Accurate Knowledge about Feature Diagnosticities Leads to Less Preference for Unidimensional Strategy

Autor/inn/en	Thomas, Sujith; Srinivasan, Narayanan
Titel	Accurate Knowledge about Feature Diagnosticities Leads to Less Preference for Unidimensional Strategy
Quelle	In: Journal of Experimental Psychology: Learning, Memory, and Cognition, 49 (2023) 9, S.1396-1418 (23 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0278-7393
DOI	10.1037/xlm0001151
Schlagwörter	Classification; Bayesian Statistics; Observational Learning; Preferences; Accuracy; Learning Processes; College Students; Foreign Countries; India + Suchen Sie Ihr Suchwort? Classification system; Klassifikation; Klassifikationssystem; Imitationslernen; Learning process; Lernprozess; Collegestudent; Ausland; Indien
Abstract	In classification learning of artificial stimuli, participants learn the perfectly diagnostic dimension better than the partially diagnostic dimensions. Also, there is a strong preference for a unidimensional categorization based on the perfectly diagnostic dimension. In a different experimental procedure, called array-based classification task, participants do not exhibit a preference for a unidimensional categorization. In Experiment 1, we replicate the above results. In Experiment 2, we show that when participants learn the partially diagnostic features through repeated testing, there is a decrease in unidimensional categorization. We use Bayesian modeling to show that only those participants who learned the diagnosticity of a dimension with a high level of accuracy ([greather than or equal to]75%) used the dimension for categorization. Our results show that whenever accurate knowledge about feature diagnosticities is available, there is a lesser preference for unidimensional categorization. Our results provide a possible explanation for the preference of unidimensional categorization in classification and observation learning. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01