Title: Exemplars, prototypes, similarities and rules in category representation: An example of hierarchical Bayesian analysis
Authors: Lee, Michael
Vanpaemel, Wolf # ×
Issue Date: 2008
Publisher: Elsevier Science
Series Title: Cognitive Science vol:32 issue:8 pages:1403-1424
Abstract: We demonstrate the potential of using hierarchical Bayesian methods to relate models and data in the cognitive sciences. We do this using a worked example, considering an existing model of category representation, the Varying Abstraction Model (VAM), which attempts to infer the representations people use from their behavior in category learning tasks. The VAM allows for a wide variety of category representations to be inferred, but we show how a hierarchical Bayesian analysis can provide a unifying explanation of the representational possibilities using
two parameters. One parameter controls the emphasis on abstraction in category representations, and the other controls the emphasis on similarity. Using 30 previously published data sets, we show how inferences about these parameters, and about the category representations they generate, can be used to evaluate data in terms of the ongoing exemplar versus prototype and similarity versus rules debates in the literature. Using this concrete example, we emphasize the advantages of hierarchical Bayesian models in converting model selection problems
to parameter estimation problems, and providing one way of specifying theoretically-based priors for competing models.
ISSN: 0364-0213
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Onderzoeksgroep hogere cognitie en individuele verschillen (-)
× corresponding author
# (joint) last author

Files in This Item:
File Description Status SizeFormat
leev2008.pdfMain article Published 138KbAdobe PDFView/Open


All items in Lirias are protected by copyright, with all rights reserved.

© Web of science