Two-Component Model of Recognition Memory: Early Thoughts and Two-Process Theory - Prof. M, Exams of Psychology

A portion of a university lecture note from a human memory course, focusing on the two-component model of recognition memory. The note covers the early thoughts of atkinson and juola (1973), the distinction between episodic and semantic memory, and the two-process theory. Techniques for measuring recollection and familiarity are also discussed, including the remember/know paradigm and roc curves.

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Uploaded on 08/09/2009

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Two components of recognition
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PSY 400, Human Memory
March 8, 2005
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Two components of recognition

memory

PSY 400, Human Memory

March 8, 2005

Housekeeping

  • Should have done Phonological Similarity by today.
  • Report is due Tues after Spr Break
  • Exam is Thurs after Spr Break.

Important things from last time

  • Recognition memory nomenclature.
  • Signal detection theory.
  • Distinction between discriminability and bias.

Overview of Today’s Material

  • Brief signal detection review
  • Two-process theory
    1. Early thought: Atkinson and Juola (1973)
    2. Episodic/semantic distinction
    3. Recollection and familiarity
  • Techniques for measuring recollection and familiarity
    1. Remember/know
    2. Process dissociation procedure

Important nomenclature

Response Probe Yes No Old hit miss New false alarm correct rejection

Confidence levels in yes/no

recognition memory

  • Yes/no recognition asks you to rate your confidence on a 2-point scale.
  • Rather than responding “yes” or “no”, you give a more graded response.

Sure No Sure Yes Sure Yes Sure Yes Sure Yes

Bias affects hit and fa rate

Discriminability

  • How far apart the distributions are, in units of the standard deviation, called d′.
  • d′^ is a measure of discriminability

Estimating d′^ from data

Given a hit rate H and a false alarm rate F A, you can estimate d′^ as

d′^ = z(H) − z(F A),

where z(x) is the z-transform.

Reciever operating characteristic

curves

  • ROC curves plot P (hit) as a function of P (fa)
  • Allow a way to assess memory at different criteria.
  • No discriminability means no memory means P (hit) = P (fa)
  • If SDT applies, you should see a special type of curve.

Imagine multiple criteria on our old

and new distributions

ROC curves

  • Several different criteria for “yes”
  • ROC is hits vs false alarms... -... for several different criteria.

(^00) 0.2 0.4 (^) P(fa)0.6 0.8 1

1

P(hit)

One possibility

page 27, Kahana book.

What’s a zROC curve

Simply plots z-transformed HR as a function of z-transformed FAR.