chap6

Slide1: 

goal and task hierarchies linguistic physical and device architectural Cognitive models

Slide2: 

Cognitive models They model aspects of user: understanding knowledge intentions processing Common categorisation: Competence Performance Computational flavour No clear divide

Slide3: 

Goal and task hierarchies Mental processing as divide-and-conquer Example: sales report 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

Slide4: 

Issues for goal hierarchies Granularity Where do we start? Where do we stop? Routine learned behaviour, not problem solving The unit task Conflict More than one way to achieve a goal Error

Slide5: 

Techniques Goals, Operators, Methods and Selection (GOMS) Cognitive Complexity Theory (CCT) Hierarchical Task Analysis (HTA) - Chapter 7

Slide6: 

GOMS Goals what the user wants to achieve Operators basic actions user performs Methods decomposition of a goal into subgoals/operators Selection means of choosing between competing methods

Slide7: 

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] For a particular user: Rule 1: Select USE-CLOSE-METHOD unless another rule applies Rule 2: If the application is GAME, select L7-METHOD GOMS example

Slide8: 

CCT Two parallel descriptions: User production rules Device generalised transition networks Production rules are of the form: if condition then action Transition networks covered under dialogue models

Slide9: 

Example: editing with vi Production rules are in long-term memory 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) 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 to model inserting a space: 

Active rules: SELECT-INSERT-SPACE INSERT-SPACE-MOVE-FIRST INSERT-SPACE-DOIT INSERT-SPACE-DONE Four rules to model inserting a space New working memory (GOAL insert space) (NOTE executing insert space) (LINE 5) (COLUMN 23) SELECT-INSERT-SPACE matches current working memory (SELECT-INSERT-SPACE IF (AND (TEST-GOAL perform unit task) (TEST-TEXT task is insert space) (NOT (TEST-GOAL insert space)) (NOT (TEST-NOTE executing insert space))) THEN ( (ADD-GOAL insert space) (ADD-NOTE executing insert space) (LOOK-TEXT task is at %LINE %COLUMN)))

Slide11: 

Notes on CCT Parallel model Proceduralisation of actions Novice versus expert style rules Error behaviour can be represented Measures depth of goal structure number of rules comparison with device description

Slide12: 

Problems with goal hierarchies a post hoc technique expert versus novice How cognitive are they?

Slide13: 

Linguistic notations Understanding the user's behaviour and cognitive difficulty based on analysis of language between user and system. Similar in emphasis to dialogue models Backus-Naur Form (BNF) Task-Action Grammar (TAG)

Slide14: 

BNF Very common notation from computer science A purely syntactic view of the dialogue Terminals lowest level of user behaviour e.g. CLICK-MOUSE, MOVE-MOUSE Nonterminals ordering of terminals higher level of abstraction e.g. select-menu, position-mouse

Slide15: 

Example of BNF Basic syntax: nonterminal ::= expression An expression contains terminals and nonterminals combined in sequence (+) or as alternatives (|) 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

Slide16: 

Measurements with BNF Number of rules (not so good) Number of + and | operators Complications same syntax for different semantics no reflection of user's perception minimal consistency checking

Slide17: 

TAG Making consistency more explicit Encoding user's world knowledge Parameterised grammar rules Nonterminals are modified to include additional semantic features

Slide18: 

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 No BNF measure could distinguish between this and a less consistent grammar in which link ::= ln + filename + filename | ln + directory + filenames

Slide19: 

Consistency in TAG (cont'd) consistency of argument order made explicit using a parameter, or semantic feature for file operations Feature Possible values Op = copy; move; link Rules file-op[Op] ::= command[Op] + filename + filename | command[Op] + filenames + directory command[Op = copy] ::= cp command[Op = move] ::= mv command[Op = link] ::= ln

Slide20: 

Other uses of TAG Users existing knowledge Congruence between features and commands These are modeled as derived rules

Slide21: 

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

Slide22: 

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

Slide23: 

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] assume hand starts on mouse

Slide24: 

Architectural models All of these cognitive models make assumptions about the architecture of the human mind. Long-term/Short-term memory Problem spaces Interacting Cognitive Subsystems Connectionist ACT

Slide25: 

Display-based interaction Most cognitive models do not deal with user observation and perception. Some techniques have been extended to handle system output (e.g., BNF with sensing terminals, Display-TAG) but problems persist. Level of granularity Exploratory interaction versus planning