human computer Interaction cognitive models.ppt

HCI

1. Chapter 12
Cognitive Models

2. Overview
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.

3. Cognitive models
Theymodelsomeaspectsoftheuser’s:
i.understanding
ii.knowledge
iii.intentions
iv.processing
Toclassifythemodel:
–Competencevs.Performance
Competence modelscanpredictlegalbehaviour
sequences
Performancemodelsdescribewhattheuserneedsto
knowandhowthisisemployedinactualtask
execution

4. Goal and task hierarchies
Many models make use of a model of mental processing in which the
user achieves goals by solving sub goals in a divide-and-conquer
fashion.
We will consider two models, GOMS(Goals, Operators, Methods and
Selection )and CCT (Cognitive Complexity Theory) , where this is a
central feature.

5. Example: Sales of introductory HCI
textbooks
•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. Issues for goal hierarchies
•Granularity
–Where do we start?
–Where do we stop?
•Conflict
–More than one way to achieve a goal
•Error

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

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

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 unlessanother
rule applies
Rule 2: If the application is GAME,
select CTRL-W-METHOD

10. How to do GOMS Analysis
•Generate task description
–pick high-level user Goal
–write Methodfor accomplishing Goal -may invoke
subgoals
–write Methodsfor subgoals
•this is recursive
•stops when Operatorsare reached
•Evaluate description of task
•Apply results to UI
•Iterate!

11. 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

12. 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

13. Advantages of GOMS
•Gives qualitative & quantitative
measures
•Model explains the results
•Less work than user study
•Easy to modify when UI is revised

14. Disadvantages of GOMS
•Takes lots of time, skill, & effort
•Only works for goal-directed tasks
•Assumes tasks performed by experts
withouterror
•Does not address several UI issues,
–readability, memorizability of icons,
commands

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

16. QGOMStool

20. Cognitive Complexity Theory
•Twoparalleldescriptions:
1.Userproductionrules
GOMSlikehierarchyexpressedinproductionrules
Productionrules(sequenceofrules)areoftheform:
ifconditionthenaction
–whereconditionisastatementaboutthecontentsof
workingmemory.
–Anactionmayconsistofoneormoreelementaryactions,
whichmaybeeitherchangestotheworkingmemory,or
externalactionssuchaskeystrokes.Theproductionrule
‘program’iswritteninaLISP-likelanguage.
2.Devicegeneralisedtransitionnetworks
Forthesystemgrammer,CCT usesgeneralisedtransition
networks

21. Example: editing with vi
•Thetaskistoinsertaspacewhereone
hasbeenmissedoutinthetext,
‘cognitivecomplexitytheory’.
•Thisisareasonablyfrequenttyping
error.
•Weconsiderafragmentofthe
associatedCCTproductionrules.

23. •Toseehowtheseruleswork,imaginethattheuserhasjust
seenthetypingmistakeandthusthecontentsofworking
memory(w.m.)are
(GOALperformunittask)
(TEXTtaskisinsertspace)
(TEXTtaskisat523)
(CURSOR87)
•TEXTreferstothetextofthemanuscriptthatisbeing
edited
•CURSORreferstotheinsertioncursoronthescreen.
•Thelocation(5,23)isthelineandcolumnofthetyping
mistakewherethespaceisrequired.However,thecurrent
cursorpositionisatline8andcolumn7.

24. 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 perform unit task)
(TEXT task is insert space)
(TEXT task is at 5 23)
(NOTE executing insert space)
(GOAL insert space)
(LINE 5)
(COL 23)
(CURSOR 8 7)
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)))

25. Notes on CCT
•Proceduralisationofactions
•Setofstylerules(limittheconditionand
actionsintheproductionrule)fornovices
•TherulesinCCTneednotrepresenterror-free
performance.Theycanbeusedtoexplain
errorphenomena,thoughtheycannotpredict
them.
•Measures
–depthofgoalstructure
–numberofrules

26. Why CCT?
•CCTanalyzed to discuss issues of
proceduralization and error behavior
•Related to GOMS-like goal hierarchies
•Main aim is to be able to measure complexity
of interface
•The more production rules in the CCT
description the more difficult the interface is to
learn
•Problem closure
–No higher level goal should be satisfied until all
subgoals have been satisfied
–Not easy to predict

27. Linguistic notations
•Understanding the user's behaviour and
cognitive difficulty based on analysis of
language between user and system.
1. Backus–Naur Form (BNF)
2. Task–Action Grammar (TAG)

28. 1. Backus-Naur Form (BNF)
•BNF has been used widely to specify the syntax of computer
programming languages
•A purely syntactic view of the dialogue
The names in the description are of two types
•Terminals
–lowest level of user behaviour
–such as pressing a key, clicking a mouse button or
moving the mouse.
•Nonterminals
–higher level of abstraction
–e.g. select-menu, position-mouse

29. 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

30. Measurements with BNF
•The BNFdescription of an interface can be analyzed in
various ways.
–count the number of rules
–counts the number of ‘+’ and ‘|’ operators
•Complications
–same syntax for different semantic
–not the user’s perception of the system’s responses
–minimal consistency checking

31. 2. Task Action Grammar (TAG)
•Toemphasizeconsistencyandencodingtheuser’sworld
knowledge.
ConsistencyinTAG
•Toillustrateconsistency,wehavethreeUNIXcommands
wouldbedescribedas:
copy ::=cp+filename+filename|cp+filenames+
directory
move ::=mv+filename+filename|mv+filenames
+directory
link::=ln+filename+filename|ln+filenames+directory
•MeasuresbaseduponBNFcouldnotdistinguishbetween
theseconsistentcommandsandaninconsistentalternative–
sayiflntookitsdirectoryargumentfirst
link ::=ln+filename+filename |ln+
directory+filenames

32. 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

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

34. Physical and device models
•KLM(Keystroke-LevelModel)usestheunderstanding
asabasisfordetailedpredictionsaboutuser
performance.
•Itisaimedatunittaskswithininteraction–the
executionofsimplecommand sequences,typically
takingnomorethan20seconds.Examplesofthis
wouldbeusingasearchandreplacefeature,or
changingthefontofaword.
•KLMisrelatedtotheGOMSmodel.Morecomplextasks
wouldbesplitintosubtasks(asinGOMS)beforethe
userattemptstomapthemintophysicalactions.The
taskissplitintotwophases:
–acquisitionofthetask(howtoaccomplishthe
task),whentheuserbuildsamentalrepresentation
ofthetask;
–executionofthetask(useriseffectivelyexpert)
usingthesystem’sfacilities.

35. •Themodeldecomposestheexecutionphaseintofivedifferent
physicalmotoroperators,amentaloperatorandasystem
responseoperator:
–KKeystroking,actuallystrikingkeys,includingshiftsand
othermodifierkeys.
–BPressingamousebutton.
–PPointing,movingthemouse(orsimilardevice)ata
target.
–HHoming,switchingthehandbetweenmouseand
keyboard.
–DDrawinglinesusingthemouse.
–MMentallypreparingforaphysicalaction.
–RSystemresponsewhichmaybeignorediftheuserdoes
nothavetowaitforit,asincopytyping.
•Themodelpredictsthetotaltimetakenduringtheexecution
phasebyaddingthecomponenttimesforeachoftheabove
activities.Forexample,ifthetimetakenforonekeystrokeis
tk,thenthetotaltimedoingkeystrokesis

37. 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-METHODvs.
•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

40. Three-state model
•Buxtonhasdevelopedasimplemodelofinputdevices,thethree-
statemodel,whichcapturessomeofthesecrucialdistinctions.
•Hebeginsbylookingatamouse.Ifyoumoveitwithnobuttons
pushed,itnormallymovesthemousecursorabout.
•Thistrackingbehaviouristermedstate1.Depressingabuttonover
aniconandthenmovingthemousewilloftenresultinanobject
beingdraggedabout.Thishecallsstate2.