Varieties of Numerical Estimation: A Unified Framework [Konferenzbericht] Paper presented at the Annual Meeting of the Cognitive Science Society (39th, 2017).

Autor/inn/en	Qin, Jike; Kim, Dan; Opfer, John
Titel	Varieties of Numerical Estimation: A Unified Framework [Konferenzbericht] Paper presented at the Annual Meeting of the Cognitive Science Society (39th, 2017).
Quelle	(2017), (6 Seiten) PDF als Volltext (1); PDF als Volltext kostenfreie Datei (2) Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Mathematics Skills; Numeracy; Arithmetic; Cognitive Processes; Cognitive Development; Kindergarten; Elementary School Students; Grade 1; Grade 2; Adults; Computation; Predictor Variables; Number Concepts + Suchen Sie Ihr Suchwort? Mathmatics achievement; Mathematics ability; Mathematische Kompetenz; Rechenkompetenz; Addition; Arithmetik; Arithmetikunterricht; Rechnen; Cognitive process; Kognitiver Prozess; Kognitive Entwicklung; School year 01; 1. Schuljahr; Schuljahr 01; School year 02; 2. Schuljahr; Schuljahr 02; Prädiktor; Number concept; Zahlbegriff
Abstract	There is an ongoing debate over the psychophysical functions that best fit human data from numerical estimation tasks. To test whether one psychophysical function could account for data across diverse tasks, we examined 40 kindergartners, 38 first graders, 40 second graders and 40 adults' estimates using two fully crossed 2 × 2 designs, crossing symbol (symbolic, non-symbolic) and boundedness (bounded, unbounded) on free number-line tasks (Experiment 1) and crossing the same factors on anchored number-line tasks (Experiment 2). This strategy yielded 4 novel tasks to assess the generalizability of the models. Across all 8 tasks, 90% of participants provided estimates better fit by a mixed log-linear model than other cognitive models, and the weight of the logarithmic component ([lambda]) decreased with age. After controlling for age, the weight of the logarithmic component ([lambda]) significantly predicted arithmetic skills, whereas parameters of other models failed to do so. Results suggest that the logarithmic-to-linear shift theory provides a unified account of numerical magnitude estimation and provides uniquely accurate predictions for mathematical proficiency. [This paper was published in: "Proceedings of the 39th Annual Meeting of the Cognitive Science Society," edited by G. Gunzelmann et al., Cognitive Science Society, 2017, pp. 2943-2948.] (As Provided).
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01