Chapter 2: Mental models and cognitive task analysis literature

Chapter overview and links to contents

The thesis needed to be related to and grounded in some academic literature, and this is rarely straightforward for an interdisciplinary thesis. Searching through the literature of AI and HCI and related topics, it seemed that the topics of mental models and cognitive task analysis were potentially related to the thesis; but that literature is very complex, with diverse strands. §2.1 identifies and tries to clarify the various threads of literature. It also incorporates discussion of other review papers.

2.1 An outline of the complexity of the literature
2.1.1 Introduction
A note on some of the terms in the literature
2.1.2 Mental models and (cognitive) task analysis
2.1.3 Formalisable models
2.1.4 Engineering and operator models
2.1.5 More general mental models
2.1.6 Models used in training or learning
2.1.7 Models derived from real users or operators
2.1.8 Expert systems for modelling operators
2.1.9 Models of error-prone human operators
2.1.10 Ragged edges

The next task was to bring greater order into the literature. It emerged that one could use leads from the literature itself to help in the organisation and classification. This was through the idea of the purpose of a model.

2.2 Classifying mental models and their literature
2.2.1 The owner and the object of a model
2.2.2 The purpose of a model
Models as means of communication
Models as an aid to understanding
Models as tools for prediction and control
Models as devices for training

At this point the way was clear to proceed with a substantial review of the literature selected, in terms of the objectives of the thesis.

2.3 A more detailed review of exemplary literature
2.3.1 Decomposition formalisms
2.3.1.1 The GOMS family of models
The Keystroke-Level Model (KLM)
2.3.1.2 Command Language Grammar (CLG)
2.3.1.3 Cognitive Complexity Theory
2.3.1.4 Task-Action Grammars (TAG)
2.3.1.5 General points about formalisms
2.3.2 Models of cognition
2.3.2.1 The Model Human Processor (MHP)
2.3.2.2 Programmable User Models (PUMs)
2.3.2.3 ACT*
2.3.2.4 Interacting Cognitive Subsystems
2.3.2.5 General points about models of cognition
2.3.3 Important features of cognition in complex systems
2.3.3.1 Individuality
2.3.3.2 Skills, rules and knowledge
2.3.3.3 Mapping cognitive demands
2.3.3.4 Modelling the operator's view of the structure of a system
2.3.3.5 Qualitative models and reasoning
Non-monotonic reasoning
2.3.3.6 General points about important features of cognition

§2.4 gathers together published critique along with some points generalised from the review.

2.4 Reviews and criticisms of the literature
2.4.1 A ``trade-off'' analysis of cognitive models
2.4.2 ``Literal models'' promise more than they can deliver
2.4.3 They are not yet of practical use to designers
2.4.4 Why formal models are not very useful
The grain of analysis
User consistency
Predicting behaviour without ``world knowledge''
User involvement
Compatibility with the design process
Understanding the task
Practical complexity
The areas addressed by a method
Summary of Booth's criticisms
2.4.5 ``Design and evaluation techniques'' are not used anyway
2.4.6 Failure to appreciate variety of possible solutions
2.4.7 Problems of validation without theoretical foundations
2.4.8 Ease of formal analysis suggests simplicity of the system
2.4.9 The expert is a learner at the edges

The next section, considering the evolving nature of the literature, reviews what the literature has to say about possible and desirable developments.

2.5 Where does the literature point to?
2.5.1 What do authors consider desirable?
2.5.2 Needs

§2.6 takes up one particular issue that is highly relevant. It shows how nearly all current approaches neglect the proper consideration of representation, and picks up points from general review, above, emphasising difficulties with formal task analysis techniques such as GOMS and TAG.

2.6 Representation in mental models literature
2.6.1 Difficulties with formal techniques
2.6.2 Consistency in HCI design
2.6.3 Representation in complex tasks
2.6.4 Representation in context