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chapter 12

chapter 12. cognitive models. Cognitive models. goal and task hierarchies linguistic physical and device architectural. Cognitive models. Cognitive models represent users of interactive systems. Hierarchical models represent a user's task and goal structure.

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chapter 12

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  1. chapter 12 cognitive models

  2. Cognitive models • goal and task hierarchies • linguistic • physical and device • architectural

  3. Cognitive models • Cognitive models represent users of interactive systems. • Hierarchical models represent a user's task and goal structure. • Linguistic models represent the user-system grammar. • Physical and device models represent human motor skills. • Cognitive architectures underlie all of these cognitive models.

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

  5. 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

  6. goals vs. tasks • goals – intentions what you would like to be true • tasks – actions how to achieve it • GOMS – goals are internal • HTA – actions external – tasks are abstractions

  7. 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

  8. Techniques • Goals, Operators, Methods and Selection (GOMS) • Cognitive Complexity Theory (CCT) • Hierarchical Task Analysis (HTA) - Chapter 15

  9. 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

  10. GOMS example • GOAL: CLOSE-WINDOW • . [select GOAL: USE-MENU-METHOD • . MOVE-MOUSE-TO-FILE-MENU • . PULL-DOWN-FILE-MENU • . CLICK-OVER-CLOSE-OPTION • GOAL: USE-CTRL-W-METHOD • . PRESS-CONTROL-W-KEYS] • For a particular user: • Rule 1: Select USE-MENU-METHOD unlessanother • rule applies • Rule 2: If the application is GAME, • select CTRL-W-METHOD

  11. How to do GOMS Analysis • Generate task description • pick high-level user Goal • write Method for accomplishing Goal - may invoke subgoals • write Methods for subgoals • this is recursive • stops when Operators are reached • Evaluate description of task • Apply results to UI • Iterate!

  12. Example - DOS • Goal: Delete a File • Method for accomplishing goal of deleting a file • retrieve from Long term memory that command verb is “del” • think of directory name & file name and make it the first listed parameter • accomplish goal of entering & executing command • return with goal accomplished

  13. Example - Mac • Goal: Delete a File • Method for accomplishing goal of deleting a file • find file icon • accomplish goal of dragging file to trash • Return with goal accomplished

  14. Advantages of GOMS • Gives qualitative & quantitative measures • Model explains the results • Less work than user study • Easy to modify when UI is revised • Research: tools to aid modeling process since it can still be tedious

  15. Disadvantages of GOMS • Not as easy as HE, guidelines, etc. • Takes lots of time, skill, & effort • Only works for goal-directed tasks • Assumes tasks performed by experts without error • Does not address several UI issues, • readability, memorizability of icons, commands

  16. Automated GOMS Tools • Can save, modify and re-use the model • Automation of goal hierarchy, method, selection rule creation

  17. QGOMS tool

  18. Cognitive Complexity Theory • Two parallel descriptions: • User production rules • Device generalised transition networks • GOMS like hierarchy expressed in production rules • Production rules are of the form: • if condition then action • Transition networks covered under dialogue models

  19. Example: editing with vi • Production rules are in long-term memory • Model 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 %COLUMNis true, with LINE = 5 COLUMN = 23.

  20. Four rules to model inserting a space Active rules: SELECT-INSERT-SPACE INSERT-SPACE-MOVE-FIRST INSERT-SPACE-DOIT INSERT-SPACE-DONE 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)))

  21. 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

  22. Why CCT? • CCT analyzed to discuss proceduralization and error behavior • Related to GOMS-like goal hierarchies • Able to measure complexity of interface • More production rules = more difficult to learn interface • Proportional to # of rules you to learn • Problem closure • No higher level goal should be satisfied until all subgoals have been satisfied • Not easy to predict

  23. Problems with goal hierarchies • a post hoc technique • Produce a goal structure based on pre-existing manual procedures to obtain a more natural hierarchy • expert versus novice • How cognitive are they?

  24. 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)

  25. Backus-Naur Form (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

  26. 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

  27. 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

  28. Task Action Grammar (TAG) • Making consistency more explicit • Encoding user's world knowledge • Parameterised grammar rules • Nonterminals are modified to include additional semantic features

  29. 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

  30. 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

  31. Other uses of TAG • User’s existing knowledge • Congruence between features and commands • These are modelled as derived rules

  32. Physical and device models • The Keystroke Level Model (KLM) • Buxton's 3-state model • Bill Buxton.com • Bill Buxton at Microsoft • Perspectives on Design • Based on empirical knowledge of human motor system • User's task: acquisition then execution. • these only address execution • Complementary with goal hierarchies

  33. Keystroke Level Model (KLM) • lowest level of (original) GOMS • 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

  34. USE-CTRL-W-METHOD H[to kbd] 0.40 M 1.35 K[ctrlW key] 0.28 Total 2.03 s USE-CLOSE-METHOD P[to menu] 1.1 B[LEFT down] 0.1 M 1.35 P[to option] 1.1 B[LEFT up] 0.1 Total 3.75 s KLM example • GOAL: ICONISE-WINDOW • [select • GOAL: USE-CLOSE-METHOD • . MOVE-MOUSE-TO- FILE-MENU • . PULL-DOWN-FILE-MENU • . CLICK-OVER-CLOSE-OPTION • GOAL: USE-CTRL-W-METHOD • PRESS-CONTROL-W-KEY] • compare alternatives: • USE-CTRL-W-METHOD vs. • USE-CLOSE-METHOD • assume hand starts on mouse

  35. 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

  36. 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 • Exploratory interaction versus planning

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