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Scientific Underpinnings of
Usability Engineering
Intro Usability
Week 4
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Objectives
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After this class you will be able to (it is my
hope!):
•
•
•
•
•
Describe some eye physiology
Explain how the visual system works (somewhat)
Identify visual cues to depth
Explain some aspects of the psychology of reading
Explain how perceptual and cognitive psychology
influence HCI designs
• Have an excellent memory for “VAM”
• Discuss the importance of designing systems to match
the human.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Designing Stuff
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• In Week 1, I asked the question “What would a system
look like if we were designing it for dogs?”
– Wouldn’t be a lot of text.
– Wouldn’t require a lot of dexterity.
– Might code information in smells and tastes.
• But we’re designing systems for humans (usually!).
So it will behoove us to know something about how
human beings take in and process information.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Whole point . . .
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• Let’s design systems to fit people
instead of the other way around.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Human Information Processing
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• How do human beings take in and process
information?
– Sensory psychology – how humans transform physical
energy (e.g., light and sound waves) into sensory signals to
and in the brain.
– Perceptual psychology – how humans interpret these
sensory signals as perceptions.
– Cognitive psychology – how humans think about these
perceptions, and previous experiences, and their own mental
creations, and . . .
– Psycholinguistics – The psychology of language -- what
goes on between the time I have a thought and you have the
same (or similar!) thought, whether I say it or write it.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Eye Physiology
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Eye Muscles
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Visual Field
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Retinal Physiology
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Distribution of Rods and Cones
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Visible Spectrum
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Visual Sensitivity
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Neural Pathways
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Aftereffect
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Ambiguous Figure
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Sensation/Perception
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• POINT: Perceptions are made up of more
than just a collection of sensations!
• OTHER things influence our perceptions, e.g.,
–
–
–
–
–
Our experiences
Our biases
The context
Our current emotional state
Etc.
• So, what does that have to say about
designing human-computer interfaces???
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Perceptual Psy – Color Vision
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• Color perception – 3 types of cones (RGB)
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Perceptual Psy -- Depth
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• Different visual cues to depth
– Oculomotor vs. Visual
• Oculomotor – Lens accommodation and
extraocular muscle convergence are “read” by
the brain
• Visual: Binocular vs. Monocular
– Binocular – Stereopsis (retinal disparity)
– Monocular (next screen)
» Static
» Motion parallax
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
18
More Depth Cues
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• Monocular
– Static
• Interposition
• Size
• Perspective
–
–
–
–
Linear perspective
Texture gradient
Aerial perspective
Shading
– Motion parallax
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Monocular Cues -- Interposition
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Monocular Cues -- Size
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Monocular Cues – Linear
Perspective
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Monocular Cues – Texture
Gradient
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Sooooo . . .
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The grass really
IS
greener on the other side of the fence!!!
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Monocular Cues – Aerial
Perspective
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Monocular Cues -- Shading
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Monocular Cues – Motion
Parallax
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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More visual perception
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• Illusions – and what they tell us about
vision
• Ponzo illusion
• Muller-Lyer illusion
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Ponzo Illusion
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Muller-Lyer Illusion
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Psycholinguistics
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• The psychology of language.
• What goes on from the time I get an idea
until you have the same idea,
– Whether I speak my idea (speech
production, auditory science, speech
perception)
– Or write my idea (motor movements, visual
system, reading)
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
33
The Psychology of Reading
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• Except for fairly rare cases of “phonetic
symbolism” (onomatopoeia) words have
no inherent meaning.
– (And rarer cases of “orthographic
symbolism”!!)
• So, READING is the interpreting of
words, the acts that go on to impose
meaning, from within, on external visual
stimuli.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Some facts about reading
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• Eyes of the mature reader move rhythmically across the page
(from left to right).
• Eye movement consists of fixations, saccades, regressions, and
return sweeps.
• No information is taken in during saccades (10-25 msec),
regressions (same duration), or return sweeps (40 msec).
• During fixation (250 msec) a visual pattern is reflected onto the
retina.
• Span of perception = amount of print seen during a single fixation.
• Span of perception = 12 letter spaces for good readers, 6 for poor
readers.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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More facts
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• Span of recognition – 1.21 words for senior high, 1.33 words for
college readers.
• So, 7 to 8 fixations per line of print.
• As content gets tougher, duration of fixations, not number,
changes (increases).
• Regressive movements aren’t systematic. Used when attention
is faltering.
• College readers have 1 regressive movement per 3 or 4 lines of
print. Immature readers have 3 or 4 regressions per line.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
36
Iconic Memory
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• Remember in Week 1 I mentioned a two-stage memory process –
STM and LTM.
• A third stage, Iconic Memory: The unidentified, “pre-categorical”
pattern of lines, curves and angles; formed in about 100 msec.
• Icon can hold up to 20 letter spaces.
• Pattern recognition routines are applied to the lines, curves.
• It takes about 10 – 20 msec to read each letter out of the iconic
memory.
• Neural signal takes about 30 msec to go from the retina to the
visual cortex.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
37
Iconic Memory (cont’d.)
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• At some point, thanks to pattern recognition routines, letters are
read out.
• Letters are transformed into abstract phonemic representations.
• The abstract phonemes are used to search the mental lexicon.
• About 300 msec after the eye has fallen upon the page, the first
word is “understood,” i.e., placed in Primary Memory (STM,
Working Memory).
• Syntactic and semantic rules are applied to gain the meaning of
the sentence.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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How do you know, Randolph?
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• Psycholinguists employ a variety of methods to
acquire this data about human behavior.
• One question: Why do we think readers routinely
transform the visual representation into a phonological
representation?
– Cognitive economy – all (healthy) new readers
come to the task as skilled hearers.
– “I thought you said something about data?”
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
39
Rubenstein et al. (1971)
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• Used a lexical decision task (word/nonword?).
• Two types of nonwords – homophonous (with real
words), like burd and nonhomophonous like rolt.
Equally “wordlike.”
• Longer latencies for burd.
• Similarly, longer for real homophones like meat.
• Pointed to “false matches” in the mental lexicon.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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More Data
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• McCusker et al. (1977) proofreading experiment
– Homophonous typos (e.g., furst) went undetected
more often than nonhomophonous typos (e.g.,
farst).
• Gough and Cosky (1977) used the Stroop task.
– Nonwords homophonous with color words (e.g,. bloo) led to
more interference than control words (e.g., blot) or nonwords
nonhomophonous with color words (e.g., blop).
• I found readers took longer to process words with
irregular “spelling-to-sound rules” (e.g., pint) than
words with regular rules (e.g., hint) (Bias, 1978).
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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The Point
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• The reasons for this somewhat esoteric
discourse on the psychology of reading
are:
– To communicate the complexity that is
human information processing
– The illustrate the ways scientists go about
answering questions about info processing
– To sensitize you to the sorts of things
known about human behavior
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
42
In week 1, we were talking about
Perception and Cognition
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•What do we know about humans?
– In the physical realm: Anthropometry.
– These days we’re more interested in the
cognitive realm.
– Question: Can you remember a 30-digit
number?
– I say that you can, right now, without practice,
seeing it only once, for 1 second, with no time to
rehearse.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
43
i
3333333333333333333333333333333
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Experiment 1
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Instead of numbers, I’ll present CVC
(consonant-vowel-consonant) strings -- like
“NEH”.
10 CVCs, one at a time.
Presented visually.
Don’t have to remember them in order.
Pencils down.
Ready?
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
45
i
BOV
NAZ
TOL
RIJ
DIH
REN
WUK
CAQ
GOC
MEB
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
46
i
BOV
NAZ
TOL
RIJ
DIH
REN
WUK
CAQ
GOC
MEB
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
47
Experiment 2
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•Now, 10 new CVCs.
•Same task -- recall them.
•This time, after we read the 10th item,
we’ll all count backwards from 100 by 3s,
aloud, together.
•Then when I say “Go,” write down as
many of the 10 CVCs as you can.
•Pencils down.
•Ready?
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
48
i
VAM
LUN
XOP
REH
WIV
CIT
JEG
KUC
ZOB
YAD
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
49
i
VAM
LUN
XOP
REH
WIV
CIT
JEG
KUC
ZOB
YAD
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
50
Experiment 3
•
•
•
•
•
•
i
Same as Experiment 2.
Yet 10 more CVCs.
Backwards counting.
Don’t have to recall them in order.
Pencils down.
Ready?
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
51
i
GEP
TIV
WOH
LUP
MAZ
SEX
KOL
RUC
NID
BIR
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
52
i
GEP
TIV
WOH
LUP
MAZ
SEX
KOL
RUC
NID
BIR
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
53
So?
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• So, the answer to “Can you remember a 30digit number?”, is . . . It depends. On what?
– Whether you hear or see the number.
– Whether the number is masked.
– Whether you have time to rehearse.
– Whether you can “chunk” the numbers.
– If there are any intervening tasks.
– How meaningful the number is.
– WHAT the number is.
So, what’s a usable interface?
It depends.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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SO WHAT?
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• Given that we’re so all-fired complex,
what does this have to say about how we
design computer interfaces?
– Depth cues.
– Color perception.
– Effects of context on perception.
– What’s easy to read?
– Recognition vs. recall.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
55
People Differ
• Some of your
users/visitors may be:
– Non-native English
speakers
– Left handed
– Capricorns
– Republicans
– Heterosexuals
– Poor visual
processors
– In a hurry
– Alabamans
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–
–
–
–
–
–
–
In a public library
Blind
Mostly blind
Color blind
Geniuses
Drunk
Visitors to your earlier
site
– First-time web visitors!
– On a subway
– Using an iPhone
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
56
Situations Differ
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• Cognitive Set
– Context influences perception.
– A series of events can “set” a person to
perceive things a certain way.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
57
i
Guess what frequent error was made on
this form, on an IBM Service site:
•
•
•
•
•
Name:
___________________
Street Address: ___________________
City:
___________________
State/ZIP:
___________________
County:
___________________
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
58
Design of Everyday Things
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• It ain’t rocket science.
• You’ve already read the book.
• Let me hit just the high points from my point
of view
• While I’m presenting this, see if you can
characterize your good and bad designs
that you’ve discovered this week in
Norman’s terms.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
59
Chapter 1
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• The PsychoPATHOLOGY of everyday
things
• Assumption: We blame ourselves for
errors, but the real culprit is faulty
design.
• Assumption: There’s nothing special
about computers. They have the same
sorts of design problems as simpler,
everyday things.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
60
Good Design
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• Well designed objects . . .
– are easy for the mind to understand
– contain visible cues to their operation
• Poorly designed objects . . .
– provide no clues, or
– provide false clues.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
61
Natural Signals
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• Natural signals lead to natural design.
• A metal plate “naturally” is to be pushed.
• Visible hinges “naturally” indicate
attachment, and that the other side
swings open. (And swings open
TOWARD me?)
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
62
Mapping
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• Mapping is a relationship between two
things (e.g., between what you want to
do and what appears possible).
• Good design allows for a clear (visible)
mapping between . . .
– intended actions and
– actual operations.
• Now -- think of what this might mean in a
web site.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
63
Good Design
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• Principles of good design
– the importance of visibility
– appropriate clues
– feedback of ones actions.
• Just so you’ll know -- others have proposed
OTHER principles of good design. Go
check out the web site of Bruce Tognazzini:
http://www.asktog.com/basics/firstPrinciples.html
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
64
Affordance
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• Affordance is the perceived and actual properties of a
thing.
– Primarily those fundamental properties that
determine how a thing could possibly be used.
– “Affords” means, basically, “is for.”
– A chair affords support, therefore affords sitting.
• Affordances provide strong clues to things’ operations.
• When affordances are taken advantage of, the user
knows what to do just by looking.
– No label, picture, or instruction (“Push”) is required.
• - When simple things need pictures, labels, or
instructions,
the
design
has
failed.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
65
The Paradox of Technology
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– Added functionality generally comes along
at the price of added complexity.
– The same technology that simplifies life by
providing more functions also complicates
life by making the device harder to learn
and use.
– The Paradox of Technology should never
be used as an excuse for poor design.
– Added complexity cannot be avoided when
functions are added, but with clever design
they can be minimized.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
66
Chapter 2 -- Psy of Everyday
Actions
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• Norman’s credo on errors -- if an error is
possible, someone will make it.
• The designer must design so as to:
– minimize the chance of errors in the first
place
– minimize the effects of an error
– make errors easy to detect
– make errors reversible, if possible.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
67
Models
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• Mental Models = our conceptual models of the way . . .
– objects work
– events take place
– people behave
• Mental models result from our tendency to form
explanation of things.
• Models are essential in helping us . . .
– understand our experiences
– predict the outcomes of our actions
– handle unexpected occurrences.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
68
Models (cont’d.)
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• We base our models on whatever knowledge we have:
– real or imaginary
– naïve or sophisticated
– even fragmentary evidence.
• Everyone forms theories (mental models) to explain
what they have observed.
• In the absence of feedback to the contrary, people are
free to let their imaginations run free.
• More on models in Chapter 3.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
69
7 Stages of Action
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•
On p. 47 is a series of four figures that
illustrate Norman’s view of the structure
of action.
• Actions have two major aspects:
1. Doing something (execution)
2. Checking (evaluation)
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
70
Chapter 3 - Knowledge in the
Head and in the World
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• Not all knowledge required for precise
behavior must be in the head. It can be
distributed:
– partly in the head
– partly in the world
– partly in the constraints of the world.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
71
Constraints
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• The power of constraints -- the “memory” for epic poetry
is found to be mostly reconstruction, with the aid of the
constraints of rhyme, meter, etc.
• We use constraints to simplify what we must remember.
• For example, putting mechanical parts together.
– Some are constrained by what will and will not fit
together.
– Also cultural constraints -- screws tighten clockwise.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
72
Memory
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• . . . is knowledge in the head.
• Think of all you can remember. Phone
numbers, postal codes, passwords,
SSN, birthdays, etc., etc.
• It’s tough!
– So, we put memory in the world.
(Daytimers. Smart phones. Address books.
Stickies.)
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Tradeoff . . .
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• . . . between info in the world and in the
head.
– Knowledge in the world acts as its own
reminder.
– Knowledge in the head is efficient. (You can
travel light.)
– Knowledge in the world is easier (no learn
time), but often difficult to use. Relies heavily
on the physical presence of info.
• See Fig. 3.6, p. 79.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
74
Ch. 4 -- Knowing what to do
i
• When we encounter a novel object,
either
– We’ve dealt with something similar before,
and we transfer old knowledge, or
– We get instruction.
• Thus, information in the head.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
75
Design
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• How can the design of an object (NOTE:
info in the world) signal the appropriate
actions?
– Natural (physical) constraints
– Affordances, that convey messages about the
item’s possible uses, actions, and functions
• “The thoughtful uses of affordances and
constraints together in design lets a user
determine readily the proper course of
action even in a novel situation.”
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
76
Ch. 5 - To err is human
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• Errors come in several forms
– Slips -- result from automatic behavior,
when subconscious actions get waylaid en
route (“performance errors”)
– Mistakes -- result from conscious
deliberations (“competence errors”)
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
77
Ch. 6 -- The Design Challenge
i
• Norman talks about what forces work
against evolutionary, or natural design
(pp. 142-143).
– The demands of time (quick product cycles)
– The pressure to be distinctive (related to the
curse of individuality)
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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Pitfalls
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•
Three reasons why designers go
astray:
1. Putting aesthetics first
2. Designers aren’t typical users
3. Designers’ clients may not be the users
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
79
Ch. 7 - UCD
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• Chapter 7 is the “punch line” of the whole
book.
• User-Centered Design
• Most of the chapter is given over to
describing “seven principles for
transforming difficult tasks into simple
ones.”
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
80
Etc.
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• He goes on to offer a section on why you
might want to design something to be
hard to use ON PURPOSE.
• And he ends with a few sections on
writing, the home of the future, and a
concluding section.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
81
Now . . .
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• Let’s try to put it in Norman’s terms why
the good designs were good and the bad
designs were bad. (“Some important
feature was, or was not, visible.”)
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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. . . your homework
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• Bad designs.
• Good designs.
Famous quote: “No one ever raised a statue to
a critic.” Sibelius
I want us all to remember that it is easier to
criticize another design than it is to design
something.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
83
For next week
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• More reading -- Carroll (1997), Olson and
Olson (2003).
• For next week, come with one good and one
bad example of web usability. Actually, send
‘em to the TA by Friday noon.
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
84
i
• In fact, by Friday noon, please send to the TA:
– Two URLs (one good and one bad design)
– The name of the book you’re reviewing. (Book reviews due 2
weeks from today – and be prepared to give a 4 or 5 minute
summary, with or without ppt.)
– Your white paper topics. Due 3 weeks from today.
• Usability test plan (for your final project) due in 5
weeks. Will help with pointers to templates.
– (Randolph – hand out some project suggestions.)
R. G. Bias | School of Information | UTA 5.424 | Phone: 512 471 7046 | rbias@ischool.utexas.edu
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