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A Bayesian model for implicit effects in perceptual identification.

L J Schooler1, R M Shiffrin, J G Raaijmakers

  • 1Department of Psychology, Moore Building, Pennsylvania State University, University Park, Pennsylvania 16802, USA. ljs24@psu.edu

Psychological Review
|February 24, 2001
PubMed
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This study extends the Retrieving Effectively from Memory (REM) model to explain implicit memory. It shows how adding context information to memory representations accounts for prior study effects in perceptual identification tasks.

Area of Science:

  • Cognitive Psychology
  • Memory Research
  • Computational Neuroscience

Background:

  • Episodic memory models like REM explain explicit recall.
  • Implicit memory phenomena, such as perceptual identification, pose challenges for existing models.
  • Prior study effects are observed when stimuli are presented briefly or when choices are similar.

Purpose of the Study:

  • To extend the REM model to account for implicit memory phenomena.
  • To explain how prior study influences perceptual identification tasks.
  • To test a model where prior study adds context information to memory representations.

Main Methods:

  • Adapting the REM model to incorporate context information.
  • Utilizing a Bayesian decision process at test to compare perceptual and contextual features.

Related Experiment Videos

  • Fitting the modified model to existing perceptual identification data (Ratcliff & McKoon, 1997).

    • Main Results:

    • The modified REM model successfully fits data where prior study effects are strongest with brief presentations and similar foils.
    • The model demonstrates that adding context information explains the observed effects of prior study.
    • Increased similarity between alternatives amplifies prior study effects by reducing noise.

    Conclusions:

    • The REM model, augmented with context information, can explain implicit memory phenomena.
    • Prior study enhances memory by adding context, which aids discrimination, especially when alternatives are confusable.
    • This approach provides a unified framework for understanding both explicit and implicit memory effects.