Human Information Processing

1
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)

3
Stage Model of Information Processing
Mental Resources
Working Memory

• Cognition
• situation
• awareness
• decision making
• planning
• attention
• task management

• Response
• Fitts Law
• Hicks Law

• Sensing
• Perception
• vision
• hearing
• ...
• perception

Long Term Memory
Stimuli
Responses
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 i city, population 1,000-10,000
• n city, population 10,000-100,000
• Color n normal
• n non-normal
• Pitch high barcode read
• low 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

8
Stage Model of Information Processing
Mental Resources
Working Memory

• Cognition
• situation
• awareness
• decision making
• planning
• attention
• task management

• Response
• Fitts Law
• Hicks Law

• Sensing
• Perception
• vision
• hearing
• ...
• perception

Long Term Memory
Stimuli
Responses
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
Stimulus
Noise
Signal Noise
Yes
Response
No
14
Signal Detection Theory (1)
noise only
P (stimulus intensity x)
X (decibels)
15
Signal Detection Theory (2)
d
noise only
signal noise
P (stimulus intensity x)
X (decibels)
16
Signal Detection Theory (3)
criterion
NO
YES
d
noise only
signal noise
P (stimulus intensity x)
X (decibels)
17
Signal Absent Condition
criterion
NO
YES
d
noise only
signal noise
P (stimulus intensity x)
P(quiet)
X (decibels)
P(false alarm)
18
Signal Present Condition
criterion
NO
YES
d
noise only
signal noise
P (stimulus intensity x)
P(hit)
P(miss)
X (decibels)
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 gt 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

23
Stage Model of Information Processing
Mental Resources
Working Memory

• Cognition
• situation
• awareness
• decision making
• planning
• attention
• task management

• Response
• Fitts Law
• Hicks Law

• Sensing
• Perception
• vision
• hearing
• ...
• perception

Long Term Memory
Stimuli
Responses
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

Red
Blue
?
Compare with
Green
Yellow
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.
• Millers 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.

29
Stage Model of Information Processing
Mental Resources
Working Memory

• Cognition
• situation
• awareness
• decision making
• planning
• attention
• task management

• Response
• Fitts Law
• Hicks Law

• Sensing
• Perception
• vision
• hearing
• ...
• perception

Long Term Memory
Stimuli
Responses
30
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)
• dont 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
• Confirmational bias
• seek only confirming evidence
• dont 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

62
Stage Model of Information Processing
Mental Resources
Working Memory

• Cognition
• situation
• awareness
• decision making
• planning
• attention
• task management

• Response
• Fitts Law
• Hicks Law

• Sensing
• Perception
• vision
• hearing
• ...
• perception

Long Term Memory
Stimuli
Responses
63
Response Selection Reaction Time

• Definition
• time it takes for a human to respond to a stimulus

64
Reaction Time Experiments (1)

• Simple RT (Donders A)

1 stimulus
1 response
65
Reaction Time Experiments (2)

• Choice RT (Donders B) 1-to-1 match
• . n stimuli
• . n responses

66
Reaction Time Experiments (3)

• Donders 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)

• Formula
• H log2 N bits
• H number of equiprobable messages
• Note
• log2 X _at_ 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 - S pi log2 pi
• i1
• 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 content
• H - 0.25(-2.0) 0.25(-2.0) 0.45(-1.15)
  0.05(-4.32)
• 1.73 bits

74
Hicks Law (Hick-Hyman Law)

• RT a b H(s)
• H(s) info in stimulus
• Assumption human is perfect channel

Reaction Time (ms)
H (s) in bits
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.