Can semi-supervised learning explain incorrect beliefs about categories?

Cognition 120 (1):106-118 (2011)

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Abstract
Three experiments with 88 college-aged participants explored how unlabeled experiences—learning episodes in which people encounter objects without information about their category membership—influence beliefs about category structure. Participants performed a simple one-dimensional categorization task in a brief supervised learning phase, then made a large number of unsupervised categorization decisions about new items. In all three experiments, the unsupervised experience altered participants’ implicit and explicit mental category boundaries, their explicit beliefs about the most representative members of each category, and even their memory for the items encountered during the supervised learning phase. These changes were influenced by both the range and frequency distribution of the unlabeled stimuli: mental category boundaries shifted toward the middle of the range and toward the trough of the bimodal distribution of unlabeled items, whereas beliefs about the most representative category members shifted toward the modes of the unlabeled distribution. One consequence of this shift in representations is a false-consensus effect (Experiment 3) where participants, despite receiving very disparate training experiences, show strong agreement in judgments about representativeness and boundary location following unsupervised category judgments.
Keywords Categorization  Learning  Stereotyping  Semi-supervised learning
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DOI 10.1016/j.cognition.2011.03.002
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References found in this work BETA

The Adaptive Nature of Human Categorization.John R. Anderson - 1991 - Psychological Review 98 (3):409-429.
Alcove-an Exemplar-Based Connectionist Model of Category Learning.Jk Kruschke & Rm Nosofsky - 1991 - Bulletin of the Psychonomic Society 29 (6):475-475.

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