Toward a Universal Law of Generalization for Psychological Science

Shepard, Roger N.

doi:10.1126/science.3629243

Cited by 2,158 publications

(2,078 citation statements)

References 41 publications

(14 reference statements)

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“…This viewpoint makes empirical predictions that are not captured by existing spatial or featurebased theories of similarity, but which have been confirmed [17]. † Shepard's Universal Law of Generalization [18], which implies that items have a probability of confusion that is a negative exponential function of the distance between them in an internal 'space', can be derived from the assumption that the psychological similarity between two objects is a function of the complexity of the simplest…”

Section: Quantifying Simplicitymentioning

confidence: 97%

Simplicity: a unifying principle in cognitive science?

Chater

Vitányi

2003

Trends in Cognitive Sciences

348

282

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Section: Quantifying Simplicitymentioning

confidence: 97%

Simplicity: a unifying principle in cognitive science?

Chater

Vitányi

2003

Trends in Cognitive Sciences

348

282

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“…The similarity measure is typically defined as a decaying exponential function of the distance between the two stimuli, following Shepard (1987). In a prototype model (e.g., Reed, 1972), a category c is represented by a single prototypical instance.…”

Section: Representing Categoriesmentioning

confidence: 99%

Uncovering mental representations with Markov chain Monte Carlo

Sanborn

Griffiths

Shiffrin

2010

Cognitive Psychology

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A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP URL' above for details on accessing the published version and note that access may require a subscription. Richard M. Shiffrin Indiana University, Bloomington Abstract A key challenge for cognitive psychology is the investigation of mental representations, such as object categories, subjective probabilities, choice utilities, and memory traces. In many cases, these representations can be expressed as a non-negative function defined over a set of objects. We present a behavioral method for estimating these functions. Our approach uses people as components of a Markov chain Monte Carlo (MCMC) algorithm, a sophisticated sampling method originally developed in statistical physics. Experiments 1 and 2 verified the MCMC method by training participants on various category structures and then recovering those structures. Experiment 3 demonstrated that the MCMC method can be used estimate the structures of the real-world animal shape categories of giraffes, horses, dogs, and cats. Experiment 4 combined the MCMC method with multidimensional scaling to demonstrate how different accounts of the structure of categories, such as prototype and exemplar models, can be tested, producing samples from the categories of apples, oranges, and grapes.

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“…The similarity between exemplars i and j is an exponential decay function of their distance (Shepard, 1987),…”

Section: Exemplar-generalization Modelmentioning

confidence: 99%

Exemplar and prototype models revisited: Response strategies, selective attention, and stimulus generalization.

Nosofsky

Zaki

2002

Journal of Experimental Psychology: Learning, Memory, and Cogni

157

231

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J. D. Smith and colleagues (J. P. Minda & J. D. Smith, 2001; J. D. Smith & J. P. Minda, 1998,2000; J. D. Smith, M. J. Murray, & J. P. Minda, 1997) presented evidence that they claimed challenged the predictions of exemplar models and that supported prototype models. In the authors' view, this evidence confounded the issue of the nature of the category representation with the type of response rule (probabilistic vs. deterministic) that was used. Also, their designs did not test whether the prototype models correctly predicted generalization performance. The present work demonstrates that an exemplar model that includes a response-scaling mechanism provides a natural account of all of Smith et al.'s experimental results. Furthermore, the exemplar model predicts classification performance better than the prototype models when novel transfer stimuli are included in the experimental designs.

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Toward a Universal Law of Generalization for Psychological Science

Cited by 2,158 publications

References 41 publications

Simplicity: a unifying principle in cognitive science?

Simplicity: a unifying principle in cognitive science?

Uncovering mental representations with Markov chain Monte Carlo

Exemplar and prototype models revisited: Response strategies, selective attention, and stimulus generalization.

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