Psyco 350 Lec #12– Slide 1
Lecture 12 – Psyco 350, B1Winter, 2011
N. R. Brown
Psyco 350 Lec #12 – Slide 2
Outline
• Dual Process Models– Recognition & Remember/Know
– Process Dissociation Procedure• Direct Tests• Indirect Tests
• Implicit Memory & Real-World Estimation
• Semantic Memory
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Remember/Know: An Example – Rajaram (1993)
Exp 1. Levels of Processing• R: semantic > rhyme; K: deep = shallow
Exp 2. Pictures vs words• R: picture > words; K: picture = word
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Remember/Know (Radvansky, pp 307-308)
General Findings:
factors recollection, “remember”
LOP, repetition, short (vs long) delay
Problems:
• poor terminology
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Remember/Know Instructions: Rajaram
(1993)
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Remember/Know
General Findings:factors recollection, “remember”LOP, repetition, short (vs long) delay
Problems:• poor terminology• judgmental criteria• r/k as confidence judgment Converging Evidence:
Process dissociation studies
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Process Dissociation; Jacoby (1991)
• Two Independent Process:
recollective (R)
automatic (A)
• Strategy: set processes in opposition
manipulate factor(s) affecting recollection
2 tests:
recollection yes (Inclusion)
recollection no (Exclusion)
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Process Dissociation
2 tests:• recollection yes (Inclusion)• recollection no (Exclusion)
Goal: Compute values for R & A• Data:
Inclusion = R + A(1-R)
Exclusion = A(1-R)• Parameter Estimates
R = Inclusion – Exclusion
A = Exclusion / (1-R)
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Process Dissociation
Evidence for the role of Dual-Processes in two classes of memory test
1. A Direct Test (recognition)
2. An Indirect Task (fragment completion)
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Process Dissociation: Direct Test
• Read a list of words – List 1• Hear a list of words – List 2• Two recognition tests:
– Both tests include List 1, List 2 and novel words.
– Inclusion test: Respond “old” if word was on either list.
– Exclusion test: Respond “old” only if word was on List 2.
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Inclusion test
• Inclusion test: Respond “old” if word was on either list.– Intentional (recollective) process will have a
certain probability of concluding “old” for List 1 words – R
– Automatic process will also have a certain probability of concluding “old” for List 1 words – A
– If either process concludes “old”, the subject will respond “old”
P(old) = R + A (1-R)
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Inclusion Condition
List1Word
“OLDR”Recollected
NOT Recollected
High Familiarity “OLDA”
“New”Low
Familiarity
P(OLD) = P(OLDR) + P(OLDA)
R%
1-R%
1-A%
A%
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Exclusion test
• Exclusion test: Respond “old” only if word was on List 2.– Subject will only respond “old” to List 1 words if
two things happen:• The automatic process responds “old” due to a
feeling of familiarity – A• The intentional process fails to recognise the
word (if it had, it would recall it was from List 1) – (1-R)
P(old ) = A(1-R)
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Exclusion Condition
List1Word
“NEW”Recollected
NOT Recollected
High Familiarity “OLDA”
“New”Low
Familiarity
P(OLD) = P(OLDA)
R%
1-R%
1-A%
A%
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Dissociating the processesData:• Inclusion: P(old) = R + (1- R)• Exclusion: P(old) = A(1-R)
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Inclusion Condition
List1Word
“OLDR”Recollected
NOT Recollected
High Familiarity “OLDA”
“New”Low
Familiarity
P(OLD) = P(OLDR) + P(OLDA)
R%
1-R%
A%
1-A%
Psyco 350 Lec #12– Slide 17
Exclusion Condition
List1Word
“NEW”Recollected
NOT Recollected
High Familiarity “OLDA”
“New”Low
Familiarity
P(OLD) = P(OLDA)
R%
1-R%
1-A%
A%
Psyco 350 Lec #12– Slide 18
Dissociating the processesData:• Inclusion: P(old) = R + (1- R)• Exclusion: P(old) = A(1-R)
Parameter Estimates• Inclusion – Exclusion = R• A = Exclusion / (1-R)
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Jacoby (1991)
Materials:• List 1: READ words• List 2: HEAR wordsTests:• Inclusion
– List 1 “OLD”– List 2 “OLD”
• Exclusion– List 1 “NEW”– List 2 “OLD”
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Jacoby (1991; Exp 3)Two recognition tests (% “OLD” for READ words):
• Inclusion test P(old) = 0.48• Exclusion test P(old) = 0.37*
– R = Inclusion – Exclusion = 0.11– A = Exclusion / (1-R) = 0.37 / 0.89 = 0.42
*in exclusion condition, “OLD” are errors
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Jacoby (1991; Exps 2 & 3)Implication: When recollection is knocked out, P(OLD) in exclusion condition should equal A
Exclusion test w/ digit monitoring task (monitor for 3 odd digits in a row).
Expectation: Recollection eliminated by divided attention (digit task) – R = 0
Prediction: Exclusion = A(1-R) = 0.42 (1-0) = 0.42
Results: Exclusion w/ divided attention: Prob(Old) = 0.43
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Process Dissociation: Indirect Test
Jacoby, Toth, & Yolelinas, (1993, Exp 1b)
Study: read words• full attention• divided attention ( recollection)
Task: stem completion:• inclusion: complete with list word or guess• exclusion: complete with new words only
@ Test: green stem inclusion
red stem exclusion
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Jacoby et al. (1993): Results
Divided attention:• Inclusion task: P(old)• Exclusion task: P(old)
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Jacoby et al. (1993): Results
Results:• Inclusion: div (46%) < full (61%)• Exclusion: div (46%) > full (36%)
Interpretation:• div attention knocked out recollection• recollection accuracy in both conditions
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Jacoby et al. (1993): Results
Computing A & R
Full
R = I – E A = E/(1-R)
25 = 61 – 36 47 = 36/75
Divided
R = I – E A = E/(1-R)
0 = 46 – 46 46 = 46/(1-0)
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Process Dissociation Procedure: Conclusion
• There are no process pure tasks.
• Both recollective/explicit & automatic/implicit processes can influence performance on both direct and indirect tests of memory
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Implicit Memory & Judgment – Mere Exposure (Radvansky, p. 108-109)
Zajonc (1969)
Study:• view a set of Chinese characters
• subliminal exposure (4 msec/charter)
Test: • recognition (2IFC – exposed vs new)
-- OR -- • preference judgment (2IFC – exposed vs new)
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Zajonc (1969): Results
Recognition at chance.
Preference Judgment:
65% favored exposed character.
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Zajonc (1969): Explanation• (Even subliminal) exposure facilities subsequent
processing fluency, i.e., speed & easy of processing
– Evidence for fluency: Repetition priming effects on tasks like lexical decision & perceptual identification.
• People are sensitive to between-item differences in fluency, though not necessarily aware of their origins.
• OTBE*, people tend to attribute POSITIVE things, fluently processed stimuli.
*OTBE = Other Things Being Equal
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Fluency EffectsMemory & perceptual judgments:• recognition• recency• frequency• loudness
Non-mnemonic Judgments:• liking/preferences• truth• fame• r-w world estimates
Question:
• When, why, and to what extent does fluency (implicit memory) affect knowledge-based judgment?
Psyco 350 Lec #12– Slide 31
Estimate the current populations of the following countries.
Actual Pop Estimate 1992
2006 . Austria 37.0 mil 7.6 mil 8.2 mil
Bangladesh 15.0 mil 114.7 mil 147.2 mil
Nigeria 16.5 mil 115.6 mil 131.9 mil
Norway 24.5 mil 4.2 mil 4.6 mil
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“Availability Bias” & Populations Estimation
1. People tend to UNDERestimate populations of large, obscure countries.
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“Availability Bias” & Populations Estimation
2. People tend to OVERestimate populations of small, well-known countries.
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A Possible Explanation for Availability Bias
Domain-specific knowledge & fluency-based intuitions can influence real-world estimation.
For populations estimation:– People use fluency/familiarity/availability to
gauge relative population size.
– Assumption: better known countries have larger populations then less-well known countries.
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Availability
Terminology:• Tversky & Kahneman (1973) identified the “Availability
Heuristic.” • when ease-of-retrieval used to estimate frequency or
probability of events.
Generalization: • ease-of-retrieval fluency, familiarity• “availability” used in situations in which fluency is
found to affect judgment and decision making.
Psyco 350 Lec #12– Slide 36
Availability
The Logic of the Availability (fluency, familiarity)
Assume: propx correlates w/ memory
Goal: propx for itemi?
Mechanism:
• assesses availability of info for itemi.
• use assessment as index of propx for itemi .
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Implicit Memory & Real-Estimation
Brown & Siegler (1992)
Background: availability might be a good cue for estimating population. But is it used?
Reason: population & media exposure highly correlated
• R(New York Times index/ True Pop) = .59
Prediction: • estimated population should correlate strongly w/
rated knowledge (a proxy for availability)
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Brown & Siegler (1992): Method
Materials: 100 countries
Participants: 24 CMU undergrads
Tasks:
• Rate knowledge
• Estimate population
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Brown & Siegler (1992): Results
Important results:• As predicted, R(est w/ know) quite high (.58)• [R(est w/know) = .58] >> [R(est/true) = .41]
Interpretation:• pop-estimates based in availability-base intuitions
Mean Rank-Order Correlations
True Pop
NYT Index
Est Pop
Estimated Population .41 .57Rated Knowledge .37 .70 .58
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Availability & Population Estimation
Interpretation:• pop-estimates based in availability-base intuitions
An Alterative Interpretation:
• People hold preexisting beliefs about the size of well-known countries.
• These beliefs are biased by media coverage.
• People infer that unknown countries are small.
(Recognition Heuristic – Gigerenzer & Goldstein, 1996)
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Availability & Population Estimation• People can and do
justify their estimates with reference to task relevant knowledge.
• Size categories are often mentioned.
• Comparisons w/ other countries also occur
Key question:• Are size categories
retrieved or inferred?
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Brown, Cui, & Gordon (2002)
Aim: Determine whether population estimation is sensitive to priming, as Availability account predicts.
Method:
• Phase 1 – rate knowledge– 52 countries (primed set)
• Phase 2 – estimate populations– 52 primed countries & 52 unprimed countries*
* primed & unprimed sets matched for estimated pop, rated kn, actual pop,
actual area & region
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Brown et al. (2002): Results
Availability Prediction: Primed > Unprimed
Results:• Primed: 23.3 million• Unprimed: 21.2 million
2.1 million*• % 10%
Interpretation:
knowledge ratings availability/fluency in primed set
Availability/fluency influenced estimation process
Psyco 350 Lec #12 – Slide 44
Another Example: Fatality Estimates
Actual Fatality Rate
1e+0 1e+1 1e+2 1e+3 1e+4 1e+5 1e+6
Med
ian
Est
imat
ed F
atal
ity R
ate
1e+0
1e+1
1e+2
1e+3
1e+4
1e+5
1e+6
Smallpox Vac
Venomous Bites
Syphilis
Horses
Lightning
MeningitusBicycle Acc
Animal Att
Fireworks
Pregnancy
Train Crash
Electrocution
Appendicitis
Polio
Alcohol
Cold
Snowmobles
Tuberculosis
Hepatitis
Fire
DrowningAsthma
Homicide
drug ODs
AIDS
Emphysema
Stomach Cancer
Falls
Traffic Acc
Suicide
Breast Cancer
LeukemiaDiabetes
Lung Cancer
Stroke
Heart Disease
All Accidents
All Cancer
All CausesAbsoulte Format Group
(N=29)
R2 = .71; = .56
Task: How many Canadians died of CauseX last year?
Results:• reasonable correlation
between estimated & true fatality rate.
• Availability Bias: holding true frequency constant, more vivid causes elicit estimates
Psyco 350 Lec #12 – Slide 45
Another Example: Fatality Estimates
Actual Fatality Rate
1e+0 1e+1 1e+2 1e+3 1e+4 1e+5 1e+6
Med
ian
Est
imat
ed F
atal
ity R
ate
1e+0
1e+1
1e+2
1e+3
1e+4
1e+5
1e+6
Smallpox Vac
Venomous Bites
Syphilis
Horses
Lightning
MeningitusBicycle Acc
Animal Att
Fireworks
Pregnancy
Train Crash
Electrocution
Appendicitis
Polio
Alcohol
Cold
Snowmobles
Tuberculosis
Hepatitis
Fire
DrowningAsthma
Homicide
drug ODs
AIDS
Emphysema
Stomach Cancer
Falls
Traffic Acc
Suicide
Breast Cancer
LeukemiaDiabetes
Lung Cancer
Stroke
Heart Disease
All Accidents
All Cancer
All CausesAbsoulte Format Group
(N=29)
R2 = .71; = .56
Task: How many Canadians dies of CauseX last year?
Results:• reasonable correlation
between estimated & true fatality rate.
• Availability Bias: holding true frequency constant, more vivid causes elicit estimates
Psyco 350 Lec #12 – Slide 46
Importance of Availability
Importance of availability differs across tasks.__________________________________________________________________________________________
_
Determinants:• actual/perceived correlation between propx and
memory• quantity & credibility of competing information__________________________________________________________________________________________
Psyco 350 Lec #12 – Slide 47
Importance of Availability
Judgment/estimation tasks that are (sometimes) display an availability bias:
• recency (dates, recognition), truth, fatality rates, frequency, probability, corporate sales, wealth, population
Judgment/estimation tasks that do not display an availability bias:
• age, distance, area, latitude, longitude