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4441 Midterm - Final Prep > Chapter #7 Modeling Interaction > Flashcards

Chapter #7 Modeling Interaction Flashcards

1
Q

Descriptive Model

A

reduction or partition of a problem space

Examples: Politics, GroupWare, Keyboards, Two Handed input, Circumplex model of affect, Graphical input

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2
Q

CSCW

A

Also know as Groupware
Name (computer supported cooperative work)

Def: People working collaboratively with computer technology

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3
Q

Quadrant Model of Groupware

A

A descriptive model

2x2 space
Same time, Different time
Same place | | |
Different place | | |

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4
Q

Critiquing the Model, Groupware

A

Lots of new ways to collaborate through technology didn’t exist when this model came out, hence some new forms of collaboration don’t fit in just one category.

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5
Q

KAM

A

Key Action Model
A descriptive model

Symbol Keys: Produce graphical symbols, ex A
Executive Keys: Does an execution, ex Esc
Modifier Keys: Modifies the affects of other keys, Ex Crtl

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6
Q

Critiquing the Model, KAM

A

Some keys seem like they belong in multiple categories.
Also the right hand is noted to be super busy with lots of executive keys

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7
Q

Study of hand usage is called

A

laterality or bimanual control

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8
Q

Guiard’s Model of Bimanual Control

A

Descriptive model

Non-preferred hand: does corese movements, sets frame and leads the preferred hand.

Preferred hand: does fine movements, works within the frame and follows the non-preferred hand.

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9
Q

Critiquing the Model, Guiard’s Model

A

Developed in phycology and did not do testing with computers

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10
Q

What’s the argument for where scrolling should be?

A

Non preferred hand since the preferred hand, normally the right, tends to be overloaded with the mouse and right side of the keyboard

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11
Q

CMA

A

Circumplex Model of Affect
2D descriptive model of human effect or emotion

Horizontal axis: pleasure vs displeasure
Vertical axis: arousal vs sleep

                 high arousal displeasure          +          pleasure
                      sleep
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12
Q

Applications of CMA

A

How expressive lighting in a robot can express emotion
emotion through shape-changing interfaces
emotional state in play environments
etc

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13
Q

SAM

A

Self Assessment Manikin

Follows the axis from CMA, with 2x9
such that it has a manakin conveying each emotional level

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14
Q

Three State Model of Graphical Input

A

Developed by Buxton
0: out of range (ie mouse off desk)
1: tracking; mouse moving along desk
2: dragging; mouse moving while holding down click

Ex: new touchpads and that invention of the tactile touchpad mouse

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15
Q

Who also had a three state model but for the light pen?

A

Newman

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16
Q

Predictive model

A

Uses predictive values to predict the outcome of dependent variables

Exact definition: Predicts the outcome on a criterion variable (aka
dependent variable or human response) based on the value
of one or more predictor variables

uses numbers and is an equation

17
Q

Linear Prediction Equation

A

basically y = mx+b

expresses a linear relationship between a predictor variable (x) and a criterion variable (y)

used in linear regression

18
Q

Fitts law, 3 applications

A

Predictive equation for determining alternatives
Throughput as a dependent variable
Check if conforms to Fitts law

19
Q

Fitts Law, Task Paradigms

A

Serial Task: Ex hit one thing on one side then the other, and go back and forth
Discrete Task: Two things to hit, one on each side, a light will light up on one side at a time and you have to hit that side

19
Q

Fitts Index of Difficulty

A

ID log_2(A/W + 1), unit is bits

19
Q

Fitts’ Index of Performance

A

TP = IDe/MT

20
Q

Throughput and the Speed-Accuracy Tradeoff

A

TP = (log_2(Ae/4.1333*SDx + 1)) / MT

21
Q

Choice Reaction Time

A

n stimuli with n ways to respond to each
Ex fingers to each light

Equation: RT = a + blog_2(n+1)

22
Q

KLM

A

Keystroke Level Model

predicts error free task completion times

t_execute = t_k + t_p + t_h + t_ d + t_m + t_r

K  keystroking P  pointing H  homing
– D  drawing M  mental prep R  system response

23
Q

Expert Behaviour in KLM

A

where someone who is experienced doesn’t have to think before doing the action. Ex knowing what buttons to press to quick write beep

Involves the M and Mp for mental processing between actions like k, p, h,d

24
Q

Two approaches for KLM modeling with M_P

A

All-In: Include Mp at every reasonable juncture (novice user)
All-Out: remove all Mp (expert)

25
Q

Mental Operator for Visual Search, Mv

A

Mv, mental time it takes to visual search.

Primarily in things where you enter a letter and it gives word predictions. Hence taking time to read suggestions is Mv. But overall it should save time

26
Q

Skill Acquisition

A

We start as novices with poor performance, over time with practice we gain skills and preform better and potentially become experts.

Dependent Variable: proficiency
Independent: Amount of Practice

27
Q

Power Law of Learning

A

Relationship between practice and proficiency is non-linear

Tn = T1 * n^a , Time specific one, curves left top to down so like slope
Sn = S1 * n^a , Speed specific one, curves left boom to top so like x

28
Q

Log-Log Model

A

In power law of learning

In a graph, whether speed or time variation, the x and y are put into log scales then the relationship between data is linear

29
Q

More than one predictor

A

Essentially allows multiple predictor values in an equation
ex y = b1x1 + b2x2 + ….

Also known as multiple regression

30
Q

Stepwise Linear Regression

A

Variables are added one at a time to determine which has the highest r (variability), then tested in that manner

31
Q

MCM

A

Model Continuum Model

Graph that shows the differences between descriptive and predictive models

Analogy -> categories / Design spaces -> Stats -> Equations

4441 Midterm - Final Prep (7 decks)

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