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Models of Human Performance. CSCI 4800 Spring 2006 Kraemer. Objectives. Introduce theory-based models for predicting human performance Introduce competence-based models for assessing cognitive activity Relate modelling to interactive systems design and evaluation.

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Models of human performance l.jpg

Models of Human Performance

CSCI 4800

Spring 2006


Objectives l.jpg

  • Introduce theory-based models for predicting human performance

  • Introduce competence-based models for assessing cognitive activity

  • Relate modelling to interactive systems design and evaluation

Seven stage action model norman 1990 l.jpg
Seven Stage Action Model[Norman, 1990]


Describing problem solving l.jpg
Describing Problem Solving

  • Initial State

  • Goal State

  • All possible intervening states

    • Problem Space

  • Path Constraints

  • State Action Tree

  • Means-ends analysis

Problem solving l.jpg
Problem Solving

  • A problem is something that doesn’t solve easily

  • A problem doesn’t solve easily because:

    • you don’t have the necessary knowledge or,

    • you have misrepresented part of the problem

  • If at first you don’t succeed, try something else

  • Tackle one part of the problem and other parts may fall into place

Conclusion l.jpg

  • More than one solution

  • Solution limited by boundary conditions

  • Representation affects strategy

  • Active involvement and testing

Functional fixedness l.jpg
Functional Fixedness

  • Strategy developed in one version of the problem

  • Strategy might be inefficient

    X ) XXXX

  • Convert numerals or just ‘see’ 4

Data driven perception l.jpg
Data-driven perception

Activation of neural structures of sensory system by pattern of stimulation from environment

Theory driven perception l.jpg
Theory-driven perception

Perception driven by memories and expectations about incoming information.

Keypoint l.jpg

PERCEPTION involves a set of active processes that impose:




on the world

Visual illusions l.jpg
Visual Illusions

Rabbit or duck?

Old Woman or Young girl?

Interpretation l.jpg

Knowledge of what you are “looking at” can aid in interpretation






Organisation of information is also useful

Story grammars l.jpg
Story Grammars

  • Analogy with sentence grammars

    • Building blocks and rules for combining

  • Break story into propositions

    “Margie was holding tightly to the string of her beautiful new balloon. Suddenly a gust of wind caught it, and carried it into a tree. It hit a branch, and burst. Margie cried and cried.”

Story grammar l.jpg
Story Grammar























Of state


Inferences l.jpg

  • Comprehension typically requires our active involvement in order to supply information that is not explicit in the text

    1. Mary heard the ice-cream van coming

    2. She remembered her pocket money

    3. She rushed into the house.

Inference and recall l.jpg
Inference and Recall

  • Thorndyke (1976): recall of sentences from ‘Mary’ story

    • 85% correct sentence

    • 58% correct inference –

      • sentence not presented

    • 6% incorrect inference

Mental models l.jpg
Mental Models

  • Van Dijk and Kintsch (1983)

    • Text processed to extract propositions, which are held in working memory;

    • When sufficient propositions in WM, then linking performed;

    • Relevance of propositions to linking proportional to recall;

    • Linking reveals ‘gist’

Semantic networks l.jpg
Semantic Networks

Has Skin

Can move




Can fly

Has Wings

Has feathers


Has fins

Can swim

Has gills


Is Yellow

Can sing


Collins &

Quillian, 1969

Levels and reaction time l.jpg






Mean Reaction Time (s)









Levels of Sentences

Levels and Reaction time

A canary

can fly

A canary

has gills

A canary

can sing

A canary

has skin

Collins &

Quillian, 1969

A canary is

a fish

A canary is

a canary

A canary is

a bird

A canary is

an animal

Canaries l.jpg

  • Different times to verify the statements:

    • A canary is a bird

    • A canary can fly

    • A canary can sing

  • Time proportional to movement through network

Scripts schema and frames l.jpg
Scripts, Schema and Frames

  • Schema = chunks of knowledge

    • Slots for information: fixed, default, optional

  • Scripts = action sequences

    • Generalised event schema (Nelson, 1986)

  • Frames = knowledge about the properties of things

Mental models23 l.jpg
Mental Models

  • Partial

  • Procedures, Functions or System?

  • Memory or Reconstruction?

Concepts l.jpg

  • How do you know a chair is a chair?

A chair has four legs…does it? A chair has a seat…does it?

Prototypes typical features and exemplars l.jpg
Prototypes, Typical Features, and Exemplars

  • Prototype

    • ROSCH (1973): people do not use feature sets, but imagine a PROTOTYPE for an object

  • Typical Features

    • ROSCH & MERVIS (1975): people use a list of features, weighted in terms of CUE VALIDITY

  • Exemplars

    • SMITH & MEDIN (1981): people use an EXAMPLE to imagine an object

  • Representing concepts l.jpg
    Representing Concepts

    • BARSALOU (1983)


        • Categories that are well known and can be recalled consistently and reliably

          • E.g., Fruit, Furniture, Animals

        • Used to generate overall representation of the world

      • AD HOC

        • Categories that are invented for specific purpose

          • E.g., How to make friends, Moving house

        • Used for goal-directed activity within specific event frames

    Long term memory l.jpg
    Long Term Memory

    • Procedural

      • Knowing how

    • Declarative

      • Knowing that

    • Episodic vs. Semantic

      • Personal events

      • Language and knowledge of world

    Working memory l.jpg
    Working Memory

    • Limited Capacity

      • 7 + 2 items (Miller, 1965)

      • 4 + 2 chunks (Broadbent, 1972)

      • Modality dependent capacity

  • Strategies for coping with limitation

    • Chunking

    • Interference

    • Activation of Long-term memory

  • Slide29 l.jpg

    Baddeley’s (1986) Model of

    Working Memory



    Visual Cache

    Inner scribe

    Phonological store

    Auditory word presentation

    Visual word presentation

    Articulatory control process

    Slave systems l.jpg
    Slave Systems

    • Articulatory loop

      • Memory Activation

      • Rehearsal capacity

        • Word length effect and Rehearsal speed

    • Visual cache

      • Visual patterns

      • Complexity of pattern, number of elements etc

    • Inner scribe

      • Sequences of movement

      • Complexity of movement

    Typing l.jpg

    • Eye-hand span related to expertise

      • Expert = 9, novice = 1

  • Inter-key interval

    • Expert = 100ms

  • Strategy

    • Hunt & Peck vs. Touch typing

  • Keystroke

    • Novice = highly variable keystroke time

    • Novice = very slow on ‘unusual’ letters, e.g., X or Z

  • Salthouse 1986 l.jpg
    Salthouse (1986)

    • Input

      • Text converted to chunks

    • Parsing

      • Chunks decomposed to strings

    • Translation

      • Strings into characters and linked to movements

    • Execution

      • Key pressed

    Rumelhart norman 1982 l.jpg
    Rumelhart & Norman (1982)

    • Perceptual processes

      • Perceive text, generate word schema

    • Parsing

      • Compute codes for each letter

    • Keypress schemata

      • Activate schema for letter-keypress

    • Response activation

      • Press defined key through activation of appropriate hand / finger

    Schematic of rumelhart and norman s connectionist model of typing l.jpg
    Schematic of Rumelhart and Norman’s connectionist model of typing


    index ring

    thumb little

    Right hand


    ring index

    little thumb

    Left hand

    Response system

    Keypress node, breaking

    Word into typed letters;

    Excites and inhibits nodes






    Word node, activated from

    Visual or auditory stimulus


    Automaticity l.jpg
    Automaticity typing

    • Norman and Shallice (1980)

      • Fully automatic processing controlled by SCHEMATA

      • Partially automatic processing controlled by either Contention Scheduling

      • Supervisory Attentional System (SAS)

    Supervisory attentional system model l.jpg
    Supervisory Attentional System Model typing




    Control schema









    Contention scheduling l.jpg
    Contention Scheduling typing

    • Gear changing when driving involves many routine activities but is performed ‘automatically’ – without conscious awareness

    • When routines clash, relative importance is used to determine which to perform – Contention Scheduling

      • e.g., right foot on brake or clutch

    Sas activation l.jpg
    SAS activation typing

    • Driving on roundabouts in France

      • Inhibit ‘look right’; Activate ‘look left’

      • SAS to over-ride habitual actions

    • SAS active when:

      • Danger, Choice of response, Novelty etc.

    Attentional slips and lapses l.jpg
    Attentional Slips and Lapses typing

    • Habitual actions become automatic

    • SAS inhibits habit

    • Perserveration

      • When SAS does not inhibit and habit proceeds

  • Distraction

    • Irrelevant objects attract attention

    • Utilisation behaviour: patients with frontal lobe damage will reach for object close to hand even when told not to

  • Performance operating characteristics l.jpg
    Performance Operating Characteristics typing

    • Resource-dependent trade-off between performance levels on two tasks

    • Task A and Task B performed several times, with instructions to allocate more effort to one task or the other

    Task difficulty l.jpg
    Task Difficulty typing

    • Data limited processes

      • Performance related to quality of data and will not improve with more resource

  • Resource limited processes

    • Performance related to amount of resource invested in task and will improve with more resource

  • Slide42 l.jpg

    Data limited typing

    Resource limited












    Task B

    Task B

    Why model performance l.jpg
    Why Model Performance? typing

    • Building models can help develop theory

      • Models make assumptions explicit

      • Models force explanation

    • Surrogate user:

      • Define ‘benchmarks’

      • Evaluate conceptual designs

      • Make design assumptions explicit

    • Rationale for design decisions

    Why model performance44 l.jpg
    Why Model Performance? typing

    • Human-computer interaction as Applied Science

      • Theory from cognitive sciences used as basis for design

      • General principles of perceptual, motor and cognitive activity

      • Development and testing of theory through models

    Types of model in hci l.jpg
    Types of Model in HCI typing

    Whitefield, 1987

    Task models l.jpg
    Task Models typing

    • Researcher’s Model of User, in terms of tasks

    • Describe typical activities

    • Reduce activities to generic sequences

    • Provide basis for design

    Pros and cons of modelling l.jpg
    Pros and Cons of Modelling typing

    • PROS

      • Consistent description through (semi) formal representations

      • Set of ‘typical’ examples

      • Allows prediction / description of performance

    • CONS

      • Selective (some things don’t fit into models)

      • Assumption of invariability

      • Misses creative, flexible, non-standard activity

    Generic model process l.jpg
    Generic Model Process? typing

    • Define system: {goals, activity, tasks, entities, parameters}

    • Abstract to semantic level

    • Define syntax / representation

    • Define interaction

    • Check for consistency and completeness

    • Predict / describe performance

    • Evaluate results

    • Modify model

    Device models l.jpg
    Device Models typing

    • Buxton’s 3-state device model







    Application l.jpg
    Application typing

    Button up

    Pen off







    Pen on

    Button down



    Out of range

    Conclusions l.jpg
    Conclusions typing

    • Models abstract aspects of interaction

      • User, task, system

    • Models play a variety of roles in design

    Hierarchical task analysis l.jpg
    Hierarchical Task Analysis typing

    • Activity assumed to consist of TASKS performed in pursuit of GOALS

    • Goals can be broken into SUBGOALS, which can be broken into tasks

    • Hierarchy (Tree) description

    The analysis comes from plans l.jpg
    The “Analysis” comes from plans typing

    • PLANS = conditions for combining tasks

    • Fixed Sequence

      • P0: 1 > 2 > exit

    • Contingent Fixed Sequence

      • P1: 1 > when state X achieved > 2 > exit

      • P1.1: 1.1 > 1.2 > wait for X time > 1.3 > exit

    • Decision

      • P2: 1 > 2 > If condition X then 3, elseif condition Y then 4 > 5 > exit

    Reporting l.jpg
    Reporting typing

    • HTA can be constructed using Post-it notes on a large space (this makes it easy to edit and also encourages participation)

    • HTA can be difficult to present in a succinct printed form (it might be useful to take a photograph of the Post-it notes)

    • Typically a Tabular format is used:

    Original design l.jpg
    Original Design typing

    Menu driven

    Menus accessed by first letter of command

    Menus arranged in hierarchy

    Problems with original design l.jpg
    Problems with original design typing

    • Lack of consistency

      • D = DOS commands; Delete; Data file; Date

  • Hidden hierarchy

    • Only ‘experts’ could use

  • Inappropriate defaults

    • Setting up a scan required ‘correction’ of default settings three or four times

  • Initial design activity l.jpg
    Initial design activity typing

    • Observation of non-technology work

      • Cytogeneticists inspecting chromosomes

  • Developed model of task

    • Hierarchical task analysis

  • Developed design principles, e.g.,

    • Cytogeneticists as ‘picture people’

    • Task flow

    • Task mapping

  • Task model l.jpg
    Task Model typing

    • Work flows between specific activities


    Patient details


    Cell sample

    Set up



    First prototype l.jpg
    First “prototype” typing

    Layout related to

    task model

    ‘Sketch’ very simple

    Annotations show


    Second prototype l.jpg
    Second prototype typing

    Refined layout

    ‘Prototype’ using


    Initial user trials compared

    this with a mock-up of the

    original design

    Final product l.jpg
    Final Product typing

    Picture taken from company


    Initial concepts retained

    Further modifications possible

    Predicting performance time l.jpg
    Predicting Performance Time typing

    • Time and error are ‘standard’ measures of human performance

    • Predict transaction time for comparative evaluation

    • Approximations of human performance

    Unit times l.jpg
    Unit Times typing

    • From task model, define sequence of tasks to achieve a specific goal

    • For each task, define ‘average time’

    Quick exercise l.jpg
    Quick Exercise typing

    • Draw two parallel lines about 4cm apart and about 10cm long

    • Draw, as quickly as possible, a zig-zag line for 5 seconds

    • Count the number of lines and the number of times you have crossed the parallel lines

    Predicted result l.jpg
    Predicted result typing

    • About 70 lines

    • About 20 cross-overs

    Why this prediction l.jpg
    Why this prediction? typing

    • Movement speed limited by biomechanical constraints

      • Motor subsystem change direction @ 70ms

      • So: 5000 / 70 = 71 oscillations

    • Cognitive / Perceptual system cycles:

      • Perceptual @ 70ms

      • Cognitive @ 100ms

      • Correction takes 70+70+100 = 240ms

      • 5000/240 = 21

    Fitts law l.jpg
    Fitts’ Law typing

    • Paul Fitts 1954

    • Information-theoretic account of simple movements

    • Define the number of ‘bits’ processed in performing a given task

    Fitts law74 l.jpg
    Fitts’ Law typing

    • A = 62, W = 15

    • A = 112, W = 7

    • A = 112, W = 21

    Movement Time = a + b (log2 2A/W)








    a = 10

    b = 27.5




    Log2 (2A/W)

    1 = 5.3 2 = 4.5 3 = 3.2

    Alternate versions l.jpg
    Alternate Versions typing

    MT = a + b log2 (2A/W)

    MT = b log2 (A/W + 0.5)

    MT = a + b log2 (A/W/+1)

    A and b are constants l.jpg
    a and b are “constants” typing

    Data derived from plot

    Data as predictors?

    Potential problems l.jpg
    Potential Problems typing

    • Data-fitter rather than ‘law’

    • ‘Generic value’: a+b = 100

    • Variable predictive power for devices?

      • From ‘mouse data’ we get:

        (assume A = 5 and W = 10) log2(2A/W)  0.3

        339ms, 150.5ms and 34.9ms (!!)

    Hick hyman law l.jpg
    Hick – Hyman Law typing

    • William Hick 1952

    • Selection time, from a set of items, is proportional to the number of items

      T = k log2 (n+1), Where k = a constant (intercept+slope)

    • Approximately 150ms added to T for each item

    Example of hick hyman law l.jpg
    Example of Hick-Hyman Law typing

    Search Time









    2 3 4 5 6 7 8 10 12

    Landauer and Nachbar, 1985

    Keystroke level models l.jpg
    Keystroke Level Models typing

    • Developed from 1950s ergonomics

    • Human information processor as linear executor of specified tasks

    • Unit-tasks have defined times

    • Prediction = summing of times for sequence of unit-tasks

    Building a klm l.jpg
    Building a KLM typing

    • Develop task model

    • Define task sequence

    • Assign unit-times to tasks

    • Sum times

    Example cut and paste l.jpg
    Example: cut and paste typing

    Task Model: Select line – Cut – Select insertion point – paste

    Task One: select line

    move cursor to

    start of line

    press (hold) button

    drag cursor to

    end of line

    release button

    Times for movement l.jpg
    Times for Movement typing

    • H: homing, e.g., hand from keyboard to mouse

      • Range: 214ms – 400ms

      • Average: 320ms

    • P: pointing, e.g., move cursor using mouse

      • Range: defined by Fitts’ Law

      • Average: 1100ms

    • B: button pressing, e.g., hitting key on keyboard

      • Range: 80ms – 700ms

      • Average: 200ms

    Times for cognition perception l.jpg
    Times for Cognition / Perception typing

    • M: mental operation

      • Range: 990ms – 1760ms

      • Average: 1350ms

    • A: switch attention between parts of display

      • Average: 320ms

    • R: recognition of items

      • Range: 314ms – 1800ms

      • Average: 340ms

    • Perceive change:

      • Range: 50 – 300ms

      • Average: 100ms

    Rules for summing times l.jpg
    Rules for Summing Times typing

    • How to handle multiple Mental units:

      • M before Ks in new argument strings

      • M at start of ‘cognitive unit’

      • M before Ps that select commands

      • Delete M if K redundant terminator

    Alternative l.jpg

    Pe typing







    • What if we use ‘accelerated scrolling’ on the cursor keys?

      • Press  key and read scrolling numbers

      • Release key at or near number

      • Select correct number

    Critical path models l.jpg
    Critical Path Models typing

    • Used in project management

    • Map dependencies between tasks in a project

      • Task X is dependent on task Y, if it is necessary to wait until the end of task Y until task X can commence

    Procedure l.jpg
    Procedure typing

    • Construct task model, taking into account dependencies

    • Assign times to tasks

    • Calculate critical path and transaction time

      • Run forward pass

      • Run backward pass

    Example l.jpg
    Example typing







    M = 1.35

    H = 0.32

    P = 0.2

    R = 0.34



















    Comparison l.jpg
    Comparison typing

    • ‘Summing of times’ result:

      • 2.61s

    • ‘Critical path’ result:

      • 2.47s

    • R allowed to ‘float’

    Other time based models l.jpg
    Other time-based models typing

    • Task-network models

      • MicroSAINT

      • Unit-times and probability of transition




    Speak word

    [300  9]ms

    System response

    [1000  30]ms


    Performance vs competence l.jpg
    Performance vs. Competence typing

    • Performance Models

      • Make statements and predictions about the time, effort or likelihood of error when performing specific tasks;

    • Competence Models

      • Make statements about what a given user knows and how this knowledge might be organised.

    Sequence vs process vs grammar l.jpg
    Sequence vs. Process vs. Grammar typing

    • Sequence Models

      • Define activity simply in terms of sequences of operations that can be quantified

    • Process Models

      • Simple model of mental activity but define the steps needed to perform tasks

    • Grammatical Models

      • Model required knowledge in terms of ‘sentences’

    Process models l.jpg
    Process Models typing

    • Production systems

    • GOMS

    Production systems l.jpg
    Production Systems typing

    • Rules = (Procedural) Knowledge

    • Working memory = state of the world

    • Control strategies = way of applying knowledge

    Production systems98 l.jpg

    Rule base typing


    Working Memory

    Production Systems

    Architecture of a production system:

    The problem of control l.jpg
    The Problem of Control typing

    • Rules are useless without a useful way to apply them

    • Need a consistent, reliable, useful way to control the way rules are applied

    • Different architectures / systems use different control strategies to produce different results

    Forward chaining l.jpg

    If A and B then not C typing

    If A then B

    If A then B

    If not C then GOAL



    If A and B then not C




    Forward Chaining



    If not C then GOAL

    Backward chaining l.jpg

    C typing


    If A and B then not C

    If A then B

    If not C then GOAL






    Backward Chaining

    Need GOAL

    If not C then GOAL

    Need: not C

    If A and B then not C

    Need B

    If A then B

    Production systems102 l.jpg
    Production Systems typing

    • A simple metaphor



    Production systems103 l.jpg
    Production Systems typing

    • Ships must fit the correct dock

    • When one ship is docked, another can be launched

    Production rules l.jpg
    Production Rules typing

    IF condition

    THEN action


    IF ship is docked

    And free-floating ships

    THEN launch ship

    IF dock is free

    And Ship matches

    THEN dock ship

    The parsimonious production systems rule notation l.jpg
    The Parsimonious Production Systems Rule Notation typing

    • On any cycle, any rule whose conditions are currently satisfied will fire

    • Rules must be written so that a single rule will not fire repeatedly

    • Only one rule will fire on a cycle

    • All procedural knowledge is explicit in these rules rather than being explicit in the interpreter

    Worked example the tower of hanoi l.jpg
    Worked Example: typingThe Tower of Hanoi

    A B C






    Possible steps 1 l.jpg
    Possible Steps 1 typing

    Disc 1 from a to c

    Disc 2 from a to b

    Disc 1 from c to a

    Disc 3 from a to c

    Disc 2 from b to c

    Disc 1 from a to c

    Worked example the tower of hanoi110 l.jpg
    Worked Example: typingThe Tower of Hanoi

    A B C






    Possible steps 2 l.jpg
    Possible Steps 2 typing

    Disc 4 from a to b

    Disc 1 from c to b

    Disc 2 from c to a

    Disc 1 from b to a

    Disc 2 from a to b

    Disc 3 from a to b

    Worked example the tower of hanoi112 l.jpg
    Worked Example: typingThe Tower of Hanoi

    A B C






    Possible steps 3 l.jpg
    Possible Steps 3 typing

    Disc 5 from a to c

    Disc 1 from b to a

    Disc 2 from b to c

    Disc 1 from a to c

    Disc 3 from b to a

    Disc 1 from c to b

    Disc 2 from c to a

    Disc 4 from b to c

    Disc 1 from a to c

    Disc 2 from a to b

    Disc 1 from c to b

    Disc 3 from a to c

    Disc 1 from b to a

    Disc 2 from b to c

    Disc 1 from a to c

    Simon s 1975 goal recursive logic l.jpg
    Simon’s (1975) goal-recursive logic typing

    To get the 5-tower to Peg C, get the 4-tower to Peg B, then move

    The 5-disc to Peg C, then move the 4-tower to Peg C

    To get the 4-tower to Peg B, get the 3-tower to Peg C, then move

    The 4-disc to Peg B, then move the 3-tower to Peg B

    To get the 3-tower to Peg C, get the 2-tower to Peg B, then move

    The 3-disc to Peg C, then move the 2-tower to Peg C,

    To get the 2-tower to Peg B, move the 1-disc to Peg C, then move

    The 2-disc to Peg B, then move the 1-disc to Peg A

    Production rule 1 l.jpg
    Production Rule 1 typing


    IF the goal is to achieve a particular configuration of discs

    And Di is on Px but should go to Py in the configuration

    And Di is the largest disc out of place

    And Dj is on Py

    And Dj is smaller than Di

    And Pz is clear OR has a disc larger than Dj

    THEN set a subgoal to move the Dj tower to Pz and Di to Py

    Production rule 2 l.jpg
    Production Rule 2 typing


    IF the goal is to achieve a particular configuration of discs

    And Di is on Px but should go to Py in the configuration

    And Di is the largest disc out of place

    And Py is clear

    THEN move Di to Py

    Goals operators method selection card moran and newell 1983 l.jpg
    Goals Operators Method Selection typingCard, Moran and Newell, 1983

    • Human activity modelled by Model Human Processor

    • Activity defined by GOALS

    • Goals held in ‘Stack’

    • Goals ‘pushed’ onto stack

    • Goals ‘popped’ from stack

    Goals l.jpg
    Goals typing

    • Symbolic structures to define desired state of affairs and methods to achieve this state of affairs

      GOAL: EDIT-MANUSCRIPT top level goal

      GOAL: EDIT-UNIT-TASK specific sub goal

      GOAL: ACQUIRE UNIT-TASK get next step

      GOAL: EXECUTE UNIT-TASK do next step

      GOAL: LOCATION-LINE specific step

    Operators l.jpg
    Operators typing

    • Elementary perceptual, motor or cognitive acts needed to achieve subgoals




    Methods l.jpg
    Methods typing

    • Descriptions of procedures for achieving goals

    • Conditional upon contents of working memory and state of task


      GET-NEXT-PAGE if at end of manuscript


    Selection l.jpg
    Selection typing

    • Choose between competing Methods, if more than one



      [select: if hands on keyboard

      and less than 5 lines to move



      USE MOUSE]

    Example122 l.jpg
    Example typing

    • Withdraw cash from ATM

      • Construct task model

      • Define production rules

    Task model123 l.jpg
    Task Model typing

    Method for goal: Obtain cash from ATM

    Step1: access ATM

    Step2: select ‘cash’ option

    Step3: indicate amount

    Step4: retrieve cash and card

    Step5: end task

    Production rules124 l.jpg
    Production Rules typing


    ADD-UNIT-TASK (access ATM)

    ADD-WM-UNIT-TASK (access ATM)

    ADD-TASK-STEP (insert card in slot)

    SEND-TO-MOTOR(place card in slot)

    SEND-TO-MOTOR (eyes to slot)

    SEND-TO-PERCEPTUAL (check card in)

    ADD (WM performing card insertion)

    ADD-TASK-STEP (check card insertion)


    ADD-UNIT-TASK (enter PIN)

    Problems with goms l.jpg
    Problems with GOMS typing

    • Assumes ‘error-free’ performance

      • Even experts make mistakes

    • MHP gross simplifies human information processing

    • Producing a task model of non-existent products is difficult

    Task action grammar l.jpg
    Task Action Grammar typing

    • GOMS assumes ‘expert’ knows operators and methods for tasks

    • TAG assumes ‘expert’ knows simple tasks, i.e., tasks that can be performed without problem-solving

    Tag and competence l.jpg
    TAG and competence typing

    • Competence

      • Defines what an ‘ideal’ user would know

    • TAG relies on ‘world knowledge’

      • up vs down

      • left vs right

      • forward vs backward

    Task action grammar128 l.jpg
    Task-action Grammar typing

    • Grammar relates simple tasks to actions

    • Generic rule schema covering combinations of simple tasks

    Slide129 l.jpg
    TAG typing

    • A ‘grammar’

      • maps

        • Simple tasks

      • Onto

        • Actions

      • To form

        • an interaction language

      • To investigate

        • consistency

    Consistency l.jpg
    Consistency typing

    • Syntactic: use of expressions

    • Lexical: use of symbols

    • Semantic-syntactic alignment: order of terms

    • Semantic: principle of completeness

    Procedure131 l.jpg
    Procedure typing

    • Step 1: Write out commands and their structures

    • Step 2: Determine in commands have consistent structure

    • Step 3: Place command items into variable/feature relationship

    • Step 4: Generalise commands by separating into task features, simple tasks, task-action rule schema

    • Step 5: Expand parts of task into primitives

    • Step 6: Check to ensure all names are unique

    Example132 l.jpg
    Example typing

    • Setting up a recording on a video-cassette recorder (VCR)

    • Assume that all controls via front panel and that the user can only use the up and down arrows

    Feature list for a vcr l.jpg
    Feature list [for a VCR] typing

    • Property Date, Channel, Start, End

    • Value number

    • Frequency Daily, Weekly

    • Record on, off

    Simple tasks l.jpg
    Simple tasks typing

    SetDate [Property = Date, Value = US#, Frequency = Daily]

    SetDate [Property = Date, Value = US#, Frequency = Weekly]

    SetProg[Property =Prog, Value = US#]

    SetStart[Property = start, Value = US#, Record = on]

    SetEnd[Property = start, Value = US#, Record = off]

    Rule schema l.jpg
    Rule Schema typing

    1. Task[Property = US#, Value]  SetValue [Value]

    2. Task[Property = Date, Value, Frequency = US#]  SetValue [Value] + press “ |” until Frequency = US#

    3. Task[Property = Start, Value]  SetValue [Value] + press “Rec”

    4. SetValue [Value = US#]  press “ |” until Value = US#

    5. SetValue[Value = US#]  use “ |” until Value = US#

    Why cognitive architecture l.jpg
    Why Cognitive Architecture? typing

    • Computers architectures:

      • Specify components and their connections

      • Define functions and processes

    • Cognitive Architectures could be seen as the logical conclusion of the ‘human-brain-as-computer’ hypothesis

    Why do this l.jpg
    Why do this? typing

    • Philosophy: Provide a unified understanding of the mind

    • Psychology: Account for experimental data

    • Education: Provide cognitive models for intelligent tutoring systems and other learning environments

    • Human Computer Interaction: Evaluate artifacts and help in their design

    • Computer Generated Forces: Provide cognitive agents to inhabit training environments and games

    • Neuroscience: Provide a framework for interpreting data from brain imaging

    General requirements l.jpg
    General Requirements typing

    • Integration of cognition, perception, and action

    • Robust behavior in the face of error, the unexpected, and the unknown

    • Ability to run in real time

    • Ability to Learn

    • Prediction of human behavior and performance

    Architectures l.jpg
    Architectures typing

    • Model Human Processor (MHP)

      • Card, Moran and Newell (1983)

    • ACT-R

      • Anderson (1993)

    • EPIC

      • Meyer and Kieras (1997)

    • SOAR

      • Laird, Rosenbloom and Newell (1987)

    Model human processor l.jpg
    Model Human Processor typing

    • Three interacting subsystems:

      • Perceptual

        • Auditory image store

        • Visual image store

      • Cognitive

        • Working memory

        • Long-term memory

      • Motor

    Average data for mhp l.jpg
    Average data for MHP typing

    • Long-term memory: ?

    • Working memory: 3 – 7 chunks, 7s

    • Auditory image store: 17 letters, 200ms

    • Visual image store: 5 letters, 1500ms

    • Cognitive processor: 100ms

    • Perceptual processor: 70ms

    • Motor processor: 70ms

    Conclusions144 l.jpg
    Conclusions typing

    • Simple description of cognition

    • Uses ‘standard times’ for prediction

    • Uses production rules for defining and combining tasks (with GOMS formalism)

    Adaptive control of thought rational act r http act psy cmu edu l.jpg
    Adaptive Control of Thought, Rational (ACT-R) typing

    Adaptive control of thought rational act r l.jpg
    Adaptive Control of Thought, Rational (ACT-R) typing

    • ACT-R symbolic aspect realised over subsymbolic mechanism

    • Symbolic aspect in two parts:

      • Production memory

      • Symbolic memory (declarative memory)

    • Theory of rational analysis

    Theory of rational analysis l.jpg
    Theory of Rational Analysis typing

    • Evidence-based assumptions about environment (probabilities)

    • Deriving optimal strategies (Bayesian)

    • Assuming that optimal strategies reflect human cognition (either what it actually does or what it probably ought to do)

    Notions of memory l.jpg
    Notions of Memory typing

    • Procedural

      • Knowing how

      • Described in ACT by Production Rules

    • Declarative

      • Knowing that

      • Described in ACT by ‘chunks’

    • Goal Stack

      • A sort of ‘working memory’

      • Holds chunks (goals)

      • Top goal pushed (like GOMS)

      • Writeable

    Production rules149 l.jpg
    Production Rules typing

    • Knowing how to do X

      • Production rule = set of conditions and an action

        IF it is raining

        And you wish to go out

        THEN pick up your umbrella

    Very simple act l.jpg
    (Very simple) ACT typing

    • Network of propositions

    • Production rules selected via pattern matching. Production rules coordinate retrieval of chunks from symbolic memory and link to environment.

    • If information in working memory matches production rule condition, then fire production rule

    Slide151 l.jpg
    ACT* typing





    Retrieval Storage Match Execution



    Encoding Performance


    Slide152 l.jpg

    Addition-Fact typing

    Knowledge Representation



    U (4); T (1); H (0)




    18 +






    Goal buffer: add numbers in right-most column

    Visual buffer: 6, 8

    Retrieval buffer: 14

    Symbolic subsymbolic levels l.jpg
    Symbolic / Subsymbolic levels typing

    • Symbolic level

      • Information as chunks in declarative memory, and represented as propositions

      • Rules as productions in procedural memory

    • Subsymbolic level

      • Chunks given parameters which are used to determine the probability that the chunk is needed

      • Base-level activation (relevance)

      • Context activation (association strengths)

    Conflict resolution l.jpg
    Conflict resolution typing

    • Order production rules by preference

    • Select top rule in list

    • Preference defined by:

      • Probability that rule will lead to goal

      • Time associated with rule

      • Likely cost of reaching goal when using sequence involving this rule

    Example155 l.jpg
    Example typing

    • Activity: Find target and then use mouse to select target:


      IF goal = find target with feature F

      AND there is object X on screen

      THEN move attention to object X


      IF goal = find target with feature F

      AND target with F in location L

      THEN move mouse to L and click

    Example156 l.jpg
    Example typing

    • Model reaction time to target

      • Assume switch attention linearly increases with each new position

      • Assume probability of feature X in location y = 0.53

      • Assume switch attention = 185ms

    • Therefore, reaction time = 185 X 0.53 = 98ms per position

    • Empirical data has RT of 103ms per position

    Example157 l.jpg
    Example typing

    • Assume target in field of distractors

      • P = 0.42

      • Therefore, 185 x .42 = 78ms per position

    • Empirical data = 80ms per position

    Learning l.jpg
    Learning typing

    • Symbolic level

      • Learning defined by adding new chunks and productions

    • Subsymbolic level

      • Adjustment of parameters based on experience

    Conclusions159 l.jpg
    Conclusions typing

    • ACT uses simple production system

    • ACT provides some quantitative prediction of performance

    • Rationality = optimal adaptation to environment

    Executive process interactive control epic ftp ftp eecs umich edu people kieras l.jpg
    Executive Process Interactive Control (EPIC) typing

    Executive process interactive control epic l.jpg
    Executive Process Interactive Control (EPIC) typing

    • Focus on multiple task performance

    • Cognitive Processor runs production rules and interacts with perceptual and motor processors

    Epic parameters l.jpg
    EPIC parameters typing

    • FIXED

      • Connections and mechanisms

      • Time parameters

      • Feature sets for motor processors

      • Task-specific production rules and perceptual encoding types

    • FREE

      • Production rules for tasks

      • Unique perceptual and motor processors

      • Task instance set

      • Simulated task environment

    Slide163 l.jpg
    EPIC typing









    Rule interpreter














    Production memory l.jpg
    Production Memory typing

    • Perceptual processors controlled by production rules

    • Production Rules held in Production Memory

    • Production Rule Interpreter applies rules to perceptual processes

    Working memory165 l.jpg
    Working Memory typing

    • Limited capacity (or duration of 4s) and holds current production rules

    • Cognitive processor updates every 50ms

    • On update, perceptual input, item from production memory, and next action held in working memory

    Resolving conflict l.jpg
    Resolving Conflict typing

    • Production rules applied to executive tasks to handle resource conflict and scheduling

    • Conflict dealt with in production rule specification

      • Lockout

      • Interleaving

      • Strategic response deferent

    Example167 l.jpg
    Example typing

    Task one

    Stimulus one

    Perceptual process

    Cognitive process

    Response selection

    Memory process

    Response one

    Executive process

    Move eye to S2

    Enable task1 + task 2

    Wait for task1 complete


    Task2 permission

    Trial end

    Task two

    Stimulus two

    Perceptual process

    Cognitive process

    Response selection

    Memory process

    Response two

    Conclusions168 l.jpg
    Conclusions typing

    • Modular structure supports parallelism

    • EPIC does not have a goal stack and does not assume sequential firing of goals

    • Goals can be handled in parallel (provided there is no resource conflict)

    • Does not support learning

    States operators and reasoning soar http www isi edu soar soar html l.jpg
    States, Operators, And Reasoning (SOAR) typing

    States operators and reasoning soar l.jpg
    States, Operators, And Reasoning (SOAR) typing

    • Sequel of General Problem Solver (Newell and Simon, 1960)

    • SOAR seeks to apply operators to states within a problem space to achieve a goal.

    • SOAR assumes that actor uses all available knowledge in problem-solving

    Soar as a unified theory of cognition l.jpg
    Soar as a Unified Theory of Cognition typing

    • Intelligence = problem solving + learning

    • Cognition seen as search in problem spaces

    • All knowledge is encoded as productions

       a single type of knowledge

    • All learning is done by chunking

       a single type of learning

    Slide172 l.jpg

    Young, R.M., Ritter, F., Jones, G.  1998  typing"Online Psychological Soar Tutorial" available at:

    Soar activity l.jpg
    SOAR Activity typing

    • Operators:  Transform a state via some action

    • State:  A representation of possible stages of progress in the problem

    • Problem space:  States and operators that can be used to achieve a goal.

    • Goal: Some desired situation.

    Soar activity174 l.jpg
    SOAR Activity typing

    • Problem solving = applying an Operator to a State in order to move through a Problem Space to reach a Goal. 

    • Impasse =   Where an Operator cannot be applied to a State, and so it is not possible to move forward in the Problem Space. This becomes a new problem to be solved.

    • Soar can learn by storing solutions to past problems as chunks and applying them when it encounters the same problem again

    Soar architecture l.jpg

    Chunking typing


    SOAR Architecture

    Production memory

    Pattern Action

    Pattern Action

    Pattern Action

    Working memory



    Working memory


    Conflict stack



    Explanation l.jpg
    Explanation typing

    • Working Memory

      • Data for current activity, organized into objects

    • Production Memory

      • Contains production rules

    • Chunking mechanism

      • Collapses successful sequences of operators into chunks for re-use

    3 levels in soar l.jpg
    3 levels in soar typing

    • Symbolic – the programming level

      • Rules programmed into Soar that match circumstances and perform specific actions

    • Problem space – states & goals

      • The set of goals, states, operators, and context.

    • Knowledge – embodied in the rules

      • The knowledge of how to act on the problem/world, how to choose between different operators, and any learned chunks from previous problem solving

    How does it work l.jpg
    How does it work? typing

    • A problem is encoded as a current state and a desired state (goal)

    • Operators are applied to move from one state to another

    • There is success if the desired state matches the current state

    • Operators are proposed by productions, with preferences biasing choices in specific circumstances

    • Productions fire in parallel

    Impasses l.jpg
    Impasses typing

    • If no operator is proposed, or if there is a tie between operators, or if Soar does not know what to do with an operator, there is an impasse

    • When there are impasses, Soar sets a new goal (resolve the impasse) and creates a new state

    • Impasses may be stacked

    • When one impasse is solved, Soar pops up to the previous goal

    Learning180 l.jpg
    Learning typing

    • Learning occurs by chunking the conditions and the actions of the impasses that have been resolved

    • Chunks can immediately used in further problem-solving behaviour

    Conclusions182 l.jpg
    Conclusions typing

    • It may be too "unified"

      • Single learning mechanism

      • Single knowledge representation

      • Uniform problem state

    • It does not take neuropsychological evidence into account (cf. ACT-R)

    • There may be non-symbolic intelligence, e.g. neural nets etc not abstractable to the symbolic level

    User models in design l.jpg
    User Models in Design typing

    • Benchmarking

    • Human Virtual Machines

    • Evaluation of concepts

    • Comparison of concepts

    • Analytical prototyping

    Benchmarking l.jpg
    Benchmarking typing

    • What times can users expect to take to perform task

      • Training criteria

      • Evaluation criteria (under ISO9241)

      • Product comparison

    Human virtual machine l.jpg
    Human Virtual Machine typing

    • How might the user perform?

      • Make assumptions explicit

      • Contrast views

    Evaluation of concepts l.jpg
    Evaluation of Concepts typing

    • Which design could lead to better performance?

      • Compare concepts using models prior to building prototype

      • Use performance of existing product as benchmark

    Reliability of models l.jpg
    Reliability of Models typing

    • Agreement of predictions with observations

    • Agreement of predictions by different analysts

    • Agreement of model with theory

    Comparison with theory l.jpg
    Comparison with Theory typing

    • Approximation of human information processing

    • Assumes linear, error-free performance

    • Assumes strict following of ‘correct’ procedure

    • Assumes only way correct procedure

    • Assumes actions can be timed

    Klm validity l.jpg
    KLM Validity typing

    Predicted values lie

    within 20% of

    observed values

    Comparison of klm predicted with times from user trials l.jpg
    Comparison of KLM predicted with times from user trials typing

    Total time






    CUI: P = 15.84s

    mean = 15.37s

    Error = 2.9%

    GUI: P = 11.05s

    mean = 8.64s

    Error = 22%

    1 2 3 4 5 6 7

    Trial number

    Inter intra rater reliability l.jpg
    Inter / Intra-rater Reliability typing

    • Inter-rater:

      • Correlation of several analysts

      • = 0.754

    • Intra-rater:

      • Correlation for same analysts on several occasions

      • =0.916

    • Validity:

      • correlation with actual performance

      • = 0.769

    Stanton and Young, 1992

    How compare single data points l.jpg
    How compare single data points? typing

    • Models typically produce a single prediction

    • How can one value be compared against a set of data?

    • How can a null hypothesis be proved?

    Liao and milgram 1991 l.jpg
    Liao and Milgram (1991) typing

    A-D-*sd A-D A-D+*sd A A+D-*sd A+D A+D+*sd


    Defining terms l.jpg
    Defining terms typing

    • A = Actual values, with observed standard deviation (sd)

    • D = Derived values

    •  = 5% (P < 0.05 to reduce Type I error)

    •  = 20% (P<0.2 for Type II error)

    Acceptance criteria l.jpg
    Acceptance Criteria typing

    Accept Ho if: A-D+  *sd < D< A+D-  *sd

    Reject Ho if: D < A-D-  *sd

    Reject Ho if: D > A-D+  *sd

    Analytical prototyping l.jpg
    Analytical Prototyping typing

    • Functional analysis

      • Define features and functions

      • Development of design concepts, e.g., sketches and storyboards

  • Scenario-based analysis

    • How people pursue defined goals

    • State-based descriptions

  • Structural analysis

    • Predictive evaluation

    • Testing to destruction

  • Analytical prototyping199 l.jpg
    Analytical Prototyping typing

    • Functional analysis

    • Scenario-based analysis

    • Structural analysis

    Rewritable routines l.jpg
    Rewritable Routines typing

    • Mental models

      • Imprecise, incomplete, inconsistent

    • Partial representations of product and procedure for achieving subgoal

    • Knowledge recruited in response to system image

    Simple architecture l.jpg

    Action to change machine state typing

    Rewritable Routines



    Current State



    Possible States

    Relevant State

    Simple Architecture

    Global prototypical routines l.jpg
    Global Prototypical Routines typing

    • Stereotyped Stimulus-Response compatibilities

    • Generalisable product knowledge

    State specific routines l.jpg
    State-specific Routines typing

    • Interpretation of system image

      • Feature evolution

    • Expectation of procedural steps

    • Situated / Opportunistic planning

    Describing interaction l.jpg
    Describing Interaction typing

    • State-space diagrams

    • Indication of system image

    • Indication of user action

    • Prediction of performance

    State space diagram l.jpg

    0 typing

    Waiting for: Raise lid

    Waiting for: Play Mode

    Waiting for: Enter

    Waiting for: Skip forward

    Waiting for: Skip back

    Waiting for: Play

    Waiting for: Stop

    Waiting for: Off

    Task: Press ‘Play’

    Time: 200ms

    Error: 0.0004



    State-space Diagram

    • State number

    • System image

    • Waiting for…

    • Transitions

    Developing models l.jpg
    Developing Models typing







    Recall plan:


    Wrong plan:








    Press play:






    Press Playmode:








    Press Playmode:


    Press Enter:











    Press Play:


    Press Other Key:


    Results l.jpg
    Results typing

    What is the point l.jpg
    What is the point? typing

    • Are these models useful to designers?

    • Are these models useful to theorists?

    Task models problems l.jpg
    Task Models - problems typing

    • Task models take time to develop

      • They may not have high inter-rater reliability

      • They cannot deal easily with parallel tasks

      • They ignore social factors

    Task models benefits l.jpg
    Task Models - benefits typing

    • Models are abstractions – you always leave something out

    • The process of creating a task model might outweigh the problems

    • Task models highlight task sequences and can be used to define metrics

    Task models for theorists l.jpg
    Task Models for Theorists typing

    • Task models are engineering approximations

      • Do they actually describe how human information processing works?

        • Do they need to?

      • Do they describe cognitive operations, or just actions?

    Some background reading l.jpg
    Some Background Reading typing

    Dix, A et al., 1998, Human-Computer Interaction (chapters 6 and 7) London: Prentice Hall

    Anderson, J.R., 1983, The Architecture of Cognition, Harvard, MA: Harvard University Press

    Card, S.K. et al., 1983, The Psychology of Human-Computer Interaction, Hillsdale, NJ: LEA

    Carroll, J., 2003, HCI Models, Theories and Frameworks: towards a multidisciplinary science, (chapters 1, 3, 4, 5) San Francisco, CA: Morgan Kaufman