cmsc434 week 07 | lecture 19 | nov 4, 2014 human ... · card, moran, and newell, the psychology of...
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Human Computer Interaction Laboratory
@jonfroehlich Assistant Professor Computer Science
CMSC434 Introduction to Human-Computer Interaction
Week 07 | Lecture 19 | Nov 4, 2014
Human Information Processing
TODAY
1. Wrapping up Fitts’ Law
2. Improving Pointing
3. Human-Information Processing
4. GOMS Model
5. TA06 Check-In
wrapping up fitts’ law
FITTS’ LAW
A
(amplitude)
W
MT
(width)
There are different formulations in HCI
Different
formulati
ons for
fitt’s law;
this one
popular
movement time
Which will be faster on average?
pie menu (bigger targets & less distance)
[adapted from Hartmann, Landay]
FITTS’ LAW IN PRACTICE
PIE MENU VS. LINEAR MENU
USING A PIE MENU IN PRACTICE
Source: http://youtu.be/gzAN0E-xOyA
The Sims
Rainbow 6
Maya
[adapted from Landay]
Why aren’t Pie Menus more widely adopted?
OTHER PIE MENU EXAMPLES
MARKING MENUS
Source: http://youtu.be/8c58bN6ajJ4
improving pointing
TARGET ACQUISITION
[adapted from Findlater]
SUB-MOVEMENT ANALYSIS
[adapted from Findlater]
SUB-MOVEMENT ANALYSIS
[adapted from Findlater]
Bubble Cursor Grossman & Balakrishnan, CHI’05
leah findlater
alex jansen
kristen shinohara
morgan dixon
peter kamb
joshua rakita
jacob o. wobbrock
enhanced area cursors reducing fine pointing demands for
people with motor impairments
21
ENHANCED AREA CURSORS
[adapted from Findlater]
[adapted from Findlater]
ENHANCED AREA CURSORS: FOUR TYPES
[adapted from Findlater]
[adapted from Findlater]
[adapted from Findlater]
[adapted from Findlater]
[adapted from Findlater]
evaluation
lessen effects of small target size?
reduce need for corrective-phase pointing?
reduce need for accurate, steady clicking?
do the new cursors…
[adapted from Findlater]
task
36
12 participants
parkinson’s disease
multiple sclerosis
cerebral palsy
de quervain’s stenosynovitis
spinal cord injury
friedreich’s ataxia
tetraplegia
muscular dystrophy
[adapted from Findlater]
3 target sizes
12 participants
4px
8 px
16 px
[adapted from Findlater]
3 target sizes
3 target spacings
12 participants none
half-target
width
full-target
width [adapted from Findlater]
3 target sizes
3 target spacings
2 levels of clutter
12 participants
[adapted from Findlater]
3 target sizes
3 target spacings
2 levels of clutter
6 cursors
12 participants click-and-cross
point
cross-and-cross
motor-magnifier
visual-motor-
magnifier
bubble
[adapted from Findlater]
lessen effects of small target size?
do the new cursors…
[adapted from Findlater]
speed
point bubble motor-
magnifier
visual-
motor-
magnifier
click-
and-
cross
cross-
and-
cross
mean t
rial tim
e (
seco
nd
s)
error bars:
standard error
0
2
4
6
8
4 pixels 8 pixels 16 pixels
[adapted from Findlater]
0
2
4
6
8
speed
point bubble motor-
magnifier
visual-
motor-
magnifier
click-
and-
cross
cross-
and-
cross
mean t
rial tim
e (
seco
nd
s)
4 pixels 8 pixels 16 pixels
fastest for
smaller sizes
s
s s error bars:
standard error
[adapted from Findlater]
0
2
4
6
8
speed
point bubble motor-
magnifier
visual-
motor-
magnifier
click-
and-
cross
cross-
and-
cross
mean t
rial tim
e (
seco
nd
s)
reduced effect of
small target size
s
s s error bars:
standard error
4 pixels 8 pixels 16 pixels
[adapted from Findlater]
0
2
4
6
8
speed
point bubble motor-
magnifier
visual-
motor-
magnifier
click-
and-
cross
cross-
and-
cross
mean t
rial tim
e (
seco
nd
s)
s
s s error bars:
standard error
4 pixels 8 pixels 16 pixels
[adapted from Findlater]
0
0.1
0.2
0.3
0.4
0.5
mean e
rro
r ra
te
errors
point bubble motor-
magnifier
visual-
motor-
magnifier
click-
and-
cross
cross-
and-
cross
s
s s error bars:
standard error
4 pixels 8 pixels 16 pixels
[adapted from Findlater]
0
0.1
0.2
0.3
0.4
0.5
mean e
rro
r ra
te
reduced errors
compared to point
errors
point bubble motor-
magnifier
visual-
motor-
magnifier
click-
and-
cross
cross-
and-
cross
s
s s e e e error bars:
standard error
4 pixels 8 pixels 16 pixels
[adapted from Findlater]
lessen effects of small target size?
reduce need for corrective-phase pointing?
do the new cursors…
[adapted from Findlater]
0
10
20
30
40
50
submovement analysis m
ean n
um
ber
of
sub
mo
vem
ents
point bubble motor-
magnifier
visual-
motor-
magnifier
click-
and-
cross
cross-
and-
cross
s
s s e e e error bars:
standard error
4 pixels 8 pixels 16 pixels
[adapted from Findlater]
0
10
20
30
40
50
submovement analysis m
ean n
um
ber
of
sub
mo
vem
ents
point bubble motor-
magnifier
visual-
motor-
magnifier
click-
and-
cross
cross-
and-
cross
reduced submovements
compared to point
s
s s e e e m m
m
error bars:
standard error
4 pixels 8 pixels 16 pixels
[adapted from Findlater]
submovement analysis m
ean n
um
ber
of
sub
mo
vem
ents
point bubble motor-
magnifier
visual-
motor-
magnifier
click-
and-
cross
cross-
and-
cross
s
s s e e e m m
m
error bars:
standard error
0
10
20
30
40
50
4 pixels 8 pixels 16 pixels
extra movement
for activation
[adapted from Findlater]
submovement analysis m
ean n
um
ber
of
sub
mo
vem
ents
point bubble motor-
magnifier
visual-
motor-
magnifier
click-
and-
cross
cross-
and-
cross
s
s s e e e m m
m
error bars:
standard error
0
10
20
30
40
50
4 pixels 8 pixels 16 pixels
[adapted from Findlater]
lessen effects of small target size?
reduce need for corrective-phase pointing?
reduce need for accurate, steady clicking?
do the new cursors…
[adapted from Findlater]
visual-motor-magnifier
cross-and-cross
click-and-cross
bubble
most preferred
number of
participants
slowest, but
still preferred
55
7
3
2
0
human-information processing
Cognitive psychology is the study of higher
mental processes such as attention, language
use, memory, perception, problem solving, and
thinking.
American Psychological Association http://www.apa.org/research/action/glossary.aspx#c
Stuart K. Card Thomas P. Moran Allen Newell
Stuart K. Card Thomas P. Moran Allen Newell
PhD in psychology from CMU
Early HCI Pioneer at PARC
Distinguished Engineer at IBM
PhD in from CMU w/Herb Simon
Early HCI Pioneer at RAND/CMU
The domain of concern to us, and the subject
of this book, is how humans interact with
computers. A scientific psychology should
help us in arranging this interface so it is
easy, efficient, error-free—even enjoyable.
Card, Moran, and Newell Early pioneers of the field of HCI
Quote from: The Psychology of Human-Computer Interaction, 1983, p. vii
Card, Moran, and Newell, The Psychology of Human-Computer Interaction, p. 26
Model Human Processor The Model Human Processor
offers a simplified view of the
human processing involved
in interacting with computing
systems.
Comprises three subsystems:
1. Perceptual system
2. Motor system
3. Cognitive systems
Model Human Processor
1. The perceptual system handles sensory stimuli from the outside world 3. The motor system
controls physical actions
2. The cognitive system provides the processing needed to connect the two
Model Human Processor
1. The perceptual system handles sensory stimuli from the outside world 3. The motor system
controls physical actions
2. The cognitive system provides the processing needed to connect the two
Each subsystem has its own processor and memory
Card, Moran, and Newell, The Psychology of Human-Computer Interaction, p. 26
The Model Human Processor
P0. Recognize-act cycle of cognitive processor P1. Variable perceptual processor rate principle P2. Encoding specificity principle P3. Discrimination principle P4. Variable cognitive processor rate principle P5. Fitts’ Law P6. Power law of practice P7. Uncertainty principle P8. Rationality principle P9. Problem space principle
The Principles of Operation
Card, Moran, and Newell, The Psychology of Human-Computer Interaction, p. 26
The Model Human Processor
P0. Recognize-act cycle of cognitive processor P1. Variable perceptual processor rate principle P2. Encoding specificity principle P3. Discrimination principle P4. Variable cognitive processor rate principle P5. Fitts’ Law P6. Power law of practice P7. Uncertainty principle P8. Rationality principle P9. Problem space principle
The Principles of Operation
The time Tn to perform a task on the n th trial follows a power law: Tn = T1n -α
Card, Moran, and Newell, The Psychology of Human-Computer Interaction, p. 26
The Model Human Processor
P0. Recognize-act cycle of cognitive processor P1. Variable perceptual processor rate principle P2. Encoding specificity principle P3. Discrimination principle P4. Variable cognitive processor rate principle P5. Fitts’ Law P6. Power law of practice P7. Uncertainty principle P8. Rationality principle P9. Problem space principle
The Principles of Operation
The time Tn to perform a task on the n th trial follows a power law: Tn = T1n –α
where α = .4 [0.2 – 0.6]
POWER-LAW OF PRACTICE
The power law of practice states that the logarithm of the completion time for a particular task decreases linearly with the logarithm of the number of practice trials taken
Source: Newell & Rosenbloom, Mechanisms of skills acquisition and the law of practice, 1980
Trail Making Test Match-to-Sample Task
http://en.wikipedia.org/wiki/Match-to-sample_task
http://en.wikipedia.org/wiki/Trail_Making_Test
POWER-LAW OF PRACTICE: EXAMPLE TASKS
Card, Moran, and Newell, The Psychology of Human-Computer Interaction, p. 26
The Model Human Processor
P0. Recognize-act cycle of cognitive processor P1. Variable perceptual processor rate principle P2. Encoding specificity principle P3. Discrimination principle P4. Variable cognitive processor rate principle P5. Fitts’ Law P6. Power law of practice P7. Uncertainty principle P8. Rationality principle P9. Problem space principle
The Principles of Operation
The time Tpos to move the hand to a target of size S which lies a distance D away: Tpos = IM log2 (D/S + 0.5)
Card, Moran, and Newell, The Psychology of Human-Computer Interaction, p. 26
The Model Human Processor
P0. Recognize-act cycle of cognitive processor P1. Variable perceptual processor rate principle P2. Encoding specificity principle P3. Discrimination principle P4. Variable cognitive processor rate principle P5. Fitts’ Law P6. Power law of practice P7. Uncertainty principle P8. Rationality principle P9. Problem space principle
The Principles of Operation
A person acts so as to attain his goals through rational action, given the structure of the task and his inputs of information and bounded limitations on his knowledge and processing ability: Goals + Task + Operators + Inputs + Knowledge + Process-limits -> Behavior
GOMS Model
A GOMS model, as proposed by Card, Moran, and
Newell (1983), is a description of the knowledge that a
user must have in order to carry out tasks on a device
or system; it is a representation of the "how to do it"
knowledge that is required by a system in order to get
the intended tasks accomplished.
[Kieras, A Guide to GOMS Analysis, 1994; Card et al., The Psychology of Human-Computer Interaction, 1983]
GOMS Model
[Rogers et al., Interaction Design, Chapter 15, 2011; Card et al., The Psychology of HCI, 1986]
An attempt to model the
knowledge and cognitive
processes involved when a
user interacts with a system
1
2
3
4
Goals refers to a particular state the
user wants to achieve
Operators refers to the cognitive
processes and physical actions that
need to be performed to achieve those
goals
Methods are learned procedures for
accomplishing the goals
Selection rules are used to determine
which method to select when there is
more than one available.
GOMS Model Example 1
2
Goal: find a website about GOMS
Operators: Decide to use search
engine, decide which search engine to
use,
GOMS Model Example 1
2
3
4
Goal: find a website about GOMS
Operators: Decide to use search
engine, decide which search engine to
use, think up and enter keywords.
Methods: I know I have to type in
search terms and then press the search
button.
Selection: Do I use the mouse button
or hit the enter key?
GOMS Model
The goal of this work [GOMS modeling] is to radically
reduce the time and cost of designing usable systems
through developing analytic engineering models for
usability based on validated computational models of
human cognition and performance.
[Kieras, GOMS Models: An Approach to Rapid Usability Evaluation, http://web.eecs.umich.edu/~kieras/goms.html]
DavidKieras Professor in EECS and Psychology at the University of Michigan
GOMS Advocate
GOMS Model
GOMS is such a formalized representation that it can be
used to predict task performance well enough
that a GOMS model can be used as a substitute for
much (but not all) of the empirical user testing needed
to arrive at a system design that is both functional and
usable.
[Kieras, GOMS Models: An Approach to Rapid Usability Evaluation, http://web.eecs.umich.edu/~kieras/goms.html]
DavidKieras Professor in EECS and Psychology at the University of Michigan
GOMS Advocate
TA06 Mid-Fi Prototypes Check-In Remember: In-Class Design Critiques This Thursday!
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