Population of Linear Experts: Knowledge Partitioning and Function Learning

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 functi...

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Bibliographic Details
Published in:	Psychological review 2004-10, Vol.111 (4), p.1072-1099
Main Authors:	Kalish, Michael L, Lewandowsky, Stephan, Kruschke, John K
Format:	Article
Language:	eng
Subjects:	Analysis of Variance Attention Biological and medical sciences Classification (Cognitive Process) Cognition Cognition. Intelligence Cognitive Science Cognitive Structures Concept Formation Experience Level Feedback, Psychological Fundamental and applied biological sciences. Psychology Human Humans Intellectual and cognitive abilities Knowledge Knowledge Level Knowledge Representation Learning Learning Processes Linear Models Models, Psychological Psychology Psychology. Psychoanalysis. Psychiatry Psychology. Psychophysiology Task Complexity Transfer (Psychology)
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Summary:	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.
ISSN:	0033-295X 1939-1471