Population of linear experts: Knowledge partitioning and function learning

Michael L. Kalish*, Stephan Lewandowsky, John K. Kruschke

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)

70 Citations (Scopus)

Abstract

Knowledge partitioning is a theoretical construct holding that knowledge is not always integrated and homogeneous but may be separated into independent parcels containing mutually contradictory information. Knowledge partitioning has been observed in research on expertise, categorization, and function learning. This article presents a theory of function learning (the population of linear experts model - POLE) that assumes people partition their knowledge whenever they are presented with a complex task. The authors show that POLE is a general model of function learning that accommodates both benchmark results and recent data on knowledge partitioning. POLE also makes the counterintuitive prediction that a person's distribution of responses to repeated test stimuli should be multimodal. The authors report 3 experiments that support this prediction.

Original languageEnglish
Pages (from-to)1072-1099
Number of pages28
JournalPsychological Review
Volume111
Issue number4
DOIs
Publication statusPublished - Oct 2004

Structured keywords

  • Memory

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