cognitive models material from authors of human computer interaction alan dix, et al

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