Form or Function: A Comparison of Expert and Novice Judgments of Similarity Among Fish

Boster, James S.; Johnson, Jeffrey C.

doi:10.1525/aa.1989.91.4.02a00040

Cited by 209 publications

(218 citation statements)

References 49 publications

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“…Finally, Ross & Murphy showed that both taxonomic and situation-based category labels prime retrieval of category members, and that both kinds of categories can guide inferences in inductive reasoning. Similar results have been obtained by researchers investigating the domains of biology (Shafto & Coley, 2003;Boster & Johnson, 1989;Medin et al, 2005;Proffitt, Coley, & Medin, 2000) and person categorization (Nelson & Miller, 1995;Smith, Fazio, & Cejka, 1996;Zarate & Smith, 1990), and in research with children (Nguyen & Murphy, 2003;Nguyen, 2007). Together, these results provide a compelling demonstration that people spontaneously produce, consistently agree about, and reason based on multiple systems of categories.…”

Section: Empirical Evidence For Cross-cutting Systems Of Categoriessupporting

confidence: 80%

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: Empirical Evidence For Cross-cutting Systems Of Categoriessupporting

confidence: 80%

A probabilistic model of cross-categorization

et al. 2011

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“…A variety of research has shown that the different ways in which categories are used can influence their representation (e.g., Boster & Johnson, 1989;Medin et al, 1997). In addition, we may learn categories in different ways related to their uses (e.g., problem solving, inferences, explanations; see Solomon et al, 1999, for review).…”

Section: Category Learning and Category Representationmentioning

confidence: 99%

“…This influence of learning to use categories on the category representation occurs even if the classification is learned prior to any introduction of the use (Ross, 1999(Ross, , 2000. In addition, this influence is very long lived if people continue the use of the categories: Domain experts have representations that reflect both the classification and the specific uses that they make of the items in the domain (e.g., Boster & Johnson, 1989;Medin, Lynch, Coley, & Atran, 1997).…”

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A further investigation of category learning by inference

2002

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Categories are learned in many ways besides by classification, for example, by making inferences about classified items. One hypothesis is that classifications lead to the learning of features that distinguish categories, whereas inferences promote the learning of the internal structure of categories, such as the typical features. Experiment 1 included single-feature and full-feature classification tests following either classification or inference learning. Consistent with predictions, inference learners did better on the single tests but worse on the full tests. Experiment 2 further showed that inference learners, unlike classification learners, were no better at classifying items that they had seen at study compared with equally typical items they had not seen at study. Experiment 3 showed that features queried about during inference learning were classified better than ones not queried about, although even the latter features showed some learning on single-feature tests. The discussion focuses on how different types of category learning lead to different category representations.

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“…In the process, knowledge is built up about the instances, their typical features, and the application of the domain knowledge in a variety of tasks. The result is often an organization of domain knowledge into a category hierarchy that reflects the uses to which the knowledge is applied (Boster & Johnson, 1989;Medin, Lynch, Coley, & Atran, 1997;Ross & Murphy, 1999). In this paper, we will explore the manner in which people learn such hierarchies.…”

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confidence: 99%

“…For instance, Ross and Murphy (1999) identified salient organizations of the food domain knowledge of college students resulting from the need to plan and eat several meals a day (e.g., breakfast foods). Tree experts and fish experts have been shown to possess subdivisions of domain knowledge reflecting goal-directed interactions with members of those domains (e.g., weed trees and game fish; Boster & Johnson, 1989;Medin et al, 1997). In short, the use of category knowledge frequently has the result that information relevant for that use becomes organized and grouped into goal-relevant subcategories.…”

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confidence: 99%

Alignment effects on learning multiple, use-relevant classification systems

Sifonis

Ross

2002

Memory & Cognition

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People often learn multiple classificationsystems that are relevant to some goal or use. We compared conditions in which subclassificationwithin a category hierarchy was predicted by values on either the same (alignable) or different (nonalignable) dimensions between category hierarchies. The results indicated that learning in alignable conditions occurred in fewer blocks and with fewer errors than did learning in nonalignable conditions. This facilitation was not the result of differences between conditions in the representations learned by the participants, the number of dimensions needed for subclassification (Experiment 1), or the objective complexity of the learning task (Experiment 2). The facilitated learning in the alignable conditions appears to reflect a commitment on the part of the learner to alignment: the belief that the structure relevant to the use of one category system will also be relevant to the use of a comparable system.

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Form or Function: A Comparison of Expert and Novice Judgments of Similarity Among Fish

Cited by 209 publications

References 49 publications

A probabilistic model of cross-categorization

A probabilistic model of cross-categorization

A further investigation of category learning by inference

Alignment effects on learning multiple, use-relevant classification systems

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