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Human Information Processing
Perception, Memory,
Cognition, Response
2
Types of Information
• Quantitative (e.g., 100% charged, 63% charged)• Qualitative (e.g., fully charged, partially charged)• Status (normal, abnormal)• Warning (abnormal -- potentially dangerous)• Representational (e.g., pictures, diagrams)• Identification (e.g., labels)
Stage Model of Information Processing
WorkingMemory
Cognition•situation awareness•decision making•planning•attention•task management
Response•Fitts’ Law•Hicks’ Law
Sensing &Perception•vision•hearing• ...•perception
Long Term MemoryStimuli Responses
Mental Resources
World
4
Stimuli
• Sensible energy
• Examples• visual• auditory• chemical• tactile• acceleration• etc.
5
Information Coding
• use of stimulus attributes to convey meaning
6
Coding Examples:
Shape radio navigation aid
Size city, population 1,000-10,000
city, population 10,000-100,000
Colornormal
non-normal
Pitch high barcode readlow failed to read barcode
Text OFF
7
Characteristics of Coding Systems
• Detectability of codes (thresholds)
• Discriminability of codes (JNDs)
• Meaningfulness of codes
• Standardization of codes
• Code Redundancy
Stage Model of Information Processing
WorkingMemory
Cognition•situation awareness•decision making•planning•attention•task management
Response•Fitts’ Law•Hicks’ Law
Sensing &Perception•vision•hearing• ...•perception
Long Term MemoryStimuli Responses
Mental Resources
World
9
Sensing
• Vision• Hearing• Smell• Touch• Temperature• Pain• Kinesthetic• Equilibrium• Vibration
10
Sensing (continued)
• Sensory Memory• Iconic (visual)• Echoic (auditory)
• Limits• Detection thresholds• Discrimination thresholds• Pain
11
Perception
• Definition• interpretation of sensory stimuli• pattern recognition• preparation for further processing
• Processes• feature analysis (e.g., text, object perception)• top-down processing (use of context, expectancy)
• Examples• Recognizing face of friend• Detecting defect in piece of plywood
12
Perception - Signal Detection
• Stimulus: sensory input(s)• Signal: stimulus having a special pattern• Noise: Obscuring stimuli• Task: Report “yes” when signal present,
otherwise “no”• Example: steam power plant
• task: detect boiler leak• stimulus: sound pressure level (SPL)• signal: higher than normal SPL
13
Stimulus-Response Matrix
False Alarm
P (Y / N)
Hit
P (Y / S+N)
Quiet or
Correct Rejection
P (N / N)
Miss
P (N / S+N)
Noise Signal + Noise
StimulusY
esN
o
Res
pons
e
14
Signal Detection Theory (1)
noise only
X (decibels)
P (
stim
ulu
s in
ten
sity
= x
)
15
Signal Detection Theory (2)
noise only
X (decibels)
P (
stim
ulu
s in
ten
sity
= x
)
signal + noise
d’
16
Signal Detection Theory (3)
noise only
X (decibels)
P (
stim
ulu
s in
ten
sity
= x
)
signal + noise
d’
criterion
NO YES
17
Signal Absent Condition
noise only
X (decibels)
P (
stim
ulu
s in
ten
sity
= x
)
signal + noise
d’
criterion
NO YES
P(quiet)
P(false alarm)
18
Signal Present Condition
noise only
X (decibels)
P (
stim
ulu
s in
ten
sity
= x
)
signal + noise
d’
criterion
NO YES
P(miss)P(hit)
19
Signal Detection: Low d’
• Phenomenon• low d’ leads to poor SD performance
• Example• failure to detect defects in lumber
• Explanation• lack of memory to memorize signal
• Countermeasure• memory aid
20
Signal Detection: Vigilance Decrement
• Phenomenon• prolonged monitoring (signal detection)• P(hit) decreases, P(miss) increases after about 30 min
• Example• manufacturing process goes out of tolerance
• Explanation• sensitivity loss/fatigue/memory loss
• Countermeasures• training• signal transformations• feedback• extraneous stimuli
21
Signal Detection: Absolute Judgment Failures
• Phenomenon• failure to discriminate between > ~ 5 stimuli
• Example• radar operator mis-identifies aircraft
• Explanation• memory limitation
• Countermeasures• training & experience• anchors• memory aids• redundant coding
22
Perception: Left vs. Right Brain
• Phenomenon• dichotomy between
• left half of brain (verbal)• right half of brain (visual)
• Example• historians vs engineers
• Explanation• only slight indication of being influential
Stage Model of Information Processing
WorkingMemory
Cognition•situation awareness•decision making•planning•attention•task management
Response•Fitts’ Law•Hicks’ Law
Sensing &Perception•vision•hearing• ...•perception
Long Term MemoryStimuli Responses
Mental Resources
World
24
Long Term Memory
• Store for all information to be retained
• Contents• General Facts (declarative knowledge)• Procedures (procedural knowledge)• Current model of world (including self)• Current tasks• etc.
• Limits• Unknown• Accessibility vs. Actual content
25
Long Term Memory (cont.)
• Categories• Semantic memory (general knowledge)• Event memory
• episodic memory (what happened)• prospective memory (what to do)
• Mechanisms: associations• frequency of activation• recency of activation
• Forgetting• exponential decay• due to
• weak strength• weak associations• interfering associations
26
Working Memory(Short Term Memory)
• Definition• store for information being actively
processed
• Examples of WM/STM use• telephone number to be dialed
7 3 7 2 3 5 7• observed stimulus and standard stimuli
?
RedBlueGreenYellow
Compare with
27
Working Memory Capacity
• 7 + 2 “chunks”, e.g.,• digits (0, 1, 2, ...)• digit sequences (737-, 752-, 745-, 754-, ...)• names (“Bill”, “Sue”, “Nan”, etc.)• persons (Bill, Sue, Nan, etc.)• etc.
• Miller’s magic number (Miller, 1956).
• Very significant human limitation.
• Enhanced by “chunking”.
28
Working Memory Duration
• max 10 - 15 s without attention/rehearsal.
• Decay rate influenced by number of items.
• Greatest limitation of WM.
• Very significant human limitation.
• Has implications for design.
Stage Model of Information Processing
WorkingMemory
Cognition•situation awareness•decision making•planning•attention•task management
Response•Fitts’ Law•Hicks’ Law
Sensing &Perception•vision•hearing• ...•perception
Long Term MemoryStimuli Responses
Mental Resources
World
Decision Making and Problem Solving
31
Decision Making
• Characteristics of a decision making situation• select one from several choices• some amount of information available• relatively long time frame• uncertainty
32
Classical Decision Theory
• Normative Decision Models• expected value theory
• probability of outcome, given decision• value of outcome, given decision• maximize weighted sum
• subjective utility theory
33
Classical Decision Theory (cont.)
• Humans violate classical assumptions• framing effect (differences in presentation form)• don’t explicitly evaluate all hypotheses• biased by recent experience• etc.
• Descriptive Decision Models• Use of heuristics• “Satisficing”• Simplification
34
Information Processing Framework
• Cue reception and integration
• Hypothesis generation
• Hypothesis evaluation and selection
• Generation and selection of action(s)
35
Factors Affecting Decision Making
• Amount/quality of cue information in WM
• WM capacity limitations
• Available time
• Limits to attentional resources
• Amount and quality of knowledge available
• Ability to retrieve relevant knowledge
36
Heuristics and Biases
• Heuristic• “rule of thumb”
• usually powerful & efficient• history of success• does not guarantee best solution• may lead to bias
• Bias• “irrational” tendency to favor one alternative/class
of alternatives• natural result of heuristic application
• Heuristic implies bias
37
Heuristics in Obtaining and Using Cues
• Attention to limited number of cues
• Cue primacy
• Inattention to later cues
• Cue salience
• Overweighting of unreliable cues(treating all cues as if they were equal)
38
Heuristics in Hypothesis Generation
• Generation of limited number of hypotheses/potential solutions
• Availability heuristic• recency• frequency
• Representativeness heuristic (“typicality”)
• Overconfidence
39
Heuristics in Hypothesis Evaluation and Selection
• Cognitive fixation•underutilize subsequent cues
• Confirmation[al] bias•seek only confirming evidence•don’t seek, ignore disconfirming evidence
• Note:sometimes “confirmation bias” encompasses both
40
Heuristics in Action Selection
• Consideration of small number of actions
• Availability heuristic for actions
• Availability of possible outcomes
41
Naturalistic Decision Making
• Decision making in the “real world”
• Characteristics• ill-structured problems• uncertain, dynamic environments• lots of (changing) information• iterative cognition (not once-through)• multiple (conflicting, changing) goals• high risk• multiple persons• complexity
42
Skill-, Rule-, Knowledge-Based Performance
• Knowledge-based performance• novices or novel/complex problems• knowledge-intensive• analytical processing• high attentional demand• errors: limited WM, biases• e.g., navigating to a new residence
• Rule-based performance• more experienced decision makers• if-then rules• errors: wrong rule
43
Skill-, Rule-, Knowledge-Based Performance (cont.)
• Skill-based performance• experts, experienced decisions makers• automatic, unconscious• requires less attention, but must be managed• errors: misallocation of attention
44
Other Topics in Naturalistic Decision Making
• Cognitive continuum theory• intuition analysis
• Situation Awareness (SA)• perceiving status• comprehending relevant cues• projecting the future
• Recognition-Primed Decision Making• recognized pattern of cues• triggers single course of action• intuitive
45
Improving Human Decision Making
• Redesign• environment• displays• controls
• Training• use heuristics appropriately• overcome biases• improve metacognition• enhance perceptual skills
• Decision Aids• decision tables• decision trees• expert systems• decision support systems
46
Problem Solving
• Problem• goal(s)• givens/conditions• means• initial conditions goal(s)
• Errors and Biases in Problem Solving• inappropriate representations• fixation on previous plans• functional fixedness• limited WM
47
Attention: The Flashlight Metaphor
48
Attention
• Definitions• focus of conscious thought• means by which limited processing
resources are allocated
• Characteristics• limited in direction• limited in scope
49
Attention: Selection
• Phenomenon• inappropriate selection (i.e., inappropriate
attention to something)
• Example• using cell phone while driving
• Explanation• salient cues
• Countermeasures• control salience of cues
50
Attention: Distraction
• Phenomenon• tendency to be distracted
• Example• pilot distracted by flight attendant call
• Explanation• high salience of less important cues• low salience of important cues
• Countermeasures• remove distractions• control salience
51
Attention: Divided Attention
• Phenomenon• inability to divide attention among several
cues/tasks• Example
• using cell phone while driving• Explanation
• limited cognitive resources• Countermeasures
• integrate controls & displays
52
Attention: Sampling
• Phenomenon• stress-induced narrowing of attention
• Example• Everglades L1011 accident
• Explanation• anecdotal
• Countermeasures• sampling reminders
53
Attention: Sampling
• Phenomenon• excessive sampling
• Example• keep looking at clock
• Explanation• memory loss
• Countermeasures• train memory
54
Timesharing
• Definition• process of attending to two or more tasks
“simultaneously”
• Examples• Walk and talk• Drive and talk on cell phone• Fly and restart failed engine
55
Timesharing: Single Resource Theory
• Single pool of mental resources.• cognitive mechanisms, functions, capacity• required to perform tasks
• Task performance depends on amount of resource allocated.
56
Timesharing: Multiple Resource Theory
• Resources differentiated according to• information processing stages
• encoding• central processing• responding
• perceptual modality• auditory• visual
• processing codes• spatial• verbal
• non-competing tasks can be performed in parallel
57
Timesharing: Task Performance
• Phenomenon• performance limitations not due to data limitations
• Example• reading two adjacent lines of text at once
• Explanation• limited resources
• Countermeasures• decompose tasks• eliminate resource contentions
58
Mental Workload
• Definition• “amount” of mental resources required by a set of
concurrent tasks and the mental resources actually available
• Examples• Low: driving on a straight rural road• High: driving in heavy traffic
• on wet, slippery road surface• reading map• dialing cell phone• talking with passenger• worrying about fuel quantity
• Significance• high workload poor task performance
59
Workload Measures
• Analytic• e.g., timeline analysis
• Primary task performance • e.g., driving task
• Secondary task performance• e.g., driving task plus mental arithmetic
• Physiological• e.g., heart rate variability
• Subjective• e.g., NASA TLX
60
NASA TLX Workload Measurement
• Rate the following:• mental demand (low - high)
• required mental activity
• physical demand (low - high)• required physical activity
• temporal demand (low - high)• time pressure
• performance (failure - perfect)• success in accomplishing goals
• effort (low - high)• mental and physical
• frustration level (low - high)
61
Other Cognitive Functions
• Deduction
• Induction
• Situation Awareness
• Planning
• Problem Solving
Stage Model of Information Processing
WorkingMemory
Cognition•situation awareness•decision making•planning•attention•task management
Response•Fitts’ Law•Hicks’ Law
Sensing &Perception•vision•hearing• ...•perception
Long Term MemoryStimuli Responses
Mental Resources
World
63
Response Selection: Reaction Time
Definition:
• time it takes for a human to respond to a stimulus
64
• Simple RT (Donder’s A)
Reaction Time Experiments (1)
1 stimulus
1 response
65
Reaction Time Experiments (2)
• Choice RT (Donder’s B) 1-to-1 match
…. n stimuli
…. n responses
66
Reaction Time Experiments (3)
• Donder’s C
... n stimuli
1 response for 1 stimuli
67
Response: Selection
• Phenomenon• response time proportional to stimulus
uncertainty
• Example• radar operator detecting and identifying
radar contacts
• Explanation• Hick Hyman Law
68
Hick Hyman Law
Response time is proportional to stimulus uncertainty.
OR, equivalently
Response time is proportional to stimulus information content.
69
Information Theory
• Concept• Sender sends message• through channel• to Receiver• The amount of information in the message
is the amount of uncertainty the message reduces in the receiver.
70
Information Measurement (Equiprobable Case)
• FormulaH = log2 N bits
H = number of equiprobable messages
• Note
log2 X 3.32 log10 X
• Examples
• N = 8 H = log2 8 = 3 bits
• N = 13 H = log2 13 = 3.32 log10 13 = 3.7 bits
71
Rationale
Number of binary choices needed to pick right message.
1
2
3
3 bits
1 2 3 4 5 6 7 8
5 6 7 8
5 6
6
72
Non-Equiprobable Case
N
H = - pi log2 pi
i=1
N = number of messages
pi = P(message i is received)
73
Non-Equiprobable Example
• Message probabilities• p1 = 0.25• p2 = 0.25• p3 = 0.45• p4 = 0.05
• Information contentH = -[ 0.25(-2.0) + 0.25(-2.0) + 0.45(-1.15) +
0.05(-4.32)] = 1.73 bits
74
Hick’s Law (Hick-Hyman Law)
• RT = a + b H(s)H(s) = info in stimulus
Assumption: human is perfect channel
H (s) in bits
Reaction Time(ms)
75
Response: Selection
• Phenomenon• simple RT to visual stimuli faster than to auditory
• Example• visual vs. auditory low oil pressure annunciator
• Explanation• visual dominance
• Countermeasures• use visual stimuli when appropriate
76
Response: Selection
• Phenomenon• simple RT inversely proportional to
stimulus intensity• Example
• cockpit master warning• Explanation
• salience• Countermeasures
• control stimulus intensity
77
Response: Selection
• Phenomenon• response time affected by temporal uncertainty
• Example• ATC controller usually (but not always) accepts
handoffs for other controller
• Explanation• possible preprocessing (?)
• Countermeasures• provide pre-stimulus warning, if possible
78
Response: Selection
• Phenomenon• response time inversely proportional to subset
familiarity
• Example• trained radar operator vs untrained radar operator
• Explanation• response automaticity
• Countermeasures• training
79
Response: Selection
• Phenomenon• response time inversely proportional to stimulus
discriminability• Example
• sonar operator distinguishing between two submarine signatures
• Explanation• ambiguous stimuli may require more processing
• Countermeasures• increase discriminability• remove shared, redundant features
80
Response: Selection
• Phenomenon• response time affected by repeated stimuli
• usually faster for several identical stimuli in sequence• increases after “too many” of same stimulus
• Example• computer user confirming multiple file deletions
• Explanation• conspicuity, salience
• Countermeasures• ?
81
Response: Selection
• Phenomenon• response time inversely proportional to stimulus-
response compatibility
• Example• power plant operator acknowledging fault
annunciation
• Explanation• automatic responses require little processing
• Countermeasures• enhance stimulus-response compatibility
82
Response: Selection
• Phenomenon• response time inversely proportional to practice
• Example• trained radar operator faster at detecting and
identifying targets
• Explanation• automaticity of responses
• Countermeasures• provide training
83
Response: Selection
• Phenomenon• response time inversely proportional to required
accuracy• Example
• radar operator detecting and identifying targets• Explanation
• speed-accuracy tradeoff• Countermeasures
• reduce accuracy requirements• enhance operator accuracy through training & other
means
84
Other Factors Affecting RT
• Stimulus complexity
• Workload
• Stimulus location
• Task interference/workload
• Motivation
• Fatigue
• Environmental variables
• etc.