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Constructing a category prototype from statistical regularities under uncertainty
Abstract
Learning the meaning of a word requires forming a semantic representation that characterizes the referential exemplarsencountered with that word. However, each learning instance is ambiguous in that the word may plausibly refer to mul-tiple entities. To the extent that learners consider multiple referents under conditions of referential uncertainty, how dothese alternatives enter into learning word meaning? We employed a cross-situational word-learning paradigm with novelcreatures to investigate whether co-occurring exemplars that were considered but not selected as the words referent wouldinfluence the category prototype. We contrasted a condition where all exemplars were labeled with a word and a condi-tion where only some of the exemplars of a category were labeled with the word later in the learning phase. Preliminaryresults are consistent with the prediction that referents that are considered but not selected contribute less to the semanticrepresentation of the word than do the selected referents.
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