The Bayesian evaluation of categorization models: Comment on Wills and Pothos (2012) (original) (raw)
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On the adequacy of current empirical evaluations of formal models of categorization
Psychological Bulletin, 2012
Categorization is one of the fundamental building blocks of cognition, and the study of categorization is notable for the extent to which formal modeling has been a central and influential component of research. However, the field has seen a proliferation of noncomplementary models with little consensus on the relative adequacy of these accounts. Progress in assessing the relative adequacy of formal categorization models has, to date, been limited because (a) formal model comparisons are narrow in the number of models and phenomena considered and (b) models do not often clearly define their explanatory scope. Progress is further hampered by the practice of fitting models with arbitrarily variable parameters to each data set independently. Reviewing examples of good practice in the literature, we conclude that model comparisons are most fruitful when relative adequacy is assessed by comparing well-defined models on the basis of the number and proportion of irreversible, ordinal, penetrable successes (principles of minimal flexibility, breadth, good-enough precision, maximal simplicity, and psychological focus).
Thirty categorization results in search of a model
Journal of Experimental Psychology: …, 2000
(2000) conducted a meta-analysis of 30 data sets reported in the classification literature that involved use of the "5-4" category structure introduced by D. L. Medin and M. M. Schaffer (1978). The meta-analysis was aimed at investigating exemplar and elaborated prototype models of categorization. In this commentary, the author argues that the meta-analysis is misleading because it includes many data sets from experimental designs that are inappropriate for distinguishing the models. Often, the designs involved manipulations in which the actual 5-4 structure was not, in reality, tested, voiding the predictions of the models. The commentary also clarifies various aspects of the workings of the exemplar-based context model. Finally, concerns are raised that the all-or-none exemplar processes that form part of Smith and Minda's (2000) elaborated prototype models are implausible and lacking in generality.
A Causal-Model Theory of Categorization
Cognitive Science - COGSCI, 1999
In this article I propose that categorization decisions are often made relative to causal models of categories that people possess. According to this causal-model theory o f categorization, evidence of an exemplar's membership in a category consists of the likelihood that such an exemplar can be generated by the category's causal model. Bayesian networks are proposed as a representation of these causal models. Causal-model theory was fit to categorization data from a recent study, and yielded better fits than either the prototype model or the exemplar-based context model, b y accounting, for example, for the confirmation and violation of causal relationships and the asymmetries inherent in such relationships.
Unifying rational models of categorization via the hierarchical Dirichlet process
Proceedings of the 29th annual conference of the cognitive science society, 2007
Models of categorization make different representational assumptions, with categories being represented by prototypes, sets of exemplars, and everything in between. Rational models of categorization justify these representational assumptions in terms of different schemes for estimating probability distributions. However, they do not answer the question of which scheme should be used in representing a given category. We show that existing rational models of categorization are special cases of a statistical model called ...
2021
The emergence of Bayesian causal models has had an enormous influence on how researchers understand people’s processing of probabilistic information, providing a description of cognitive phenomena and allowing their formalization and ensuing mathematical predictions. In the topic of categorization, the Generative Model (GM) is one such Bayesian formalization. Over three different experiments we tested if the GM can predict human causal-based categorization when subjects are facing probabilistic information about feature base-rates and about feature’s causal-strength. In our experiments we implemented a condition designed to favor subjects’ use of interfeature causal relations when making judgments (i.e., the Consistency condition). We contrasted this condition with a typical Category Membership condition. Our data suggest that on both conditions participants were doing causal-based processing. However, our data also shows that subjects in the Category Membership condition weighted c...
The Right Tool for the Job: Information-Processing Analysis in Categorization
2001
showed that mathematical approximations of several popular categorization theories could be fit equally well to the average "percentage of 'A' responses" in their meta-analysis of studies that used the 5-4 category structure. They conclude that the 5-4 category structure is not a useful paradigm for explaining categorization in terms of cognitive processes. We disagree with their conclusion, and contend instead that the problem lies with the data collection and analysis methods typically used to study categorization (in this and other category structures). To support this claim, we describe a recently completed study in which we collected and used a variety of converging data to reveal the details of participants' cognitive processes in a 5-4 category structure task.
Comparing Categorization Models
Journal of Experimental Psychology: General, 2004
Four experiments are presented that competitively test rule-and exemplar-based models of human categorization behavior. Participants classified stimuli that varied on a unidimensional axis into 2 categories. The stimuli did not consistently belong to a category; instead, they were probabilistically assigned. By manipulating these assignment probabilities, it was possible to produce stimuli for which exemplar-and rule-based explanations made qualitatively different predictions.F. G. Ashby and J. T. Townsend's (1986) rule-based general recognition theory provided a better account of the data than R. M. Nosofsky's (1986) exemplar-based generalized context model in conditions in which the to-be-classified stimuli were relatively confusable. However, generalized context model provided a better account when the stimuli were relatively few and distinct. These findings are consistent with multiple process accounts of categorization and demonstrate that stimulus confusion is a determining factor as to which process mediates categorization.
Comparing decision bound and exemplar models of categorization
Perception & Psychophysics, 1993
for their excellent comments on an earlier version of this article. We would also like to thank Cindy Castillo and Marisa Murphy for help in typing the manuscript and Christine Duvauchelle for help in editing it. Correspondence concerning this article should be addressed to F.