Cognitive modelling, Users models and

Mental models

What’s cognitive modelling ?

The human information processing approach

Cognitive Models of Users in HCI

Knowledge, representations and Mental Models

Conceptual models

What’s cognitive modelling ?

Cognitive modelling is a discipline based on : Experimental cognitive psychology and Artificial intelligence and linguistics methods

In HCI the main objective has been to understand and represent how humans interact with computers

Knowledge about this processes depend strongly on the particular model of cognition chosen

56’ Information processing system 90´Distributed Cognition (Hutchins, E.) 90’ Situated action (Suchman, L.) 90´Activity theory (Kaptelinin, V. Engeström, Y. )

Information processing approach

During 1960 and 1970 the main paradigm in cognitive psychology was to characterise humans as information processors

Information enters and exists the human mind through a series of ordered processing stages

Information is unidirectional and sequential and each stage takes a certain amount of time

Information processing model has been highly influencial in shaping the development of cognitive models of the user in HCI

Information Processing Psychology : ingredients

Model from the computer In contrast to previous cognitive

models that were often statistical

A modelling language - production rules

In contrast to verbal descriptions

A qualitative method to derive information processes

In contrast to quantitative methods

Model from the computer

But people are not computers, we have to use reverse engineering to understand the mechanisms by which they proceed:

Define problem Identify process Derive specific strategy from

process Derive general cognitive

architecture from several studies

Modelling language

If-then rules. The current state is matchted towards the system of rules. The first rule that ”matches” the current state is ”fired”

Then a new state results, that is matched… What does this remind us of?

A qualitative method to derive information processes

The think-aloud protocol was used to elicit data on sequential problem solving

Hypotheses: people expressed (parts of) that what existed in their working memory –

i.e. part of the current ”knowledge state”

Defining problem and problem space

A problem exists when you have a goal and an initial state. The initial state does not correspond to the goal and you do not know how to get from the initial state to the goal

A problem space consists of the hypothetical states that a problem solver goes through in its processing/transformation of the initial state to the goal state.

Ex. Problem space = intitial state + operations required to reach goal state

Example of problem- Tower of Hanoi

You have three disks on a peg (A) as in the figure. These should be moved to the right peg (C). You are only allowed to move one disk at a time. You can only place a smaller disk on top of a bigger one.

A B C

Think aloud protocol : example Tower of Hanoi

First I put the smallest one here (on C)

Then I put the next smallest here (on B)

Then I take the biggest one -

O no, that is not allowed,

OK I move the smallest back to A

And the next smallest to C

Then I take the smallest to B

And the next smallest to - where should it go...

Production rules that produce the think aloud protocol

IF goal achieved THEN end

If disc1 free THEN move disc1

If move disc1 THEN check if C is possible

IF C possible THEN move disc1 to C

IF C is not possible THEN move disc1 to A

If disc2 free THEN move disc2

If move disc2 THEN check if B is possible

If B empty, THEN move disc2 to B

IF disc3 free THEN move disc3

IF move disc 3 THEN check if C is possible

But rules are not sufficient

We need a system to interpret the rules!

What can the system ”perceive”?

How should the objects be represented?

In what order are the productions tested?

How will the actions performed be remembered?

A cognitive architecture

Defines how rules are interpreted In what order they are taken What conditions prevail for how

the rules may be written. For instance how many conditions and actions are possible for one rule

How the results of actions are stored

A cognitive architecture for Human Information Processing

Must comply with knowledge about human beings

Knowledge from various sources :

Senso-motoric Attention Perception Memory Metacognition

Information processing applied to HCI

Quasi-empirical approach GOMS

Analyses a task from an expert’s actions:

Goals, Operations, Methods and Selection rules

Further applications of GOMS: Cognitive walkthrough - what will a

user find difficult in the system? Goals, operations, methods

analysed with respect to the designer’s knowledge about the user

Further applications of GOMS

Keystroke level calculations: How long will it take to perform a task with the system?

Has been used to compare different system solutions, for instance for telephone operators asking caller’s questions

A small change in the time taken may mean much when many small tasks are performed by many persons

Key-stroke level of GOMS

Task: Copy a word and position it at some place at the text

Method: Get the operations from the menu 1. Time to identify the word 2. Time to mark the word 3. Time to move to the menu and find the word ”copy” 4. Time to click on ”copy” 5. Time to go to the position in the text were the word

should be placed 6. Time to click in order to move the cursor to this place 7. Time to move to the menu and get the command

”paste”. 8. Time to click for placing the word. 9. Time for checking that the result is OK

The time for the hand movements is calculated according to ”Fitt’s law”

Cognitive Models of users in HCI

Focus on the interface and interest in measurements (errors, time) predictions of user performance, how easy an interface will be to learn, without instruction, or without manual.

Usability is a measure of the ease of use once learning is in some sense complete

Tasks Slips or accidental mistakes

Hierarchical representation of the user’s task and goal structure

GOMS : Goals, operations, methods and selection rules (Card, Moran and Newell, 1983)

Linguistic and grammatical models Physical and device-level models

Knowledge

How is knowledge organised in the users’ mind?

How is knowledge represented in memory ?

Analogical representations : images Schema and scripts

Propositional representations : language Mental models

Distributed representations : nodes and links-

Semantic networks

Representations

The ability to represent perceptions, experiences… in some medium other than that in which they have occurred…

People construct representations that facilitate their interaction with events and absent in space and time…

Representations … Capture the important, critical features of

the represented world while ignoring the irrelevant,

Are appropriate for the person, Are appropriated for the task, enhancing

the ability to discover relevant regularities and structures.

Mental Models (MM)

“The model people have of themselves, others, the environment, and the things which they interact. People form mental models through experience, training and instruction” (Norman, 1988 p.17).

MM are either analogical representations or a combination of analogical and propositional representations. They are distinct but related to images (Johnson-Laird, 1983,1988).

MM are used to make inferences or a prediction

Images are one-off representation

Mental Models (cont’)

Two main types of MM identified when interacting with computers :

Structural – how does it works ? components and parts of a device Context-free

Functional or “task-action mapping model” – how to use it ?

connections between tasks and actions context dependent

Utility of MM in HCI :People do use MM but they often are incomplete, unstable, vague

Key points

There are three types of mental representations :

Analogical, propositional and distributed

General knowledge is stored as schemata, which when activated, can be used to construct mental models

Mental models enable people to generate descriptions and explanations about systems and to make predictions about future events

Structural models describe how devices work Functional models describe how to use a

system

Most people’s understanding of devices or systems is functional

Conceptualizing users’ knowledge in terms of mental models can help deigners to develop appropriate interfaces

Conceptual Modelsor modeling a Mental model

A generic term that describes the various way in which computer systems are understood by different people

The way users conceptualize and understand the system

The way designers conceptualize and view the system

The problem is to design the system so that :

It follows a consistent coherent conceptualisation –a design model- and,

the user can develop a mental model of that system – a user model- consistent with the design model (cf. Norman, 1986 p.46)

Conceptual Models (cont’)

Ideally, the user model should completely map onto the design model (system image)

Learnability Functionality Usability

But… Users develop a partial mental model

of the design model Their understanding and ability to use

the system is limited Design model could be inappropiated

for what the user wants to achieve