Family resemblances: Studies in the internal structure of categories

Rosch, Eleanor; Mervis, Carolyn Β.

doi:10.1016/0010-0285(75)90024-9

Cited by 4,722 publications

(3,248 citation statements)

References 19 publications

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“…This was included because people have a well-known tendency to sort based on single features in artificial categorization tasks, and there is good reason to believe that this is not representative of their real-world behavior (e.g. Rosch & Mervis, 1975). This manipulation was intended to help motivate participants to produce more real-world type behavior.…”

Section: Methodsmentioning

confidence: 99%

A probabilistic model of cross-categorization

et al. 2011

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Most natural domains can be represented in multiple ways: we can categorize foods in terms of their nutritional content or social role, animals in terms of their taxonomic groupings or their ecological niches, and musical instruments in terms of their taxonomic categories or social uses. Previous approaches to modeling human categorization have largely ignored the problem of cross-categorization, focusing on learning just a single system of categories that explains all of the features. Cross-categorization presents a difficult problem: how can we infer categories without first knowing which features the categories are meant to explain? We present a novel model that suggests that human cross-categorization is a result of joint inference about multiple systems of categories and the features that they explain. We also formalize two commonly proposed alternative explanations for cross-categorization behavior: a features-first and an objects-first approach. The features-first approach suggests that cross-categorization is a consequence of attentional processes, where features are selected by an attentional mechanism first and categories are derived second. The objects-first approach suggests that crosscategorization is a consequence of repeated, sequential attempts to explain features, where categories are derived first, then features that are poorly explained are recategorized. We present two sets of simulations and experiments testing the models' predictions about human categorization. We find that an approach based on joint inference provides the best fit to human categorization behavior, and we suggest that a full account of human category learning will need to incorporate something akin to these capabilities.People explain different aspects of everyday objects in different ways. For example, steak is high in iron because it is a meat; however, it is often served with wine because it is a dinner food. The different ways of thinking about steak underscore different ways of thinking about the domain of foods: as a system of taxonomic categories including meats and vegetables, or as a system of

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Section: Methodsmentioning

confidence: 99%

A probabilistic model of cross-categorization

et al. 2011

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“…The idea that overall similarity classification requires substantial cognitive resources may seem paradoxical, given that natural categories appear to have an overall similarity structure (Mervis & Rosch, 1981;Rosch & Mervis, 1975), and are classified accurately and rapidly (Thorpe & Imbert, 1989). However, the exposure we receive to natural objects, and the practice we have in classifying them, presumably both contribute to the speed and accuracy with which everyday overall similarity categories are employed.…”

Section: Implications For a "Family Resemblance" Account Of Natural Cmentioning

confidence: 99%

“…There could be a number of reasons for this preference. First, one might note that, outside the laboratory, most categories have an approximately overall similarity structure (Rosch & Mervis, 1975). Second, as Murphy (2002) pointed out, family resemblance categories are inductively rich, while single-dimension categories are inductively impoverished.…”

Section: Why Combine?mentioning

confidence: 99%

Combination or Differentiation? Two theories of processing order in classification

Wills

Inkster

Milton

2015

Cognitive Psychology

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Does cognition begin with an undifferentiated stimulus whole, which can be divided into distinct attributes if time and cognitive resources allow (Differentiation Theory)? Or does it begin with the attributes, which are combined if time and cognitive resources allow (Combination Theory)?Across psychology, use of the terms analytic and non-analytic imply that Differentiation Theory is correct -if cognition begins with the attributes, then synthesis, rather than analysis, is the more appropriate chemical analogy. We re-examined four classic studies of the effects of time pressure, incidental training, and concurrent load on classification and category learning (Kemler Nelson, 1984;Smith & Kemler Nelson, 1984;Smith & Shapiro, 1989;Ward, 1983). These studies are typically interpreted as supporting Differentiation Theory over Combination Theory, while more recent work in classification (Milton, Longmore & Wills, 2008, et seq.) supports the opposite conclusion. Across seven experiments, replication and re-analysis of the four classic studies revealed that they do not support Differentiation Theory over Combination Theory -two experiments support Combination Theory over Differentiation Theory, and the remainder are compatible with both accounts. We conclude that Combination Theory provides a parsimonious account of both classic and more recent work in this area. The presented data do not require Differentiation Theory, nor a Combination-Differentiation hybrid account.

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“…or 'how good an example of US Presidents is Joseph Stalin?' The problem here began with Rosch and Mervis (1975) who included non-members of categories in their typicality rating lists. The application of typicality ratings to non-members has continued in the literature (e.g.…”

Section: A Pragmatic Account On Non-monotonicitymentioning

confidence: 99%

“…Nor is it the case that those researching prototype theory have adopted such a restriction. Rosch and Mervis (1975), in their series of experiments on family resemblances, based similarity to prototype on attribute overlap, where the attributes were subject-generated verbal predicates which ranged over a wide variety of features (see also Hampton, 1979). Hampton (1976) had subjects cluster attributes generated by others as true of categories on the basis of the type of information involved, and found in addition to physical/perceptual characteristics there were clusters corresponding to function, location, superordinate categorization and behaviour.…”

Section: Introductionmentioning

confidence: 99%

Similarity-based categorization and fuzziness of natural categories

1998

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The adequacy of similarity to prototype as an account of categorization in natural concepts was assessed by analyzing the monotonicity of the relation between typicality of an item in a category and the probability of a positive categorization response using data from McCloskey and Glucksberg (1978). The analysis revealed a strong underlying similarity-based threshold curve, with systematic deviations. Further data collection showed that deviations from the curve could be attributed to the effects of unfamiliarity and non-categorial associations on typicality judgments, as well as differences between the perceptual appearance of an item (which tended to boost typicality) and its underlying nature (which tended to boost categorization). The results are discussed in terms of the different presuppositions and task constraints involved in rating typicality as opposed to performing a categorization.

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Family resemblances: Studies in the internal structure of categories

Cited by 4,722 publications

References 19 publications

A probabilistic model of cross-categorization

A probabilistic model of cross-categorization

Combination or Differentiation? Two theories of processing order in classification

Similarity-based categorization and fuzziness of natural categories

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