Effects of classification context on categorization in natural categories

Hampton, James A.; Dubois, Danièle; Yeh, Wen-Lin

doi:10.3758/bf03195908

Cited by 30 publications

(35 citation statements)

References 22 publications

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“…At typicality level 7 it was estimated at .02. The results are in accordance with the McCloskey and Glucksberg (1978) finding and its replication by Hampton et al (2006).…”

Section: The Probabilistic Threshold Model 14supporting

confidence: 81%

“…He suggests that the variability in categorization decisions might arise from the lack of a clear context. Accordingly, Hampton et al (2006) proposed a study in which categorization decisions were to be made in one of two clearly specified contexts. It was expected that in pragmatic contexts, people would take a broad view of what may be included in a category, whereas in technical contexts, the category boundary would be drawn more tightly.…”

Section: Inter-individual Differences In Categorizationmentioning

confidence: 99%

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A probabilistic threshold model: Analyzing semantic categorization data with the Rasch model

2010

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This is the unspecified version of the paper.This version of the publication may differ from the final published version. is given to its ability to account for inter-and intra-individual differences in categorization and their relationship with item typicality. Extensions of the Probabilistic Threshold Permanent repository linkModel that can be used to uncover the nature of category representations and the sources of categorization differences are discussed.

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“…At typicality level 7 it was estimated at .02. The results are in accordance with the McCloskey and Glucksberg (1978) finding and its replication by Hampton et al (2006).…”

Section: The Probabilistic Threshold Model 14supporting

confidence: 81%

Section: Inter-individual Differences In Categorizationmentioning

confidence: 99%

A probabilistic threshold model: Analyzing semantic categorization data with the Rasch model

2010

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“…The impact of typicality is evident in categorization tasks: The more typical an exemplar of a category is, the more quickly it is verified as being a member of that category (e.g., Larochelle & Pineau, 1994;Rosch, 1975). Several studies have shown that typicality is an influential variable that explains a wide range of behavioral findings, including priming effects (Rosch, 1975), categorization probability (e.g., Hampton, Dubois, & Yeh, 2006), inductive inference (e.g., Osherson, Smith, Wilkie, López, & Shafir, 1990), memory interference effects (Keller & Kellas, 1978), and semantic substitutability (Rosch, 1977), as well as performance in production (Hampton & Gardiner, 1983) and naming (Dell'Acqua, Lotto, & Job, 2000) tasks.…”

Section: Experiments 3amentioning

confidence: 99%

Semantic memory: A feature-based analysis and new norms for Italian

et al. 2012

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Semantic norms for properties produced by native speakers are valuable tools for researchers interested in the structure of semantic memory and in category-specific semantic deficits in individuals following brain damage. The aims of this study were threefold. First, we sought to extend existing semantic norms by adopting an empirical approach to category (Exp. 1) and concept (Exp. 2) selection, in order to obtain a more representative set of semantic memory features. Second, we extensively outlined a new set of semantic production norms collected from Italian native speakers for 120 artifactual and natural basic-level concepts, using numerous measures and statistics following a featurelisting task (Exp. 3b). Finally, we aimed to create a new publicly accessible database, since only a few existing databases are publicly available online.

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“…Dolphins and whales had a similar relation to the category fish. In general, however, the close relation between the two variables has been confirmed many times over (Hampton, Dubois, & Yeh, 2006;Verheyen, Hampton, & Storms, 2010), so that in broad terms one would still expect to see a match between variation in typicality judgments and variation in feature importance judgments, with the possibility of some category-specific differences. People should be able to differentiate among characteristic features such as flight or song for birds in terms of their importance for the category, and their beliefs about these features would be expected to be reflected in their beliefs about the relative typicality of category members that possess those features to differing degrees.…”

Section: Mapping Extensional and Intensional Similaritymentioning

confidence: 99%

When intensions do not map onto extensions: Individual differences in conceptualization.

Hampton¹,

Passanisi²

2016

Journal of Experimental Psychology: Learning, Memory, and Cogni

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Concepts are represented in the mind through knowledge of their extensions (the class of items to which the concept applies) and intensions (features that distinguish that class of items). A common assumption among theories of concepts is that the 2 aspects are intimately related. Hence if there is systematic individual variation in concept representation, the variation should correlate between extensional and intensional measures. A pair of individuals with similar extensional beliefs about a given concept should also share similar intensional beliefs. To test this notion, exemplars (extensions) and features (intensions) of common categories were rated for typicality and importance respectively across 2 occasions. Withinsubject consistency was greater than between-subjects consensus on each task, providing evidence for systematic individual variation. Furthermore, the similarity structure between individuals for each task was stable across occasions. However, across 5 samples, similarity between individuals for extensional judgments did not map onto similarity between individuals for intensional judgments. The results challenge the assumption common to many theories of conceptual representation that intensions determine extensions and support a hybrid view of concepts where there is a disconnection between the conceptual resources that are used for the 2 tasks. Permanent repository link

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Effects of classification context on categorization in natural categories

Cited by 30 publications

References 22 publications

A probabilistic threshold model: Analyzing semantic categorization data with the Rasch model

A probabilistic threshold model: Analyzing semantic categorization data with the Rasch model

Semantic memory: A feature-based analysis and new norms for Italian

When intensions do not map onto extensions: Individual differences in conceptualization.

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