Cognitive Models

Cognitive Models

Contents • Cognitive Models • Device Models • Cognitive Architectures

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

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

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

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

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

GOMS • GOMS models goals as a hierarchy GOAL: 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]

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

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

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

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

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

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) ))

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)) )

Cognitive Complexity Theory THEN ((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)))

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

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

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

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

BNF draw-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

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

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

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

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’

Task-action Grammar • TAG can also represent world knowledge • Command Interface 1 movement[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’

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 2 movement[Direction] := command[Direction] + distance + RETURN command[Direction=forward] := ‘FORWARD’ command[Direction=backward] := ‘BACKWARD’ command[Direction=left] := ‘LEFT’ command[Direction=right] := ‘RIGHT’

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

Task-action Grammar movement[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

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

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

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

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

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

Keystroke-level Model • Move hand to mouse H[mouse] • Position after bad character PB[LEFT] • Return to keyboard H[keyboard] • Delete character MK[DELETE] • Type correction K[char] • 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

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

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

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

Three-state Model State 1 tracking State 2 dragging Button down Mouse Transitions Button up State 1 tracking State 0 No tracking State 2 dragging Touch screen Button down Light pen Transitions Button up Remove pen

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

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

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

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

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

Cognitive Models