Integrating Categorization and Decision-Making

Autor/inn/en	Zheng, Rong; Busemeyer, Jerome R.; Nosofsky, Robert M.
Titel	Integrating Categorization and Decision-Making
Quelle	In: Cognitive Science, 47 (2023) 1, (40 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0364-0213
DOI	10.1111/cogs.13235
Schlagwörter	Classification; Decision Making; Task Analysis; Feedback (Response); Interference (Learning); Probability; Bayesian Statistics; Models; Comparative Analysis + Suchen Sie Ihr Suchwort? Classification system; Klassifikation; Klassifikationssystem; Decision-making; Entscheidungsfindung; Aufgabenanalyse; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Analogiemodell
Abstract	Though individual categorization or decision processes have been studied separately in many previous investigations, few studies have investigated how they interact by using a two-stage task of first categorizing and then deciding. To address this issue, we investigated a categorization-decision task in two experiments. In both, participants were shown six faces varying in width, first asked to categorize the faces, and then decide a course of action for each face. Each experiment was designed to include three groups, and for each group, we manipulated the probabilistic contingencies between stimulus, category assignments, and decision consequences. For each group, each participant received three different sequences of category response, category feedback, decision response, and decision feedback. We found that participants were only partially responsive in the appropriate directions to the contingencies assigned to each group. Comparisons of results from different sequences provided evidence for empirical interference effects of categorization on decisions. The empirical interference effect is defined as the difference between the probability of taking a hostile action in decision-alone conditions and the total probability of taking a hostile action in categorization-decision conditions. To test competing accounts for multiple empirical results, including two-stage choice probabilities and empirical interference effects, we compared a quantum cognition model versus a two-stage exemplar categorization model at both aggregate and individual levels. Using a Bayesian information criterion, we found that the quantum model provided an overall better model fit than the exemplar model. Although both models predicted empirical interference effects, the exemplar model was able to generate probabilistic deviation by incorporating category information of the first stage into the feature representation of the subsequent decision stage, while the quantum model produced interference effect by superposition, measurement, and quantum entanglement. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01