cognitive models material from authors of human computer interaction alan dix, et al
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Cognitive Models
Material from
Authors of Human Computer Interaction
Alan Dix, et al
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
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
Goal and task hierarchies
Solve goals by solving subgoals- Mental processing as “divide-and-conquer”
produce reportgather 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
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
Techniques
Goals, Operators, Methods and Selection (GOMS)
Cognitive Complexity Theory (CCT) can represent error behavior
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)
GOMS example
GOAL: ICONIZE-WINDOW[select
GOAL: USE-CLOSE-METHODMOVE-MOUSE-TO-WINDOW-HEADERPOP-UP-MENUCLICK-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.
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
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
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 –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
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
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
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
Linguistic notations
User’s interaction with a computer is often viewed in terms of a language.
Backus-Naur Form (BNF) Task-Action Grammar (TAG)
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
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
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
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
Consistency in TAG
In BNF, three UNIX commands would be
described ascopy ::= cp + filename + filename
| cp + filenames + directory
move ::= mv + filename + filename
| mv + filenames + directory
link ::= ln + filename + filename
| ln + filenames + directory
Consistency in TAG
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 + filename | command[Op]+ filenames + directory
command[Op = copy] ::= cp
command[Op = move] ::= mv
command[Op = link] ::= ln
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.
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
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
ExampleGOAL: ICONISE-WINDOW
[selectGOAL: USE-CLOSE-METHOD
MOVE-MOUSE-TO-WINDOW-HEADER
POP-UP-MENUCLICK-OVER-CLOSE-OPTION
GOAL: USE-L7-METHODPRESS-L7-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 syntacticKLM – motor and mental operators
Architectural models
All of cognitive models make assumptionsabout 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
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