title: Probabilistic inference in human semantic memory.
abstract: The idea of viewing human cognition as a rational solution to computational problems posed by the environment has influenced several recent theories of human memory. The first rational models of memory demonstrated that human memory seems to be remarkably well adapted to environmental statistics but made only minimal assumptions about the form of the environmental information represented in memory. Recently, several probabilistic methods for representing the latent semantic structure of language have been developed, drawing on research in computer science, statistics and computational linguistics. These methods provide a means of extending rational models of memory retrieval to linguistic stimuli, and a way to explore the influence of the statistics of language on human memory.
authors.affiliation: Department of Cognitive Sciences, University of California, Irvine, California 92697, USA. msteyver@uci.edu
authors.forename: Mark
authors.initials: M
authors.lastname: Steyvers
authors.forename: Thomas L
authors.initials: TL
authors.lastname: Griffiths
authors.forename: Simon
authors.initials: S
authors.lastname: Dennis
mesh_terms: D001245:Association Learning / Q000502:physiology; D001921:Brain / Q000502:physiology*; D006801:Humans; D011939:Mental Recall / Q000502:physiology*; D015233:Models, Statistical; D011338:Probability Theory; D011340:Problem Solving / Q000502:physiology; D011578:Psycholinguistics; D012660:Semantics; D014706:Verbal Learning / Q000502:physiology*