Cognitive Models
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Transcript of Cognitive Models
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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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