Who Is Sensitive to Selection Biases in Inductive Reasoning?

Autor/inn/en	Hayes, Brett K.; Liew, Shi Xian; Desai, Saoirse Connor; Navarro, Danielle J.; Wen, Yuhang
Titel	Who Is Sensitive to Selection Biases in Inductive Reasoning?
Quelle	In: Journal of Experimental Psychology: Learning, Memory, and Cognition, 49 (2023) 2, S.284-300 (17 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Hayes, Brett K.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0278-7393
DOI	10.1037/xlm0001171
Schlagwörter	Logical Thinking; Inferences; Bias; Individual Differences; Short Term Memory; Sampling; Generalization; Prediction; Bayesian Statistics; Models; Undergraduate Students + Suchen Sie Ihr Suchwort? Inference; Inferenz; Individueller Unterschied; Kurzzeitgedächtnis; Vorhersage; Analogiemodell
Abstract	The samples of evidence we use to make inferences in everyday and formal settings are often subject to selection biases. Two property induction experiments examined group and individual sensitivity to one type of selection bias: sampling frames - causal constraints that only allow certain types of instances to be sampled. Group data from both experiments indicated that people were sensitive to the effects of such frames, showing narrower generalization when sample instances were selected because they shared a target property (property sampling) than when instances were sampled because they belonged to a particular group (category sampling). Group generalization patterns conformed to the predictions of a Bayesian model of property induction that incorporates a selective sampling mechanism. In each experiment, however, there was considerable individual variation, with a nontrivial minority showing little sensitivity to sampling frames. Experiment 2 examined correlates of frames sensitivity. A composite measure of working memory capacity predicted individual sensitivity to sampling frames. These results have important implications for current debates about people's ability to factor sample selection mechanisms into their inferences and for the development of formal models of inductive inference. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01