What Do Biased Estimates Tell Us about Cognitive Processing? Spatial Judgments as Proportion Estimation

Autor/inn/en	Zax, Alexandra; Williams, Katherine; Patalano, Andrea L.; Slusser, Emily; Cordes, Sara; Barth, Hilary
Titel	What Do Biased Estimates Tell Us about Cognitive Processing? Spatial Judgments as Proportion Estimation
Quelle	In: Journal of Cognition and Development, 20 (2019) 5, S.702-728 (27 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1524-8372
DOI	10.1080/15248372.2019.1653297
Schlagwörter	Undergraduate Students; Elementary School Students; Bayesian Statistics; Cognitive Processes; Spatial Ability; Bias; Accuracy; Performance; Cognitive Development; Geometric Concepts + Suchen Sie Ihr Suchwort? Cognitive process; Kognitiver Prozess; Räumliches Vorstellungsvermögen; Achievement; Leistung; Kognitive Entwicklung; Elementare Geometrie
Abstract	Similar estimation biases appear in a wide range of quantitative judgments, across many tasks and domains. Often, these biases (those that occur, for example, when adults or children indicate remembered locations of objects in bounded spaces) are believed to provide evidence of Bayesian or rational cognitive processing, and are explained in terms of relatively complex Bayesian models (e.g., the Category Adjustment Model). Here, we suggest that some of these phenomena may be accounted for instead within a simpler alternative theoretical framework that has previously been found to explain bias in common numerical estimation tasks across development. We report data from university undergraduate students and 7- through 10-year-olds completing a speeded linear position reproduction task. Bias in both adults' and children's responses was effectively explained in terms of a relatively simple psychophysical model of proportion estimation. These data clearly show that the proportion estimation framework is a viable alternative to theories that explain biases as the result of a Bayesian cognitive adjustment process. We also discuss our view that these data are not easily reconciled with the requirements of the more complex Category Adjustment Model that assumes estimates should exhibit a central tendency bias. (As Provided).
Anmerkungen	Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01