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Implicit measurement II: From tasks to processes. Keith Payne University of North Carolina at Chapel Hill.

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implicit measurement ii from tasks to processes
Implicit measurement II: From tasks to processes

Keith Payne

University of North Carolina at Chapel Hill

slide2
“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

alien hand
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
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
overview
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
process dissociation
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
Warrington & Weiskrantz (1970): Amnesiacs

Recall:

_______________

Recognition:

Elephant: old / new?

Fragments:

Ele_________

process purity and contamination
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
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
slide16
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 )

slide17
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
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)

slide19
An example (Jacoby et al., 1993)- Compared memory under Inclusion/Exclusion instructions with Full vs. Divided attention
solving the estimates
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
assumptions
Assumptions
  • U & C independent
    • Engaging in 1 process does not change the other
  • U & C are not altered by Inclusion / Exclusion instruction
assumptions1
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
Exercise 1
  • Please do not read your answer sheet yet!
  • Read 10 sentences then take memory test after a delay
slide25
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.
grade your own
Grade your own…
  • Didn’t have (not “forgot” or “lost”)
  • Chair (not “lap”)
  • Vanished (not “melted”)
  • Might not win (not “lost”)
  • Stood on (not “climbed” or “stood on top of”)
  • Lost his balance on (not “fell off”)
  • Hit (not “broke”)
  • Put hit lips towards (not “kissed”)
  • Pushed (not “rang”)
  • Had acid (not “spilled acid”)
explanations of weapons bias
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
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?

slide34

Process dissociation in weapon bias

Control = responding as intended

Automatic bias = responding based on activated stereotypes when control fails

process dissociation contrasts with task dissociation
Process dissociation contrasts with task dissociation

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

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

interim conclusions
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
slide39

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

a graphic illustration of pd
A Graphic Illustration of PD

Control

Automatic

1 - Control

1 - Automatic

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

an alternative model
An alternative model

Automatic

Control

1 - Automatic

1 - Control

multinomial modeling batchelder reifer 1990
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
slide44

Control-dominating Model

Control Succeeds

C

Automatic Influence

Stereotypical

A

1-C

Control

Fails

Automatic InfluenceCounter-Stereotypical

1-A

Automaticity-dominating Model

Automatic Influence

A

Control Succeeds

C

No Automatic Influence

1-A

Control Fails

1-C

quad model for implicit attitude stereotype tasks conrey sherman gawronski hugenberg groom 2005
Quad Model for implicit attitude/stereotype tasks(Conrey, Sherman, Gawronski, Hugenberg, & Groom, 2005)
slide46

“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

slide47

More consistent with dual process theories?

Control Succeeds

C

Automatic Influence

Stereotypical

Control Fails

1-C

A

1-A

Automatic InfluenceCounter-Stereotypical

Control Succeeds

C

Control Fails

1-C

three points so far
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

slide50

Quad-Model

(Algebraically Equivalent Version)

C-dominant Model with Guessing

Control Succeeds

C

Control Dominates

Automatic Influence

A

1-C

Control

Fails

Guess X

OB

G

No

Automatic Influence

1-A

Guess Y

1-G

A-dominant Model with Guessing

1-OB

Automatic Influence

A

Automaticity Dominates

Control Succeeds

C

1-A

No Automatic Influence

Guess X

G

1-C

Control Fails

Guess Y

1-G

Examples from Weapon-Bias Task

source memory schizophrenia keefe arnold bayen harvey 1999
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
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
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 mind1
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…

A Perfect Correlation!

Y

X

some things to keep in mind2
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
the big picture
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