Cognitive models unit 3
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Cognitive models aim to model aspects of how users think and process information. There are different types of cognitive models, including goal and task hierarchies which represent mental processing as dividing complex problems into sub-goals and sub-tasks, and linguistic models which analyze language between users and systems. Physical and device models complement goal hierarchies by focusing on the execution of tasks based on empirical knowledge of human motor abilities.
![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 unless another
rule applies
Rule 2: If the application is GAME,
select CTRL-W-METHOD
ARULKUMAR V AP/CSE SECE](https://image.slidesharecdn.com/cognitivemodelsunit3-171219094645/85/Cognitive-models-unit-3-9-320.jpg)
![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
ARULKUMAR V AP/CSE SECE](https://image.slidesharecdn.com/cognitivemodelsunit3-171219094645/85/Cognitive-models-unit-3-21-320.jpg)
![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
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
USE-CTRL-W-METHOD
H[to kbd] 0.40
M 1.35
K[ctrlW key] 0.28
Total 2.03 s
ARULKUMAR V AP/CSE SECE](https://image.slidesharecdn.com/cognitivemodelsunit3-171219094645/85/Cognitive-models-unit-3-25-320.jpg)
Cognitive models unit 3
- 1. chapter 12 cognitive models ARULKUMAR V AP/CSE SECE
- 2. Cognitive models • goal and task hierarchies • linguistic • physical and device • architectural ARULKUMAR V AP/CSE SECE
- 3. Cognitive models • They model aspects of user: – understanding – knowledge – intentions – processing • Common categorisation: – Competence vs. Performance – Computational flavour – No clear divide ARULKUMAR V AP/CSE SECE
- 4. 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 ARULKUMAR V AP/CSE SECE
- 5. 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 ARULKUMAR V AP/CSE SECE
- 6. 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 ARULKUMAR V AP/CSE SECE
- 7. Techniques • Goals, Operators, Methods and Selection (GOMS) • Cognitive Complexity Theory (CCT) • Hierarchical Task Analysis (HTA) - Chapter 15 ARULKUMAR V AP/CSE SECE
- 8. 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 ARULKUMAR V AP/CSE SECE
- 9. 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 unless another rule applies Rule 2: If the application is GAME, select CTRL-W-METHOD ARULKUMAR V AP/CSE SECE
- 10. Cognitive Complexity Theory • 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 ARULKUMAR V AP/CSE SECE
- 11. 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 %COLUMN is true, with LINE = 5 COLUMN = 23. ARULKUMAR V AP/CSE SECE
- 12. 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)))ARULKUMAR V AP/CSE SECE
- 13. 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 ARULKUMAR V AP/CSE SECE
- 14. Problems with goal hierarchies • a post hoc technique • expert versus novice • How cognitive are they? ARULKUMAR V AP/CSE SECE
- 15. 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) ARULKUMAR V AP/CSE SECE
- 16. 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 ARULKUMAR V AP/CSE SECE
- 17. 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 ARULKUMAR V AP/CSE SECE
- 18. 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 ARULKUMAR V AP/CSE SECE
- 19. Task Action Grammar (TAG) • Making consistency more explicit • Encoding user's world knowledge • Parameterised grammar rules • Nonterminals are modified to include additional semantic features ARULKUMAR V AP/CSE SECE
- 20. 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 ARULKUMAR V AP/CSE SECE
- 21. 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 ARULKUMAR V AP/CSE SECE
- 22. Other uses of TAG • User’s existing knowledge • Congruence between features and commands • These are modelled as derived rules ARULKUMAR V AP/CSE SECE
- 23. Physical and device models • The Keystroke Level Model (KLM) • Buxton's 3-state model • Based on empirical knowledge of human motor system • User's task: acquisition then execution. – these only address execution • Complementary with goal hierarchies ARULKUMAR V AP/CSE SECE
- 24. 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 ARULKUMAR V AP/CSE SECE
- 25. 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 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 USE-CTRL-W-METHOD H[to kbd] 0.40 M 1.35 K[ctrlW key] 0.28 Total 2.03 s ARULKUMAR V AP/CSE SECE
- 26. 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 ARULKUMAR V AP/CSE SECE
- 27. 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 ARULKUMAR V AP/CSE SECE