Models of Human Performance Dr. Chris Baber. 2 Objectives Introduce theory-based models for predicting human performance Introduce competence-based models.

Models of Human Performance

Dr. Chris Baber

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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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Some Background Reading

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

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Assessment

1½ hour written examination

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Task Models

Researcher’s Model of User, in terms of tasks

Describe typical activities

Reduce activities to generic sequences

Provide basis for design

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Pros and Cons of Modelling

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

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Generic Model Process?

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

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Hierarchical Task Analysis

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

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Hierarchical Task Description

1 .0S w itch on O H P

2 .0C h eck p ro jec tion

3 .0P lace fo il on O H P

4 .0F ocu s p ro jec tion

0 .0P resen t O H P s lid es

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The “Analysis” comes from plans

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

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Performance vs. Competence

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.

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Production Systems

Rules = (Procedural) Knowledge

Working memory = state of the world

Control strategies = way of applying knowledge

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Production Systems

Architecture of a production system:

Rule base Working Memory

Interpreter

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The Problem of Control

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

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Production Rules

IF conditionTHEN action

e.g., IF ship is docked

And free-floating shipsTHEN launch shipIF dock is free

And Ship matchesTHEN dock ship

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States, Operators, And Reasoning (SOAR)

http://www.isi.edu/soar/soar.html

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States, Operators, And Reasoning (SOAR)

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

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Soar as a Unified Theory of Cognition 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

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Young, R.M., Ritter, F., Jones, G.  1998  "Online Psychological Soar Tutorial"

available at: http://www.psychology.nottingham.ac.uk/staff/Frank.Ritter/pst/pst-tutorial.html

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SOAR Activity 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.

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SOAR Activity

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

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SOAR ArchitectureChunkingmechanism

Production memory

Pattern ActionPattern ActionPattern Action

Decision procedure

Working memoryManager

Preferences Objects

Conflict stack

Working memory

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Explanation

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

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3 levels in soar

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

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How does it work?

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

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Impasses

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

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Learning

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

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Conclusions

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