Abstraction and model evaluation in category learning

Vanpaemel, Wolf; Storms, Gerrit

doi:10.3758/brm.42.2.421

Cited by 26 publications

(31 citation statements)

References 59 publications

(75 reference statements)

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“…At least at present, using Bayesian methods is simply more technically challenging (indeed, often requiring very specialized expertise) and time consuming than using non-Bayesian methods, and even advocates of Bayesian methods sometimes counsel against their use for this reason (as in Vanpaemel and Storms, 2010). We agree.…”

Section: A Recurring Theme Of the Comment Is That Bayesian Methods Nomentioning

confidence: 96%

“…According to Vanpaemel and Storms (2010), the answer is "yes." Vanpaemel and Storms (2010) compared Bayesian and non-Bayesian methods of parameter estimation within the Varying Abstraction Model of categorization (VAM, Vanpaemel & Storms, 2008).…”

Section: A Recurring Theme Of the Comment Is That Bayesian Methods Nomentioning

confidence: 99%

“…Even where Bayesian methods can be applied, the use of non-Bayesian methods is sometimes preferable due to their computational simplicity (Vanpaemel & Storms, 2010).…”

mentioning

confidence: 99%

See 2 more Smart Citations

On the adequacy of Bayesian evaluations of categorization models: Reply to Vanpaemel and Lee (2012).

Wills

Pothos

2012

Psychological Bulletin

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Section: A Recurring Theme Of the Comment Is That Bayesian Methods Nomentioning

confidence: 96%

Section: A Recurring Theme Of the Comment Is That Bayesian Methods Nomentioning

confidence: 99%

See 1 more Smart Citation

On the adequacy of Bayesian evaluations of categorization models: Reply to Vanpaemel and Lee (2012).

Wills

Pothos

2012

Psychological Bulletin

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“…The concept 'weapons' thus is assumed to be represented by members of the category. The exemplar approach has proven successful in a large array of conditions, encompassing both artificial category learning (e.g., Busemeyer, Dewey, & Medin, 1984;Nosofsky, 1992;Vanpaemel & Storms, 2010) and natural language concepts , and in general compares favourably to the prototype approach. Obviously, these findings are problematic for the traditional models of conceptual combinations, that have their roots in prototype representations.…”

Section: Challenges To a Prototype View Of Complex Conceptsmentioning

confidence: 99%

An Exemplar Approach to Conceptual Combination

Voorspoels¹,

Storms²,

Vanpaemel

2012

PSYCHOL BELG

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Despite the success of exemplar models of representation, the general approach in the study of the representation of conceptual combinations is based on prototypes. In this study, we evaluate the exemplar view in conceptual combination, and compare it to the traditional prototype approach. For 10 complex concepts, typicality was predicted using an instantiation-based spatial exemplar model. The exemplar model's predictions were compared to the predictions of five plausible prototype models. Results clearly indicated that the exemplar model provides the best predictions of typicality for the complex concepts, and little or no unique variance in the observed typicality gradient was accounted for exclusively by the prototype model. We find that an exemplar representation of five to ten exemplars is optimal in the prediction of typicality, which is remarkably similar to earlier findings regarding simple, established concepts. Following the clear dominance of exemplar models in categorisation and concept research, the present study underlines the need of taking the exemplar approach to the next level, applying it in the more complex field of conceptual combination.

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“…The Bayesian statistical framework is becoming increasingly important and popular for implementing and evaluating psychological models, including models of psychophysical functions (Kuss, Jäkel & Wichmann, 2005), stimulus representations (Lee, 2008), category learning (Lee & Vanpaemel, 2008;Vanpaemel & Storms, 2010), signal detection , response times (Rouder, Lu, Speckman, Sun & Jiang, 2005) and decision making (Wetzels, Grasman & Wagenmakers, 2010). It is widely recognized in statistics (Gelman, Carlin, Stern & Rubin, 2004;Jaynes, 2003) and, increasingly, in psychology (Dienes, 2011;Kruschke, 2010Kruschke, , 2011Lee & Wagenmakers, 2005) that the Bayesian approach offers a complete and coherent framework for making inferences using models and data.…”

Section: Introductionmentioning

confidence: 99%

Using priors to formalize theory: Optimal attention and the generalized context model

Vanpaemel

Lee

2012

Psychon Bull Rev

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Formal models in psychology are used to make theoretical ideas precise and allow them to be evaluated quantitatively against data. We focus on one important-but underused and incorrectly maligned-method for building theoretical assumptions into formal models, offered by the Bayesian statistical approach. This method involves capturing theoretical assumptions about the psychological variables in models by placing informative prior distributions on the parameters representing those variables. We demonstrate this approach of casting basic theoretical assumptions in an informative prior by considering a case study that involves the generalized context model (GCM) of category learning. We capture existing theorizing about the optimal allocation of attention in an informative prior distribution to yield a model that is higher in psychological content and lower in complexity than the standard implementation. We also highlight that formalizing psychological theory within an informative prior distribution allows standard Bayesian model selection methods to be applied without concerns about the sensitivity of results to the prior. We then use Bayesian model selection to test the theoretical assumptions about optimal allocation formalized in the prior. We argue that the general approach of using psychological theory to guide the specification of informative prior distributions is widely applicable and should be routinely used in psychological modeling.

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Abstraction and model evaluation in category learning

Cited by 26 publications

References 59 publications

On the adequacy of Bayesian evaluations of categorization models: Reply to Vanpaemel and Lee (2012).

On the adequacy of Bayesian evaluations of categorization models: Reply to Vanpaemel and Lee (2012).

An Exemplar Approach to Conceptual Combination

Using priors to formalize theory: Optimal attention and the generalized context model

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