Learning and memorization of classifications.

Shepard, Roger N.; Hovland, Carl I.; Jenkins, H. M.

doi:10.1037/h0093825

Cited by 708 publications

(1,250 citation statements)

References 37 publications

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“…More work needs to be done in order to model category intuitiveness in a satisfactory way. For example, with the Shepard, Hovland, Jenkins (1961) data, the unsupervised GCM performed better than both the simplicity model and the rational model. In the case of the 5-4 category structure (Medin & Schaffer, 1978), the simplicity model came out ahead, with the unsupervised GCM providing the second best fit.…”

Section: Discussionmentioning

confidence: 98%

Predicting category intuitiveness with the rational model, the simplicity model, and the generalized context model.

Pothos

Bailey

2009

Journal of Experimental Psychology: Learning, Memory, and Cogni

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This is the unspecified version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractNaïve observers typically perceive some groupings for a set of stimuli as more intuitive than others. The problem of predicting category intuitiveness has been historically considered the remit of models of unsupervised categorization. In contrast, this paper develops a measure of category intuitiveness from one of the most widely supported models of supervised categorization, the Generalized Context Model (GCM). Considering different category assignments for a set of instances, we ask how well the GCM can predict the classification of each instance on the basis of all the other instances. The category assignment that results in the smallest prediction error is interpreted as the most intuitive for the GCM-we call this way of applying the GCM unsupervised GCM. The paper then systematically compares predictions of category intuitiveness from the unsupervised GCM and two models of unsupervised categorization, the simplicity model and the rational model. We found that the unsupervised GCM compares favorably to the simplicity model and rational model. This success of the unsupervised GCM illustrates that the distinction between supervised and unsupervised categorization may have to be reconsidered. However, no model emerges as clearly superior, indicating that there is more work to be done in understanding and modeling category intuitiveness.

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Section: Discussionmentioning

confidence: 98%

Predicting category intuitiveness with the rational model, the simplicity model, and the generalized context model.

Pothos

Bailey

2009

Journal of Experimental Psychology: Learning, Memory, and Cogni

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“…Medin et al, 1987). Similarly, Combination Theory predicts that single-dimension category structures are generally learned more quickly than multiattribute category structures (Shepard et al, 1961) -the assumption is that participants test the lowest effort, single-dimension, hypotheses first (cf. Nosofsky et al, 1994).…”

Section: Evidence For Combination Theorymentioning

confidence: 99%

“…Love (2002) reported that, under incidental conditions, a particular type of overall similarity classification, a Type IV problem (Shepard et al, 1961), was easier to learn than an exclusive-or classification in which one stimulus dimension was irrelevant (a Type II problem).…”

Section: Limitations and Extensionsmentioning

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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“…Rosch, Mervis, Gray, Johnson, & Boyes-Braem, 1976;Ross & Murphy, 1999;Medin et al, 2005), or understanding category learning using artificially constructed stimulus sets (e.g. Shepard et al, 1961). Studies of real-world domains have shown that people learn richly structured categories, and have identified cross-cutting systems of categories in several domains.…”

Section: Empirical Evidence For Cross-cutting Systems Of Categoriesmentioning

confidence: 99%

“…Love, Medin, & Gureckis, 2004;Medin & Schaffer, 1978;Nosofsky, 1984;Shepard, Hovland, & Jenkins, 1961). The second alternative formalizes the intuition that people's cross-categorization behavior arises as a consequence of successive attempts to account for poorly explained features.…”

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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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Learning and memorization of classifications.

Cited by 708 publications

References 37 publications

Predicting category intuitiveness with the rational model, the simplicity model, and the generalized context model.

Predicting category intuitiveness with the rational model, the simplicity model, and the generalized context model.

Combination or Differentiation? Two theories of processing order in classification

A probabilistic model of cross-categorization

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