Download Cognition - IT, Sligo

Human Abilities:
Cognition
James Landay
John Kelleher
Plain English campaign!
2
Outline




Human visual system
Guidelines for design
Models of human performance (MHP)
Memory
3
Models of the User

Model Human
Processor

Our Model
Cognitive System
Perceptual
System
Motor
System
Memory
I/O
CPU
4
Human I/O Channels

Input via the senses





Sight
Hearing
Touch
Taste
Smell

Output via motor
control





Limbs (feet?)
Fingers
Eyes
Head
Voice
5
Why Model Human Performance?


To test understanding
To predict influence of new technology
6
The Model Human Processor

Developed by Card, Moran, & Newell (’83)

based on empirical data
Long-term Memory
Working Memory
sensory
buffers
Visual Image
Store
Eyes
Ears
Perceptual
Processor
Auditory Image
Store
Motor
Processor
Fingers, etc.
Cognitive
Processor
7
Model
Human
Processor
(Card, Moran & Newell)
8
Model Human Processor
Components




Cycle times
Decay Rates
Storage Capacities
Coding/Representation Schemes
9
Perceptual (Sensory)
Memories





Area of memory that deals with information from
the senses
Perceptual memories are highly volatile
information stores
Information flows from perceptual (sensory)
memories into Working Memory
Perceptual memories decay almost immediately
and are replaced by new, incoming information
Selection of stimuli governed by level of arousal
10
What is This?
11
Parameters of Perceptual
Memories

Visual (Iconic) Memory




Coding Scheme – Physical analogs
Capacity – ~17 letters
Decay Rate – ~200ms
Auditory (Echoic) Memory




Coding Scheme – Physical analogs
Capacity – ~5 letters
Decay Rate – ~1500 ms
Buffers for stimuli
12
Perceptual Processor
The speed of the perceptual processor is about
~100ms per cycle





Light blinks appearing within 100ms look like a single
brighter light

e.g. frames of a film seen as continuous fluid scene
reflects some processing by sensory processor.
Light blinks in two locations within 100ms look like
motion of a single light
Auditory clicks occurring within 100ms sound like
one louder tone

Also, sensory memory for hearing more durable than
others
Multiple taps occurring within 100ms feel like one tap
of greater pressure
13
Working Memory
(Short-term Memory)





Working Memory is a temporary information store.
Working Memory receives information from Perceptual
Memories and LTM
Working Memory can influence LTM
Information in Working Memory is often recoded

e.g. Visual information is rehearsed as auditory

Chunking is already happening
People have some control over Working Memory

Rehearsal

Attention
Sensory
Attention
memory
Working
Rehearsal
memory
Long-term
memory
14
Memory

Working memory (short term)

small capacity (7 ± 2 “chunks”)
6174591765 vs. (617) 459-1765
 AIBIBMEMC vs. AIB IBM EMC
Chunking
 Grouping together information into sections that make
sense to the individual and seen as entities by that
individual



E.g. master chess players (but only for legal positions)

rapid access (~ 70ms) & decay (~200 ms)

pass to LTM after a few seconds
15
Chunking
1 chunk
Recoded to image
3 chunks
Recoded to words
BIG OLD MAN
B
I
G O
L
xxx
xxx
xxx
D
M
A
N
9 chunks
Recoded to letters
Perceptual
memory
16
Primacy and Recency Effects
List 1
List 2
List 3
Barrier
Babies
File
Firearms
Sofa
Heart
Scarf
Lobby
Scarecrow
Newspaper
Clock
Stylus
Sea-shell
Polish
Maggot
Tomato
Lintels
Rug
Apologies
Dog
Flea
Table
Dolls-house
Ball-pen
Plant
Oasis
Jamboree
Chemist
Festival
Neptune
Identity
Gnat
Magnum
Percolator
Curtains
Paper-clip
Saucer
Income
Typist
Tiles
Precinct
Subway
Directory
Argument
Accident
17
Primacy & Recency Effect

Free recall


Primacy effect


Recalling lists in any order
Tendency for first words on list to be commonly
recalled
Recency effect

Tendency for last words on list to be commonly
recalled
18
Fun with Working Memory
25439762608
456 295 1413
HEC ATR ANU PTH ETR EET
19
Interference affects recency
List 1
List 2
Then do…
Aubergine
Rescue
1
Chickenpox
Gravestone
+6
Elephant
Flower
-4
Telephone
Fountain
+9
Pendant
Statue
-2
Egg
Fool
+8
Melancholy
Aphid
-9
Cheese
Surprise
-6
Mug
Printer
-2
Nymph
Cenotaph
Dinghy
Dog basket
Tray
Magnet
Mole
Lawn
Tram
Pram
Macabre
Sandwich
20
Memory


Extent to which new material can be remembered depends on
its meaningfulness
Levels (Depth) of processing theory (Craik & Lockhart, 1972)

Info processed at different levels





e.g., processing physical features of a word such as its sound
Deep, semantical analysis
Depth of processing determines how well remembered
Elaborative (effortful process) vs. maintenance rehearsal
Closure

Feeling of ‘relief’ when task successfully completed


E.g. successful logon
Important to permit processes to be chunked in memory

E.g. avoid traversing between windows within an application
21
Depth of Processing (shallow)









flame
patch
sonic
bless
avarice
pears
spade
bliss
forth









peels
speed
avoid
freak
pints
rare
blush
slow
pluck
22
Depth of Processing (deep)









spoon
shares
glass
ports
spray
boots
goose
prize
steam









runner
grass
pint
stink
bride
green
queen
story
brown
23
Memory

Factors that determine meaningfulness

Familiarity of an item, and the frequency with
which a word occurs in everyday language



It’s associated imagery, the ability with which the
word can elicit images in one’s mind



Familiar: Door, read, stop
Unfamiliar: compile, substitute, scan
Ride, sleep, eat
Begin, increase, evaluate
Information best recalled if in situ

E.g. Diver education (Baddeley)
24
Improving memory

Method of Loci


Peg-word method


Associate items with rhyming words & numbers
Creating a narrative


Visualising familiar route & associated items at
particular locations long route
Create story or song linking concepts together
Creating acronyms

e.g. A.B.C. of First Aid
25
Implications for UI design

Items that need to be remembered at the interface
should be as meaningful as possible





Problems with command line interfaces
e.g., command names and icons should be selected
according to meaningfulness
 cp vs. copy
Words that represent visible objects easiest to recall
Memory best facilitated by relaxed user
Ask only relevant material


Or provide user with reason for action
Asking for non-sensible information
26
Peg Word Memory Aid










1
2
3
4
5
6
7
8
9
10
bun
shoe
tree
door
hive
sticks
heaven
gate
wine
hen
27
28
Memory Factors
Total time hypothesis
 Distribution of Practice Effect
 Listen

The engines roared above the noise of the crowd. Even in the
blistering heat people rose to their feet and waved their hands in
excitement. The flag fell and they were off. Within seconds the
car had pulled away from the pack and was careering round the
bend at a desperate pace. Its wheels momentarily left the ground
as it cornered. Coming down the straight the sun glinted on its
shimmering paint. The driver gripped the wheel with fierce
concentration. Sweat lay in fine drops on its brow.

People prone to embellishment and ‘localisation’ of facts.
29
Long-Term Memory (LTM)
Long-term memory stores everything that we “know” -- facts,
experience, knowledge, procedural rules of behavior.
LTM has huge capacity.
LTM has a relatively slow access compared to short-term
memory.




‘activation’ is process of recall to WM

Only encode the important information

Forgetting also occurs slowly.
Causes for forgetting

1) Never stored; encoding failed
2) Gone from storage; storage failed (??)
3) Can’t get out of storage; retrieval failed

Interference model of forgetting



one item inhibits the retrieval of another
proactive interference (3)
retroactive interference (3 & 2)
30
Pennies Example
31
Long-term Memory

Long-term memory works by semantics
and by association.
breathes
ANIMAL
moves
Is a
barks
eats
Four legs
has
DOG
Is a
HOUND
has
tail
Is a
SHEEPDOG
32
Parameters of LTM
Semantic network (encoded in terms of meaning
and relationships).



Related associations, images, and past experiences
How knowledge is encoded makes a difference in how
knowledge is recalled
Recognition is much easier than recall


(DOS prompt vs. Mac user interface)
Coding Scheme – Semantic
Capacity – Unlimited
Decay Rate – None




However, recall from LTM is affected by encoding
specificity and retrieval cues
33
Encoding Specificity


“Specific encoding operations performed on
what is perceived determine what is stored,
and what is stored determines what
retrieval cues are effective in providing
access to what is stored.”
-- Card, Moran, & Newell (1983)
This is a fancy way of saying that the
encoding context matters
34
Discrimination Principle

“The difficulty of memory retrieval is
determined by the candidates that exist in
the memory relative to the retrieval cues.”
-- Card, Moran, & Newell (1983)
35
Cognitive Processor



“The recognize-act cycle…is the basic
quantum of cognitive processing.” (CMN,
1983)
In each cycle, the contents of Working
Memory activate something in LTM which in
turn modifies the contents of Working
Memory
Cycle time is ~70ms
36
Motor Processor


Draw parallel lines (approx. 4cm apart)
For a duration of 5 seconds…


Draw a zig-zag line back and forth between the
lines working left to right
The basic motor cycle time is ~70ms



Move pen back and forth between two lines
~71 reversals in 5 sec, or ~70ms/reversal
forgetting anything?
37
Putting It All Together

True reaction time



1 perceptual cycle + 1 cognitive cycle + 1 motor
cycle
100ms+70ms+70ms = 240ms
Some studies include additional cognitive step

Raises total by 70ms to 340ms
38
Principles of Operation (cont.)

Fitts’ Law

moving hand is a series of microcorrections


time Tpos to move the hand to target size S which is
distance D away is given by:


correction takes Tp + Tc + Tm = 240 msec
Tpos = a + b log2 (D/S + 1)
summary

time to move the hand depends only on the relative
precision required
39
Fitts’ Law Example
Pop-up Linear Menu
Pop-up Pie Menu
Today
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday

Which will be faster on average?

pie menu (bigger targets & less distance)
40
Perception

Stimuli that occur within one PP cycle fuse
into a single concept

frame rate needed for movies to look real?


time for 1 frame < Tp (100 msec) -> 10 frame/sec.
Perceptual causality


two distinct stimuli can fuse if the first event
appears to cause the other
events must occur in the same cycle
41
Perceptual Causality

How soon must red ball move after cue ball collides
with it?

must move in < Tp (100 msec)
42
What is missing from MHP?

Haptic memory


Moving from sensory memory to WM


for touch
attention filters stimuli & passes to WM
Moving from WM to LTM

elaboration
43
Cognitive Processes

Controlled



limited capacity; require attention and conscious control
easier to change
Automatic

Activities we carry out that have become automated
 Reading, writing, speaking in native language… (others?)
 We don’t have to attend to (think about) what we are doing.
44
Automatic Processing


The more we practice, the more our performance
improves to the point that we become skilled, and
performance is automatic
Characteristics





fast,
demanding minimal attention, therefore
doesn’t interfere with other activities
unavailable to consciousness
hard to change once learned
45
Effect on UI design decisions

Interactions that have become automatic are
difficult to unlearn


Microsoft’s approach to WordPerfect domination
Consistency across versions, tools can help
avoid this problem

Microsoft Office critical mass of usage stiffles
StarOffice
46
Stroop Effect



Example of automatic behaviour
Volunteer
Start saying colors you see in list of words




when slide comes up
as fast as you can
Say “done” when finished
Everyone else time it…
47
Say the colour of these words
Paper
Home
Back
Schedule
Page
Change
48
Simple Experiment


Do it again
Say “done” when finished
49
Now do it again…
Blue
Red
Black
White
Green
Yellow
50
Importance of Context


Bottom-up perception uses features of stimulus
Top-down perception uses context (and prior
knowledge)



Temporal (for hearing) – what we heard before or after
stimulus
Spatial (for visual) – what’s around the stimulus (as below)
draws on long-term memory
51
Gestalt Laws of Grouping



German Psychologists
Primary purpose of visual system is
recognition of objects from basic visual
elements
Objects seen as more than a sum of the
parts


When elements are arranged in groups
that define an object, we tend to see the
object and not the elements.
e.g. ascii art
52
Gestalt Principles
Similarity
Proximity
Similarity
Closure
Continuity
Symmetry
53
Law of Proximity

Things that are relatively close to one another
tend to be grouped together.
54
Laws of Similarity

Items that look similar will be seen as parts of
the same form
55
Law of good continuation

Objects arranged in either a straight line or a
smooth curve tend to be seen as a unit.
56
Law of Closure

Innate tendency to perceive incomplete
objects as complete and to close or fill gaps
and to perceive asymmetric stimuli as
symmetric
57
Law of common fate
The law of common fate leads us to group together
objects that move in the same direction. In the
following illustration, imagine that three of the
balls are moving in one direction, and two of the
balls are moving in the opposite direction. If you
saw these in actual motion, you would mentally
group the balls that moved in the
same direction. Because
of this principle, we often
see flocks of birds or
schools of fish as one
unit.
58
Attention
“Everyone knows what attention is. It is the
taking possession of mind, in clear and vivid
form, of one out of what seem several
simultaneously possible objects or trains of
thought … It requires withdrawal from some
things in order to deal effectively with others.”
W. James, 1890
59
“cocktail party phenomenon”

Ability to focus on one activity, while tuning
out others

can be distracted from one task if attention called
to another
60
Attention

Can design interfaces to help users find information
they need



Can structure interface so it is easy to navigate
Not too much information, nor too little
Rather than arbitrarily presenting information:
 use groupings
 order in meaningful way
 See Gestalt laws of perceptual grouping
61
Attention

Manner in which we deploy our attention has
a tremendous bearing on how effectively we
can interact with a system


Focused attention
 Ability to attend to one event from what amounts
to a mass of competing stimuli in the environment
Divided attention
 Ability to attend to more than one stimuli at a time


Cocktail party phenomenon
Voluntary or involuntary
62
Implications for Design

Users are prone to distraction

On returning to suspended activity:
 May forget where they left off
 May forget whether they completed the task or not


Forgetting to put salt French fries, or doing it twice
Answer: cognitive aids
 Reminders or external representations intended to gain
attention at a time relevant to the task that needs to be
performed.
 E.g., status area indicating task status, coffee cup on flaps
63
Attention

Other techniques for presenting information
to guide attention

Spatial and temporal cues



Example
Color
Alerting techniques


Flashing and reverse video
Audio warnings
64
Attention

Windows are a useful way to partition the
screen into discrete or overlapping sections

enable different types of information to be
separated, provides meaningful groupings

e.g., word processor



Text area
Footnote area
Command area
65
Implications for Design

Info which needs immediate attention should always
be displayed in a prominent place (error and warning
messages)
66
Implications for Design

Less urgent info should be allocated to a less
prominent but specific areas of the screen so that the
user will know where to look when this information is
required.
67
Implications for Design

Information that is not needed very often should not
be displayed but should be made available on
request.
68
Goals of Representation
Aiding




Turn a cognitive task into a perceptual task.
Offload human working memory onto an
external representation.
Map relevant constraints in the domain onto
relevant representational properties.
Encourage people to develop a “correct”
mental model
69
Implications for Design





You have to understand the person’s task!!
You have to understand the domain!
People have cognitive constraints and
abilities
The domain imposes constraints
Map those together into a design that
represents domain constraints in a way that
people can best perceive/understand.
70
Ventilator Management Example


Practitioners: Intensive Care Unit specialists
Task: To evaluate whether a patient is
recovering his/her own breathing over time

Ventilator vs. Patient: Rate of breathing, depth of
breathing
71
Today’s displays



Typical process control displays with tables and
tables of “Label:Value” parameters
Every variable that’s measured by the patient is
displayed on the screen, (single sensor single
indicator) e.g.:
Ventilator
Patient



Rate of breathing: 10
Depth of breathing: 5
Rate of breathing: 2
Depth of breathing: 6
Difficult to get “status at a glance”, to judge whether
patient is improving or not.
72
Novel Display
(Volume Rectangles)
Ventilator Patient
rate
volume
(Cole and Stewart, 1994)
73
A series of volume rectangles
Ventilator Patient at time t1
Ventilator Patient
at time t21
The patient is clearly doing more of
his/her own breathing over time.
74
Evaluation of Novel Display


Physicians had perfect performance in
judging whether patients were getting better
or worse over time.
Furthermore, the physicians’ judgments were
significantly faster using the novel volume
rectangle display than using the familiar table
of numbers currently used in practice.
75
Recognition over Recall

Recall



info reproduced from memory
e.g., command name & semantics
Recognition



presentation of info provides knowledge that info has been
seen before
e.g., command in menu reminds you of semantics
easier because of cues to retrieval
 cue can be anything related to item or situation where it
was learned
 example: giving hints
 other examples in software?

icons, labels, menu names, etc.
76
Knowledge in the Head and in the
World



Knowledge in the world
 is the information in the environment
Knowledge in the head
 is the information that is stored in
memory
Most of the time we need to combine the
two types knowledge to operate things.
77
Knowledge in the World/Head
Property
Knowledge in the World
Knowledge in the Head
Retrievability
Retrievable whenever
visible or audible
Not readily retrievable.
Requires memory search or
reminding.
Learning
Interpretation substitutes for
learning. Ease of
interpretation depends upon
exploitation of natural
mappings and constraints.
Efficiency of use May be slowed up by need
to process & interpret
information.
Requires learning. Made
easier if a good structure is
imposed.
Ease of use at
1st encounter
High
Low
Aesthetics
Can lead to clutter, more
dependant on skill of
designer
Nothing need be visible
giving designer more
freedom.
Can be very efficient
78
Because ...

Not all of the knowledge required for
precise behavior has to be in the
head




partly in the head
partly in the world
partly in the constraints
E.g. clipboard and ‘spike’
79
Also because ...

We can recognize material far
more easily than we can recall
it.

Knowledge in the world lets
people recognize facts or things.


E.g. road signs
Knowledge in the head requires
recall.
80
AIB 24-Hour Online Banking

User logs on to:




Check balances
Move funds
Cancel cheques
AIB offers TransactOnline to:

Provide one-time credit card numbers tied to
user’s account
81
Opening Logon Screen
82
Further Validation Screen
83
TransactOnline SignOn
84
85
86
87
88
Further Reading
Vision and Cognition

Books




The Psychology Of Human-Computer Interaction, by Card,
Moran, & Newell, Erlbaum, 1983
Human-Computer Interaction, by Dix, Finlay, Abowd, and
Beale, 1998.
Perception, Irvin Rock, 1995.
Articles

“Using Color Effectively (or Peacocks Can't Fly)” by
Lawrence J. Najjar, IBM TR52.0018, January, 1990,
http://mime1.marc.gatech.edu/mime/papers/colorTR.html
89
Extra Slides
90
Vision (1/3)


Two aspects: physical receptor & subsequent perception processing
Photoreceptors:



Ganglion cells


Rods (120 m., light sensitive, can be saturated, concentrated on edges of
retina, poor visual acuity).
Cones (6 m., less light sensitive, colour perceptors, concentrated on
fovea, blind spot).
specialised nerve cells
X-cells (fovea centred, pattern detection)
Y-cells (distributed on retina, movement detection)
Size & Depth Perception



Visual angle (larger angle at same distance implies larger object)
Visual acuity (fine detail perception)
Law of size constancy

relies on cues - overlapping objects, size and height of object,
familiarity with object.
91
Vision (2/3)

Brightness




Subjective quantity; affected by luminance; contrast
visual system adjusts to perceive in differing lighting; rods/cones
visual acuity increases with luminance as does ‘flicker’
Colour


3 components (hue, intensity, saturation)
Hue determined by wavelength






Blues short, greens medium and reds long wavelength.
Intensity is brightness of colour
Saturation is amount of whiteness in the colour (‘washed-out’ affect)
3 types of cones sensitive to RGB (fewest cones for blue)
colour blindness
Visual Processing


Movement of retina & changes in luminance are perceived as constant
Ability to interpret and anticipate images is vital - easily fooled, however.

Muller-Lyer illusion, Ponzo illusion.
92
Touch




Secondary source of information
Crucial to people with disabilities
Touch is not localised
3 Types of sensory receptor



Thermoreceptors - heat and cold
Nociceptors - intense pressure, heat and pain
Mechanoreceptors - pressure




rapidly adapting
slowly adapting
two-point threshold test
Kinesthesis


awareness of position of body and limbs
three types



rapidly adapting (moving of limb)
slowly adapting (movement and static position)
positional receptors (static position only)
93
Engineering Models of Human
Performance


Predictive
Quantitative






time to perform
time to learn
number and type of errors
time to recover from errors
Learnable and usable by systems designers
Usefully approximate
94
LTM Processes
•
Remembering or Storing
•
•
•
•
•
Forgetting (2 Theories)
•
•
•
•
•
Repeat rehearsal or exposure to information aids remembering
Ebbinghaus’s Total time hypothesis
Baddeley’s Distribution of practice effect.
Factors boosting memorability: familiarity, concrete images, meaningfulness, structure.
Decay
• Ebbinghaus - information decays logarithmically
• Jost’s Law - Older memories more durable
Interference Losses
• Retroactive interference (newer knowledge inhibits older)
• Proactive inhibition (older knowledge reappears)
Non-emotive words more durable than emotive words (exhibited in nostalgia of ‘good old
days’)
Difficulty in proving forgetfulness. Associations need to be exercised.
Retrieval
•
•
Recall and Recognition
Categorisation, vivid imagery and familiarity aid retrieval.
95
Reasoning
•
Deductive Reasoning
•
•
•
•
•
Inductive Reasoning
•
•
•
•
Derives the logically necessary conclusion from the given premises.
Some people are babies, some babies cry. Some people cry?
Truth and validity clash.
Bring world knowledge into reasoning process to facilitate shortcuts.
Generalising from cases we have seen to infer information about cases we
have not seen
Every elephant we have seen has a trunk; therefore we infer all elephants
have trunks
• Unreliable inference
• Cannot be proved; only disproved by producing a ‘trunkless’ elephant.
Wason’s Cards. Need to disprove statement not add more proof.
Abductive Reasoning
•
•
•
Reasoning from a fact to the action or state that caused it.
Used to derive explanations from the events we observe.
Often unreliable; though we hold such explanations until they can be
disproven.
96
More Slides from…
Washington State University
School of EECS
CptS 443 - Human Computer Interaction
Long Term Memory

The Human World-Wide Web

Two types



episodic - events, organized temporally
semantic - facts, organized associatively
Representations



semantic nets
frames
scripts
98
Semantic Network
university
is a
is a
is a
UI
UW
WSU
vandal
husky
cougar
is a
animal
is a
99
Frames

Extends semantic nets to include structured
hierarchical information
University
WSU
Fixed: type of school
Fixed: type of University
Default: has colleges
Default: public
Variable: public/private
Variable: campus
100
Scripts

Stereotypical information






Entry conditions: need job, have money
Result: educated, less money
Props: books, schedule, new car
Roles: instructor talks, students listen
Scenes: classroom, dorm
Tracks: internships, apprenticeships
101
Processes

How does information get from short term
memory into long term memory?



Total time hypothesis - hit the books
Distribution of practice effect - don’t cram
Meaning - concrete better than abstract



faith age cold tenet quiet logic idea value past
boat tree cat child rug plate gun flame head
Structure, familiarity and concreteness
102
How We Forget

Decay



Logarithmically - forget most early
Jost’s Law - if two equally strong memories at a given
time, then the older is more durable.
Interference



retroactive interference - old phone number
proactive inhibition - driving to the old house
emotion - good old days, forget the mundane
103
Information Retrieval

How do we recall details?


Categorization
Visualization
1
2
3
4
5
bun
shoe
tree
door
hive
6 sticks
7 heaven
8 gate
9 wine
10 hen
104
Real Intelligence

How is information processed and
manipulated?


Animals - receive and store info, but do not
process it as well as humans
Computers - receive and store info better then
humans, but do not process it as well as humans
105
Human Intelligence

Humans use information to



Reason & solve problems
Even if the info is partially missing or completely
absent!
Human thought is


conscious & self-aware
capable of imagination
106
Reasoning

Inferring missing information



Deductive - conclusions
Inductive - generalizations
Abductive - suppositions
107
Deductive Reasoning

If A then B



A. Therefore B
not B, therefore not A.
The phone rings when I’m in the shower



If I’m in the shower, then the phone rings
When the phone rings, take a shower
No shower? Phone doesn’t ring.
108
Inductive Reasoning

Specific A has property B then all A is B






Elephants have trunks
Computers are slow
Classes are exciting
Students hand homework in on time
WHETS is fun
Geeks are rich
109
Wason Cards

If a card has a vowel on one side it has an
even number on the other. True or False?
4
E
7 K
110
Abductive Reasoning

From fact to the action that caused it






Totalled car
Black eye
4.0 GPA
Smile/frown
Core dump
Phone ring
111
Problem Solving

Using knowledge to find a solution



Gestalt theory
Problem space theory
Analogy
112
Gestalt Theory

Finding new solutions

Reproductive problem solving




Productive problem solving


Learned behavior, trial and error
Behavioralist
Fixation
Invention, innovation, insight
Pendulum problem
113
Problem Space Theory

Mapping out a solution step by step




Problem states, goal state, current state
Legal state transition operators
Heuristics, e.g. means-ends analysis
Examples



Games: 15 puzzle, chess
Tasks: Setting the VCR clock
Life (emphasis on “legal”)
114
Analogy

Applying one solution to a different problem




Analogical mapping
Purely productive reasoning is hard (10%)
Drawing analogies is easier (80%)
Existing solution “semantically close” to problem
domain
115
Skill Acquisition

Solving problems that are not completely new

e.g. Chess




Same goal (different goal states)
Same transitions
Different “skills”
Problem groups


novices group problems superficially
experts group problems conceptually
116
ACT Skill Acquisition Model

How is skill acquired?
General rules
Proceduralization
Specific rules
Generalization
Tuned rules
117
Errors

How do we make mistakes?


Slips - change in context of skill
Mental models - incorrect interpretation of the
evidence
118
Design

How do we use what we know about humans
to make better user interfaces?



Guidelines
Models
Evaluation
119