CLICK-OVER-CLOSE-OPTION GOAL: USE-L 7 -METHOD PRESS-L 7 -KEY] For a particular user Sam: Rule 1: Select USE-CLOSE-METHOD unless another rule applies. Rule 2: If the application is GAME, select L 7 -METHOD.

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

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

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)

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 – 1 st one only one that can fire: SELECT-INSERT-SPACE-DONE INSERT-SPACE-1 INSERT-SPACE-2 //bind to location //finished - unbind //move cursor //hit insert key and space

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) Now INSERT-SPACE-1 will fire

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

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

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

BNF n Very common notation from computer science n 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

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

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

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

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

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

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

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

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

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

Example GOAL: ICONISE-WINDOW [select GOAL: USE-CLOSE-METHOD MOVE-MOUSE-TO-WINDOW-HEADER POP-UP-MENU CLICK-OVER-CLOSE-OPTION GOAL: USE-L 7 -METHOD PRESS-L 7 -KEY]

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

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

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