Cognitive Models

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Contents

Cognitive Models Device Models Cognitive Architectures

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Cognitive Models

Cognitive models are used to represent the users of interactive systems Models of user’s tasks and goals Models of the user-system grammar Models of human motor skills Cognitive architectures which

underlie these models

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Unit Tasks The models of tasks and goals all

decompose these into simpler parts One is always faced with the question of

to what depth the decomposition should proceed

This is a question of granularity and it can proceed to the lowest level operations

We define the unit task as the most abstract task a user can perform that does not require any problem solving on the part of the user

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GOMS This models goal and task hierarchies It stands for Goals, Operators, Methods,

and Selection Goals

These describe what the user wants to achieve

They also represent a memory point which can be used to evaluate what has been achieved

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GOMS Operators

These are the simplest actions the user performs to use the system

Pressing the ‘X’ key would be an operator Methods

Often there is more than one way to accomplish a goal

Help could be by hitting F1 or by clicking the help button

These are referred to as two methods for the same goal

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GOMS

Selection Whenever there is more than one

method to achieve a goal, a selection must be made

The choice of methods usually depends on the state of the system and the particular user

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GOMS

GOMS models goals as a hierarchyGOAL: Iconize-window

[select GOAL: use-close-method Move-mouse-to-window-header Pop-up-menu Click-close-option GOAL: use-L7 method

Press-L7-key]

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GOMS The dots indicate the hierarchical level

of each goal GOMS uses this to decompose large

goals into sub-goals Note the use of select to indicate that

there is a choice of methods A typical GOMS analysis breaks a high-

level goal into unit tasks which are further decomposed into basic operators

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GOMS Uses The analysis of GOMS goal structures

can be used to create measures of performance Assigning a time to each operator and

summing the result yielded estimates within 33% of the actual values

The depth of the hierarchy can be used as a measure of how much the user must store in short-term memory

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GOMS Uses The selection rules can be used to

predict the actual commands which will be used In practice this allowed predictions of

commands that were 90% accurate The GOMS model has served as a basis

for other models It can be combined with other models to

make more advanced predictions

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Cognitive Complexity Theory This is an extension of the GOMS

model which provides improved prediction

It provides two parallel descriptions Of the user’s goals Of the system

The descriptions consist of a series of production rules of the form If condition then action

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Cognitive Complexity Theory

These rules are written in a LISP-like language

Let’s look at the description of how we would insert a missing space in text using the vi text editor

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Cognitive Complexity Theory

(select-insert-space

IF(AND (TEST-GOAL perform unit task)

(TEST-TEXT task is insert space)

(NOT TEST-GOAL insert space)

(NOT (TEST-NOTE executing insert space)) )

THEN ( (ADD-GOAL insert space)

(ADD-NOTE executing insert space)

(LOOK-TEXT task is at %LINE %COL) ))

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Cognitive Complexity Theory(INSERT-SPACE-DONE

IF (AND (TEST-GOAL perform unit task)

(TEST-NOTE executing insert space)

(NOT (TEST-GOAL insert space)) )

THEN ( (DELETE-NOTE executing insert space)

(DELETE-GOAL perform unit task)

(UNBIND %LINE %COL) ))

(INSERT-SPACE-1

IF (AND (TEST-GOAL insert space)

(NOT (TEST-GOAL move cursor))

(NOT (TEST-CURSOR %LINE %COL)) )

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Cognitive Complexity TheoryTHEN ((ADD-GOAL move cursor to %LINE %COL)))

(INSERT-SPACE-2

IF (AND (TEST-GOAL insert space)

(TEST-CURSOR %LINE %COL) )

THEN ((DO-KEYSTROKE ‘I’)

(DO-KEYSTROKE space)

(DO-KEYSTROKE ESC)

(DELETE-GOAL insert space)))

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Cognitive Complexity Theory CCT allows you to model GOMS like

hierarchies CCT also allows you to model

concurrent goals since more than one rule can be matched at the same time

However, the main use of CCT is in measuring the complexity of the interface

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Cognitive Complexity Theory CCT can be used to model the system

as well If this is done, it can be used to predict

the difficulty in translating from the user’s model to the system model

The sheer size of the CCT description is a predictor of the complexity of the operations necessary to achieve a goal

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Linguistic Models

The user’s interaction with a computer is similar to a language

Therefore, several modeling techniques have been built on interaction as a language

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BNF Backus-Naur Form was originally

developed to describe the syntax of programming languages

It can be used equally well to describe the interaction between a user and a computer

Consider the case of drawing a line in a graphics system

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BNFdraw-line ::= select-line + choose-points +

last-point

select-line ::= position-mouse + CLICK-MOUSE

choose-points::= choose-one |

choose-one + choose-points

choose-one ::= position-mouse + CLICK-MOUSE

last-point ::= position-mouse +

DOUBLE-CLICK-MOUSE

position-mouse ::= empty | MOVE-MOUSE +

position-mouse

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BNF BNF represents the users action

but not the systems responses The complexity of the description

provides a crude measure of the complexity of the task

BNF is also a good way to unambiguously specify how a user interacts with a system

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Task-action Grammar

While BNF can represent the structure of a language, it cannot represent consistency in commands or Any knowledge the user has of the

world The task-action grammar (TAG)

addresses both of these problems

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Task-action Grammar

Consider using BNF for the UNIX copy, move, and link commands

copy ::= ‘cp’ + filename + filename

| ‘cp’ + filenames + directory

move ::= ‘mv’ + filename + filename

| ‘mv’ + filenames + directory

link ::= ‘ln’ + filename + filename

| ‘ln’ + filenames + directory

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Task-action Grammar

The TAG description of the same commands makes the consistency far more apparent

file-op[Op] := command[Op] + filename + filename

| command[Op] + filenames + directory

command[Op=copy] := ‘cp’

command[Op=move] := ‘mv’

command[Op=link] := ‘ln’

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Task-action Grammar

TAG can also represent world knowledge

Command Interface 1movement[Direction]

:= command[Direction] + distance + RETURN

command[Direction=forward] := ‘go 395’

command[Direction=backward] := ‘go 013’

command[Direction=left] := ‘go 712’

command[Direction=right] := ‘go 956’

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Task-action Grammar The previous interface could represent

addresses of functions to call to perform actions Let’s look at a second version of the interface Command Interface 2movement[Direction]

:= command[Direction] + distance + RETURN

command[Direction=forward] := ‘FORWARD’

command[Direction=backward] := ‘BACKWARD’

command[Direction=left] := ‘LEFT’

command[Direction=right] := ‘RIGHT’

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Task-action Grammar

The second form of the interface is preferable and takes advantage of the words (forward, back, etc.) the user already knows

We can rewrite the previous TAG to show the information that the user already knows and does not have to learn

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Task-action Grammarmovement[Direction]

:= command[Direction] + distance + RETURN

command[Direction] :=

known-item[Type=word,Direction]

* command[Direction=forward] := ‘FORWARD’

* command[Direction=backward] := ‘BACKWARD’

* command[Direction=left] := ‘LEFT’

* command[Direction=right] := ‘RIGHT’ The rules with asterisks can be generated from

the second rule combined with the user’s knowledge

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Contents

Cognitive Models Device Models Cognitive Architectures

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GUI Systems BNF and TAG were designed for

command line interfaces While pressing a button is a reasonable

action, moving a mouse one pixel is less obvious

In GUI systems, the buttons are virtual and depend on what is displayed at a particular screen position

The keystroke model allows us to model low-level interaction with a device

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Keystroke-level Model This is used for modeling simple

interaction sequences on the order of a few seconds

It does not extend to more complex operations such as producing an entire diagram

The model decomposes actions into 5 motor operators, a mental operator and a response operator

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Keystroke-level Model K

Keystroke operator B

Pressing a mouse button P

Pointing or moving the mouse over a target H

Homing or switching the hand between mouse and keyboard

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Keystroke-level Model D

Drawing lines with the mouse M

Mentally preparing for a physical action

R System response User does not always wait for this as

happens in continuous typing

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Keystroke-level Model

Consider using a mouse based editor to correct a single character error Point at the error Delete the character Retype it Return to the previous typing point

The following notation will capture this

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Keystroke-level Model1. Move hand to mouse H[mouse]

2. Position after bad character PB[LEFT]

3. Return to keyboard H[keyboard]

4. Delete character MK[DELETE]

5. Type correction K[char]

6. Reposition insert point H[mouse]MPB[LEFT] Timings for individual operations can be measured These timings can then be summed to create the total

time for the overall operation Alternative ways of performing an action can have

their times computed and compared to find which one is more efficient

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Three-state Model

Pointing devices like mice, trackballs, and light pens all behave differently as far as the user is concerned

The three-state model is used to capture the behaviour of these devices State 1

Moving the mouse with no buttons pressed This usually moves the pointer on the screen

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Three-state Model State 2

Depressing a button over an icon and then moving the mouse

This is usually thought of as dragging an object

State 0 This is for a light pen when it is not

touching the screen In this state the location of the pen is not

tracked at all

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Three-state Model A touch screen behaves like a light pen

with no button to press This means that a touch screen is in

state 0 when the finger is off the screen When the finger touches the screen, it

can be tracked and is in state 1 Thus,

a touch screen is a state 0-1 device A mouse is a state 1-2 device

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Three-state Model

State 1tracking

State 2dragging

Button down

Button up

MouseTransitions

State 1tracking

State 2dragging

Button down

Button up

Light penTransitions

State 0No tracking

Touch screen

Remove pen

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Fitt’s Law Fitt’s law states that the time to move a

pointer to a target of size S at a distance D from the starting point is

a + b log2(D/S + 1) Where a and b are constants dependent on the

type of pointing device and the skill of the user The insight provided by the three state model

is that a and b also depend on the state This is due to dragging being more accurate

than the original pointing which does not have as good feedback

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Contents

Cognitive Models Device Models Cognitive Architectures

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Cognitive Architectures

The models we have looked at up to this point have implied a model of the mental processes of the user

For example, GOMS implied a divide and conquer approach

We will now look at a different model of the user’s cognitive processes

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The Problem Space Model Rational behaviour is defined as behaviour

directed to achieving a specific goal This is the behaviour you would expect of a

human or a knowledge based system This is in contrast to the problem solving

modeled as a search of a solution space until a solution is found

This search is performed by traversing the space until a solution is found

This is a brute force search and is not rational behaviour in seeking a solution to a goal

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The Problem Space Model

This model can be adapted to the rational behaviour of humans

A problem space consists of A set of states A set of operations to go from one

state to another A goal is a subset of states which must

be reached for the goal to be achieved

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The Problem Space Model To solve a problem in this model

Identify the current state Identify the goal Devise a set of operations which will

move from the current state to the goal state

This model is inherently recursive If you cannot find the operations to

achieve the goal then this becomes a new recursive problem to be solved

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