Human Information Processing
Perception, Memory,
Cognition, Response
Types of Information
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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)
2
Stage Model of Information Processing
Mental Resources
Sensing &
Perception
•vision
•hearing
• ...
•perception
Stimuli
Cognition
Working
Memory
•situation
awareness
•decision making
•planning
•attention
•task management
Long Term Memory
World
Response
•Fitts’ Law
•Hicks’ Law
Responses
Stimuli
• Sensible energy
• Examples
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visual
auditory
chemical
tactile
acceleration
etc.
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Information Coding
• use of stimulus attributes to convey
meaning
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Coding Examples:
Shape
Size
Color
Pitch
Text
radio navigation aid
i
n
city, population 1,000-10,000
n
n
normal
high
low
OFF
barcode read
failed to read barcode
city, population 10,000-100,000
non-normal
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Characteristics of Coding Systems
• Detectability of codes (thresholds)
• Discriminability of codes (JNDs)
• Meaningfulness of codes
• Standardization of codes
• Code Redundancy
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Stage Model of Information Processing
Mental Resources
Sensing &
Perception
•vision
•hearing
• ...
•perception
Stimuli
Cognition
Working
Memory
•situation
awareness
•decision making
•planning
•attention
•task management
Long Term Memory
World
Response
•Fitts’ Law
•Hicks’ Law
Responses
Sensing
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Vision
Hearing
Smell
Touch
Temperature
Pain
Kinesthetic
Equilibrium
Vibration
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Sensing (continued)
• Sensory Memory
• Iconic (visual)
• Echoic (auditory)
• Limits
• Detection thresholds
• Discrimination thresholds
• Pain
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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
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Perception - Signal Detection
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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
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Stimulus-Response Matrix
Stimulus
Response
Noise
Signal + Noise
False Alarm
P (Y / N)
Hit
P (Y / S+N)
Quiet or
Correct Rejection
P (N / N)
Miss
P (N / S+N)
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P (stimulus intensity = x)
Signal Detection Theory (1)
noise only
X (decibels)
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P (stimulus intensity = x)
Signal Detection Theory (2)
d’
noise only
signal + noise
X (decibels)
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Signal Detection Theory (3)
P (stimulus intensity = x)
criterion
NO
YES
d’
noise only
signal + noise
X (decibels)
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Signal Absent Condition
P (stimulus intensity = x)
criterion
NO
YES
d’
noise only
signal + noise
P(quiet)
X (decibels)
P(false alarm)
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Signal Present Condition
P (stimulus intensity = x)
criterion
NO
YES
d’
noise only
signal + noise
P(hit)
P(miss)
X (decibels)
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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
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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
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training
signal transformations
feedback
extraneous stimuli
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Signal Detection: Absolute
Judgment Failures
• Phenomenon
• failure to discriminate between > ~ 5 stimuli
• Example
• radar operator mis-identifies aircraft
• Explanation
• memory limitation
• Countermeasures
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training & experience
anchors
memory aids
redundant coding
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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
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Stage Model of Information Processing
Mental Resources
Sensing &
Perception
•vision
•hearing
• ...
•perception
Stimuli
Cognition
Working
Memory
•situation
awareness
•decision making
•planning
•attention
•task management
Long Term Memory
World
Response
•Fitts’ Law
•Hicks’ Law
Responses
Long Term Memory
• Store for all information to be retained
• Contents
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General Facts (declarative knowledge)
Procedures (procedural knowledge)
Current model of world (including self)
Current tasks
etc.
• Limits
• Unknown
• Accessibility vs. Actual content
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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
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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
?
Compare with
Red
Blue
Green
Yellow
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Working Memory Capacity
• 7 + 2 “chunks”, e.g.,
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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”.
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Working Memory Duration
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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.
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Stage Model of Information Processing
Mental Resources
Sensing &
Perception
•vision
•hearing
• ...
•perception
Stimuli
Cognition
Working
Memory
•situation
awareness
•decision making
•planning
•attention
•task management
Long Term Memory
World
Response
•Fitts’ Law
•Hicks’ Law
Responses
Decision Making
and Problem Solving
Decision Making
• Characteristics of a decision making
situation
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select one from several choices
some amount of information available
relatively long time frame
uncertainty
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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
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Classical Decision Theory (cont.)
• Humans violate classical assumptions
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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
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Information Processing
Framework
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Cue reception and integration
Hypothesis generation
Hypothesis evaluation and selection
Generation and selection of action(s)
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Factors Affecting Decision Making
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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
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Heuristics and Biases
• Heuristic
• “rule of thumb”
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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
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Heuristics in Obtaining and Using
Cues
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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)
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Heuristics in Hypothesis
Generation
• Generation of limited number of
hypotheses/potential solutions
• Availability heuristic
• recency
• frequency
• Representativeness heuristic (“typicality”)
• Overconfidence
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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
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Heuristics in Action Selection
• Consideration of small number of actions
• Availability heuristic for actions
• Availability of possible outcomes
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Naturalistic Decision Making
• Decision making in the “real world”
• Characteristics
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ill-structured problems
uncertain, dynamic environments
lots of (changing) information
iterative cognition (not once-through)
multiple (conflicting, changing) goals
high risk
multiple persons
complexity
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Skill-, Rule-, Knowledge-Based
Performance
• Knowledge-based performance
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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
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Skill-, Rule-, Knowledge-Based
Performance (cont.)
• Skill-based performance
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experts, experienced decisions makers
automatic, unconscious
requires less attention, but must be managed
errors: misallocation of attention
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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
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Improving Human Decision
Making
• Redesign
• environment
• displays
• controls
• Training
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use heuristics appropriately
overcome biases
improve metacognition
enhance perceptual skills
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decision tables
decision trees
expert systems
decision support systems
• Decision Aids
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Problem Solving
• Problem
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goal(s)
givens/conditions
means
initial conditions  goal(s)
• Errors and Biases in Problem Solving
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inappropriate representations
fixation on previous plans
functional fixedness
limited WM
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Attention: The Flashlight Metaphor
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Attention
• Definitions
• focus of conscious thought
• means by which limited processing
resources are allocated
• Characteristics
• limited in direction
• limited in scope
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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
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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
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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
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Attention: Sampling
• Phenomenon
• stress-induced narrowing of attention
• Example
• Everglades L1011 accident
• Explanation
• anecdotal
• Countermeasures
• sampling reminders
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Attention: Sampling
• Phenomenon
• excessive sampling
• Example
• keep looking at clock
• Explanation
• memory loss
• Countermeasures
• train memory
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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
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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.
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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
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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
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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
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on wet, slippery road surface
reading map
dialing cell phone
talking with passenger
worrying about fuel quantity
• Significance
• high workload  poor task performance
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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
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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)
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Other Cognitive Functions
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Deduction
Induction
Situation Awareness
Planning
Problem Solving
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Stage Model of Information Processing
Mental Resources
Sensing &
Perception
•vision
•hearing
• ...
•perception
Stimuli
Cognition
Working
Memory
•situation
awareness
•decision making
•planning
•attention
•task management
Long Term Memory
World
Response
•Fitts’ Law
•Hicks’ Law
Responses
Response Selection: Reaction
Time
Definition:
• time it takes for a human to respond to a
stimulus
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Reaction Time Experiments (1)
• Simple RT (Donder’s A)
1 stimulus
1 response
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Reaction Time Experiments (2)
• Choice RT (Donder’s B)
….
….
1-to-1 match
n stimuli
n responses
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Reaction Time Experiments (3)
• Donder’s C
...
n stimuli
1 response for 1 stimuli
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Response: Selection
• Phenomenon
• response time proportional to stimulus
uncertainty
• Example
• radar operator detecting and identifying
radar contacts
• Explanation
• Hick Hyman Law
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Hick Hyman Law
Response time is proportional to stimulus
uncertainty.
OR, equivalently
Response time is proportional to stimulus
information content.
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Information Theory
• Concept
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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.
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Information Measurement
(Equiprobable Case)
• Formula
H = 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
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Rationale
Number of binary choices needed to pick right
message.
5
6
7
8
2
5
6
7
8
3
5 6
1
 3 bits
1
2
3
4
6
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Non-Equiprobable Case
N
H = - S pi log2 pi
i=1
N = number of messages
pi = P(message i is received)
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Non-Equiprobable Example
• Message probabilities
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p1 = 0.25
p2 = 0.25
p3 = 0.45
p4 = 0.05
• Information content
H = -[ 0.25(-2.0) + 0.25(-2.0) + 0.45(-1.15) +
0.05(-4.32)]
= 1.73 bits
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Hick’s Law (Hick-Hyman Law)
• RT = a + b H(s)
H(s) = info in stimulus
Reaction Time
(ms)
Assumption: human is perfect
channel
H (s) in bits
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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
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Response: Selection
• Phenomenon
• simple RT inversely proportional to
stimulus intensity
• Example
• cockpit master warning
• Explanation
• salience
• Countermeasures
• control stimulus intensity
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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
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Response: Selection
• Phenomenon
• response time inversely proportional to subset
familiarity
• Example
• trained radar operator vs untrained radar operator
• Explanation
• response automaticity
• Countermeasures
• training
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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
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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
• ?
80
Response: Selection
• Phenomenon
• response time inversely proportional to stimulusresponse compatibility
• Example
• power plant operator acknowledging fault
annunciation
• Explanation
• automatic responses require little processing
• Countermeasures
• enhance stimulus-response compatibility
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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
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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
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Other Factors Affecting RT
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Stimulus complexity
Workload
Stimulus location
Task interference/workload
Motivation
Fatigue
Environmental variables
etc.
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