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 Models or 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