A Bayesian Account of Reconstructive Memory

Pernille Hemmer, Mark Steyvers

Research output: Contribution to journalArticlepeer-review

67 Scopus citations

Abstract

It is well established that prior knowledge influences reconstruction from memory, but the specific interactions of memory and knowledge are unclear. Extending work by Huttenlocher et al. (Psychological Review, 98 [1991] 352; Journal of Experimental Psychology: General, 129 [2000] 220), we propose a Bayesian model of reconstructive memory in which prior knowledge interacts with episodic memory at multiple levels of abstraction. The combination of prior knowledge and noisy memory representations is dependent on familiarity. We present empirical evidence of the influences of prior knowledge at multiple levels of abstraction, showing that the reconstruction of familiar objects is influenced toward the specific prior for that object, while unfamiliar objects are influenced toward the overall category.

Original languageEnglish (US)
Pages (from-to)189-202
Number of pages14
JournalTopics in Cognitive Science
Volume1
Issue number1
DOIs
StatePublished - Jan 2009
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Experimental and Cognitive Psychology
  • Linguistics and Language
  • Human-Computer Interaction
  • Cognitive Neuroscience
  • Artificial Intelligence

Keywords

  • Bayesian models
  • Long-term memory
  • Prior knowledge
  • Reconstructive memory

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