A Model-Based Approach to the Wisdom of the Crowd in Category Learning

Autor/inn/en	Danileiko, Irina; Lee, Michael D.
Titel	A Model-Based Approach to the Wisdom of the Crowd in Category Learning
Quelle	In: Cognitive Science, 42 (2018), S.861-883 (23 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0364-0213
DOI	10.1111/cogs.12561
Schlagwörter	Group Experience; Classification; Learning Processes; Participative Decision Making; Models; Prediction; Recognition (Psychology); Stimuli; Inferences; Case Studies + Suchen Sie Ihr Suchwort? Classification system; Klassifikation; Klassifikationssystem; Learning process; Lernprozess; Analogiemodell; Vorhersage; Recognition; Wiedererkennen; Anreizsystem; Inference; Inferenz; Case study; Fallstudie; Case Study
Abstract	We apply the "wisdom of the crowd" idea to human category learning, using a simple approach that combines people's categorization decisions by taking the majority decision. We first show that the aggregated crowd category learning behavior found by this method performs well, learning categories more quickly than most or all individuals for 28 previously collected datasets. We then extend the approach so that it does not require people to categorize every stimulus. We do this using a model-based method that predicts the categorization behavior people would produce for new stimuli, based on their behavior with observed stimuli, and uses the majority of these predicted decisions. We demonstrate and evaluate the model-based approach in two case studies. In the first, we use the general recognition theory decision-bound model of categorization (Ashby & Townsend, 1986) to infer each person's decision boundary for two categories of perceptual stimuli, and we use these inferences to make aggregated predictions about new stimuli. In the second, we use the generalized context model exemplar model of categorization (Nosofsky, 1986) to infer each person's selective attention for face stimuli, and we use these inferences to make aggregated predictions about withheld stimuli. In both case studies, we show that our method successfully predicts the category of unobserved stimuli, and we emphasize that the aggregated crowd decisions arise from psychologically interpretable processes and parameters. We conclude by discussing extensions and potential real-world applications of the approach. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01