lecture 12 – psyco 350, b1 winter, 2011

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


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


Remember/Know (Radvansky, pp 307-308)

General Findings:

factors recollection, “remember”

LOP, repetition, short (vs long) delay

Problems:

• poor terminology


Remember/Know Instructions: Rajaram

(1993)


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


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)


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)


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)


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.


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)


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%


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)


Exclusion Condition

List1Word

“NEW”Recollected

NOT Recollected


“New”Low

Familiarity

P(OLD) = P(OLDA)

R%

1-R%

1-A%

A%


Dissociating the processesData:• Inclusion: P(old) = R + (1- R)• Exclusion: P(old) = A(1-R)


Inclusion Condition

List1Word

“OLDR”Recollected

NOT Recollected


“New”Low

Familiarity

P(OLD) = P(OLDR) + P(OLDA)

R%

1-R%

A%

1-A%


Exclusion Condition

List1Word

“NEW”Recollected

NOT Recollected


“New”Low

Familiarity

P(OLD) = P(OLDA)

R%

1-R%

1-A%

A%


Dissociating the processesData:• Inclusion: P(old) = R + (1- R)• Exclusion: P(old) = A(1-R)

Parameter Estimates• Inclusion – Exclusion = R• A = Exclusion / (1-R)


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”


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


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


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


Jacoby et al. (1993): Results

Divided attention:• Inclusion task: P(old)• Exclusion task: P(old)



Results:• Inclusion: div (46%) < full (61%)• Exclusion: div (46%) > full (36%)

Interpretation:• div attention knocked out recollection• recollection accuracy in both conditions



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)


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


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)


Zajonc (1969): Results

Recognition at chance.

Preference Judgment:

65% favored exposed character.


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


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?


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


“Availability Bias” & Populations Estimation

1. People tend to UNDERestimate populations of large, obscure countries.


“Availability Bias” & Populations Estimation

2. People tend to OVERestimate populations of small, well-known countries.


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.


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.


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 .


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)


Brown & Siegler (1992): Method

Materials: 100 countries

Participants: 24 CMU undergrads

Tasks:

• Rate knowledge

• Estimate population


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


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)


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?


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


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


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


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


Importance of Availability

Importance of availability differs across tasks.__________________________________________________________________________________________

_

Determinants:• actual/perceived correlation between propx and

memory• quantity & credibility of competing information__________________________________________________________________________________________


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

lecture 12 – psyco 350, b1 winter, 2011

Documents

r psyco

list of words list

words rautomatic process

independent process

process dissociationevidence

process dissociation

process dissociation2

r adata