title: A Bayesian account of reconstructive memory.
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.
authors.affiliation: University of California, Irvine.
authors.forename: Pernille
authors.initials: P
authors.lastname: Hemmer
authors.forename: Mark
authors.initials: M
authors.lastname: Steyvers
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D006801:Humans; D019359:Knowledge; D061212:Memory, Episodic; D057567:Memory, Long-Term / Q000502:physiology*; D008960:Models, Psychological; D021641:Recognition, Psychology / Q000502:physiology*; D055815:Young Adult
keywords: Bayesian models; Long-term memory; Prior knowledge; Reconstructive memory