A Context-Dependent Bayesian Account for Causal-Based Categorization

Autor/inn/en	Marchant, Nicolás; Quillien, Tadeg; Chaigneau, Sergio E.
Titel	A Context-Dependent Bayesian Account for Causal-Based Categorization
Quelle	In: Cognitive Science, 47 (2023) 1, (33 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0364-0213
DOI	10.1111/cogs.13240
Schlagwörter	Classification; Bayesian Statistics; Causal Models; Evaluation Methods; Decision Making; Task Analysis; Probability + Suchen Sie Ihr Suchwort? Classification system; Klassifikation; Klassifikationssystem; Kausalanalyse; Decision-making; Entscheidungsfindung; Aufgabenanalyse; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie
Abstract	The causal view of categories assumes that categories are represented by features and their causal relations. To study the effect of causal knowledge on categorization, researchers have used Bayesian causal models. Within that framework, categorization may be viewed as dependent on a likelihood computation (i.e., the likelihood of an exemplar with a certain combination of features, given the category's causal model) or as a posterior computation (i.e., the probability that the exemplar belongs to the category, given its features). Across three experiments, in combination with computational modeling, we offer evidence that categorization is better accounted for by assuming that people compute posteriors and not likelihoods, though both probabilities are closely related. This result contrasts with existing analyses of causal-based categorization, which assume that likelihood computations give a good approximation of human judgments. We also find that people are able to compute likelihoods in a closely related task that elicits judgments of consistency rather than category membership judgments. Our analyses show that people do use causal probabilistic information as prescribed by a Bayesian model but that they flexibly compute likelihoods or posteriors depending on the task. We discuss our results in relation to the relevant literature on the topic. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01