Implicit measurement II: From tasks to processes

Implicit measurement II: From tasks to processes

Keith PayneUniversity of North Carolina at Chapel Hill

“Very absent-minded persons in going to their bedroom to dress for dinner have been known to take off one garment after another and finally to get into bed, merely because that was the habitual issue of the first few movements when performed at a later hour,”

William James, 1890

http://www.des.emory.edu/mfp/james.html#wisdom

Environmental Dependency SyndromeLhermitte, 1986

Alien hand

“One hand tried to turn left when the other hand tried to turn right while driving a car,” (Doody & Jankovic, 1992)

Levels of control

Whole person (e.g., Lhermitte’s EDS) Different parts of same person ( e.g., Alien

hand) Different behavior

Different components of same behavior

The cognitive monster vs. the automaticity juggernaut

Overview

What is Process Dissociation and why is it useful?

Multinomial Modeling: Flexible tool for studying how intended and unintended mechanisms interact

Novel uses and new possibilities

Part 1: What in the world is process dissociation, and why would I want to do that to my data?

Process Dissociation

Developed by Larry Jacoby Separates Conscious and Unconscious uses of

memory

Implicit and Explicit memory tests show different results

Warrington & Weiskrantz (1970): Amnesiacs

Recall:

_______________

Recognition:

Elephant: old / new?

Fragments:

Ele_________

Similar dissociations in healthy subjects Tulving, Schacter, & Stark (1982)

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RecognitionFragment

Process Purity and Contamination

Dissociations suggest different forms of memory Conscious memory for episode Unconscious effect of experience; Don’t remember

episode but the past influences the present

But, comparing implicit and explicit tests assumes the each is Process Pure What if use conscious memory to fill in fragments? What if unconscious memory affects guessing on

explicit test?

Separating processes rather than tasks

Jacoby proposed using Inclusion and Exclusion instructions for performing same task (e.g., Ele_________)

Inclusion: Complete with word from study list; If you can’t remember, then use first word that comes to mind

Exclusion: Complete with first word that comes to mind that was NOT on study list Conscious memory would prevent using item

Inclusion: Complete with word from study list; If you can’t remember, then use first word that comes to mind

P (studied item) = Conscious + Unconscious * (1- Conscious )

Exclusion: Complete with first word that comes to mind that was NOT on study list

P (studied item) = Unconscious * (1 – Conscious )

Work backward to solve for Recollection & Familiarity

Conscious = P(studied item in Inclusion) – P(studied item in Exclusion)

Unconscious = P(studied item in Exclusion) / (1 – Conscious)

An example (Jacoby et al., 1993)- Compared memory under Inclusion/Exclusion instructions with Full vs. Divided attention

0.610.46 0.36 0.46

0.00.10.20.30.40.50.60.70.8

Inclusion Exclusion

Prob. responding with studied item

FullDivided

Solving the estimates

Full Attention Conscious = .61-.36 = .25 Unconscious = .36 / (1-.25) = .36 / .75 = .48

Divided Attention Conscious = .46 - .46 = 0 Unconscious = .46 / (1-0) = .46 / 1 = .46

An example: Process Estimates

0.25

0

0.48 0.46

0.00.10.20.30.40.50.60.70.8

Recollection Familiarity

Memory Processes

FullDivided

Assumptions

U & C independent Engaging in 1 process does not change the other

U & C are not altered by Inclusion / Exclusion instruction

Assumptions

Ways to test assumptions Search for theory-predicted selective effects

(dissociations) Formal model fitting

When an assumption fails, does not undermine whole approach, but specific application

Exercise 1

Please do not read your answer sheet yet! Read 10 sentences then take memory test

after a delay

The absent-minded professor didn't have his car keys. Denis the Menace sat in Santa's chair and asked for an

elephant. The children's snowman vanished when the temperature

reached 80. The gymnast made a big mistake and might not win the

gold medal. King Kong stood on the Empire State Building. The unskillful skateboarder lost his balance on the

skateboard. The Karate champion hit the cinder block. The charming prince gently put his lips towards Snow

White's cheek. The narcotics officer pushed the door bell. The clumsy chemist had acid on his coat.

Delay Task

Memory Test

Grade your own…1. Didn’t have (not “forgot” or “lost”)2. Chair (not “lap”)3. Vanished (not “melted”)4. Might not win (not “lost”)5. Stood on (not “climbed” or “stood on top of”)6. Lost his balance on (not “fell off”)7. Hit (not “broke”)8. Put hit lips towards (not “kissed”)9. Pushed (not “rang”)10.Had acid (not “spilled acid”)

Another Two Reasons…

Weapon identification

200ms

100ms

False "Tool"

False "Gun"

False "Tool"

False "Gun"

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White prime

Slow Fast

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ortio

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rors

Explanations of weapons bias Task dissociation

Implicit test = automatic process Explicit test = controlled process

Process dissociation Responses on any task reflect automatic and

controlled components

Process dissociation Jacoby (1991) What do subjects intend to do? To what extent do they respond as intended? What do subjects do when control fails?

Process dissociation in weapon bias

Control = responding as intended

Automatic bias = responding based on activated stereotypes when control fails

Automatic bias to respond “gun”

Slow response Fast response0

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BlackWhite

Control over responses

Slow response Fast response0

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Process dissociation contrasts with task dissociation

Even on “implicit measure,” the interaction of Automatic and Controlled processes key

Same degree of Automatic activation produced more or less behavioral bias, depending on Control

Interim Conclusions

Blends of multiple processes are common, even within single behavior

Process Dissociation allows taking apart complex behavior into simpler processes

Sub-processes often related differently to variables of interest

Part 2: Broadening the Scope: Process Dissociation as a special case of a more general family of models

The cognitive monster vs. the automaticity juggernaut

A Graphic Illustration of PD

Control

1 - Control

Automatic

1 - Automatic

Cong Incng

+ --

+ +

-- --

Note: + = Automatic-consistent response; -- = Non-automatic response

An alternative model

Automatic

1 - Automatic

Control

1 - Control

Cong Incng

+ +

+ --

-- --

Multinomial Modeling(Batchelder & Reifer, 1990)

Data comes from experiment

Use computer algorithm to solve For parameters (estimates)

Fit test: Compares predicted responses from model against actual data. Large discrepancies = poor fit. Can compare competing models

Cong Incng

+ --

+ +

-- --

WhiteTool

BlackGun

BlackTool

WhiteGun

+ + + +

+ + - -

- - + +

+ + - -

+ + + +

- - - -

Control Succeeds

ControlFails

Automatic Influence

Stereotypical

Automatic InfluenceCounter-

Stereotypical

C

1-C

1-A

A

Control-dominating Model

Automaticity-dominating Model

Automatic Influence

No Automatic Influence

Control Succeeds

Control Fails

A

1-A

1-C

C

Quad Model for implicit attitude/stereotype tasks(Conrey, Sherman, Gawronski, Hugenberg, & Groom, 2005)

“One of psychology’s fundamental insights is that judgments are generally the products of nonconscious systems that operate quickly, on the basis of scant evidence, and in a routine manner, and then pass their hurried approximations to consciousness, which slowly and deliberately adjusts them.”

Daniel Gilbert, 2002

WhiteTool

BlackGun

BlackTool

WhiteGun

+ + + +

+ + - -

+ + + +

- - + +

Automatic Influence

Stereotypical

Automatic InfluenceCounter-

Stereotypical

Control Succeeds

Control Fails

A

1-A

1-C

C

Control Succeeds

Control Fails1-C

C

More consistent with dual process theories?

Three points so far These models have no temporal order Control-dominating model is consistent with

theories emphasizing fast automatic process and slow controlled process

Prime or Association:

Cong-ruent

Cong-ruent

Incong-ruent

Incong-ruent

Target: X Y X Y

+ + + +

+ + - -

+ - + -

- + - +

+ + - -

+ + + ++ - + -

- + - +

White Black Black White

Tool Gun Tool Gun

Control Succeeds

ControlFails

Automatic Influence

NoAutomatic Influence

C1-C

1-A

A

Quad-Model (Algebraically Equivalent Version)

Examples from Weapon-Bias Task

Guess X

Guess Y

Control Dominates

1-G

G

Automatic Influence

No Automatic Influence

Control Succeeds

Control Fails

A

1-A

1-C

CGuess X

Guess Y

Automaticity Dominates

1-G

G

OB

1-OB

C-dominant Model with Guessing

A-dominant Model with Guessing

Part 3: Some other interesting applications of models…

Source Monitoring (Batchelder & Reifer, 1990)

Source memory & Schizophrenia(Keefe, Arnold, Bayen, & Harvey, 1999)

Are hallucinations based on faulty source monitoring?

Schizophrenic and healthy P’s studied words from 2 external sources, 2 internal sources, & 1 of each.

Applied model to separate Source Memory and Biases to attribute info to Internal vs. External sources Patients had poorer Source Memory Patients had bias to attribute internal thoughts to

external sources

Some potential novel uses

Heuristics & Biases Availability is similar to schematic inferences Anchoring and adjusting (Bishara, 2005)

Intuition vs. Reason (Ferreira et al. 2006) Red & White jelly beans: 1/10 or 10/100 (Epstein,

1994) Attitude change

Central / Peripheral processes?

Some things to keep in mind

New models need to be validated Show that each parameter can be selectively

affected by theory-predicted variables E.g., source model: More similar sources cause

poorer source discrimination, without affecting other parameters

Some things to keep in mind

To test model, needs to be identifiable Need more data cells than free parameters A saturated model fits perfectly, but…

X

Y

A Perfect Correlation!

Some things to keep in mind A more complex model will tend to fit data

better then simpler model

Model complexity: When to stop? Tradeoffs: Completeness vs. parsimony Two criteria

Can a simpler model account for data? Fit tests that correct for complexity

X

Y

XX

The BIG Picture

Any model encourages more precise thought What are the processes involved? How do they combine? How do they relate to behavior?

Emphasizes process over task Facilitates theory testing

Tools for quantifying the unobservable

Implicit measurement II: From tasks to processes

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