Unsupervised categorization and category learning.

Emmanuel M Pothos; Nick Chater

doi:10.1080/02724980443000322

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Unsupervised categorization and category learning.

Emmanuel M Pothos·Nick Chater

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Abstract

When people categorize a set of items in a certain way they often change their perceptions for these items so that they become more compatible with the learned categorization. In two experiments we examined whether such changes are extensive enough to change the unsupervised categorization for the items-that is, the categorization of the items that is considered more intuitive or natural without any learning. In Experiment 1 we directly employed an unsupervised categorization task; in Experiment 2 we collected similarity ratings for the items and inferred unsupervised categorizations using Pothos and Chater's (2002) model of unsupervised categorization. The unsupervised categorization for the items changed to resemble more the learned one when this was specified by the suppression of a stimulus dimension (both experiments), but less so when it was almost specified by the suppression of a stimulus dimension (Experiment 1, nonsignificant trend in Experiment 2). By contrast, no changes in the unsupervised categorization were observed when participants were taught a classification that was specified by a more fine tuning of the relative salience of the two dimensions.

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Association Learning Attention Classification Concept Formation Discrimination Learning Feedback Generalization, Stimulus Humans Intuition Pattern Recognition, Visual Practice, Psychological Set, Psychology Size Perception Comparative Study Journal Article Research Support, Non-U.S. Gov't

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Emmanuel M Pothos, Nick Chater. 2005. Unsupervised categorization and category learning.. https://doi.org/10.1080/02724980443000322

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