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      The blowfish effect: children and adults use atypical exemplars to infer more narrow categories during word learning

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          Abstract

          Learners preferentially interpret novel nouns at the basic level (‘dog’) rather than at a more narrow level (‘Labrador’). This ‘basic-level bias’ is mitigated by statistics: children and adults are more likely to interpret a novel noun at a more narrow label if they witness ‘a suspicious coincidence’ – the word applied to three exemplars of the same narrow category. Independent work has found that exemplar typicality influences learners’ inferences and category learning. We bring these lines of work together to investigate whether the content (typicality) of a single exemplar affects the level of interpretation of words and whether an atypicality effect interacts with input statistics. Results demonstrate that both four- to five-year-olds and adults tend to assign a narrower interpretation to a word if it is exemplified by an atypical category member. This atypicality effect is roughly as strong as, and independent of, the suspicious coincidence effect, which is replicated.

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          Most cited references 27

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          Basic objects in natural categories

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            Word learning as Bayesian inference.

            The authors present a Bayesian framework for understanding how adults and children learn the meanings of words. The theory explains how learners can generalize meaningfully from just one or a few positive examples of a novel word's referents, by making rational inductive inferences that integrate prior knowledge about plausible word meanings with the statistical structure of the observed examples. The theory addresses shortcomings of the two best known approaches to modeling word learning, based on deductive hypothesis elimination and associative learning. Three experiments with adults and children test the Bayesian account's predictions in the context of learning words for object categories at multiple levels of a taxonomic hierarchy. Results provide strong support for the Bayesian account over competing accounts, in terms of both quantitative model fits and the ability to explain important qualitative phenomena. Several extensions of the basic theory are discussed, illustrating the broader potential for Bayesian models of word learning. (c) 2007 APA, all rights reserved.
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              Semantic distance and the verification of semantic relations

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                Author and article information

                Journal
                applab
                Journal of Child Language
                J. Child Lang.
                Cambridge University Press (CUP)
                0305-0009
                1469-7602
                July 16 2019
                : 1-17
                Article
                10.1017/S0305000919000266
                © 2019

                https://www.cambridge.org/core/terms

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