human information processing perception, memory, cognition, response

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


WorkingMemory





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


noise only

X (decibels)

P (

stim

ulu

s in

ten

sity

= x

)

signal + noise

d’

16


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


WorkingMemory





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

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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

?

RedBlueGreenYellow

Compare with

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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.


WorkingMemory





Mental Resources

World

Decision Making and Problem Solving

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Decision Making

• Characteristics of a decision making situation• 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

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

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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

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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

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

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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.

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

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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

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


WorkingMemory





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


• Choice RT (Donder’s B) 1-to-1 match

…. n stimuli

…. n responses

66


• 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.

human information processing perception, memory, cognition, response

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