Testing Adaptive Toolbox Models: A Bayesian Hierarchical Approach

Autor/inn/en	Scheibehenne, Benjamin; Rieskamp, Jorg; Wagenmakers, Eric-Jan
Titel	Testing Adaptive Toolbox Models: A Bayesian Hierarchical Approach
Quelle	In: Psychological Review, 120 (2013) 1, S.39-64 (26 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0033-295X
DOI	10.1037/a0030777
Schlagwörter	Cognitive Processes; Behavior; Models; Bayesian Statistics; Statistical Inference; Simulation; Decision Making; Evaluative Thinking; Cognitive Development; Children; Adolescents; Learning; Classification + Suchen Sie Ihr Suchwort? Cognitive process; Kognitiver Prozess; Analogiemodell; Inferential statistics; Schließende Statistik; Simulation program; Simulationsprogramm; Decision-making; Entscheidungsfindung; Kognitive Entwicklung; Child; Kind; Kinder; Adolescent; Adolescence; Adoleszenz; Jugend; Jugendalter; Jugendlicher; Lernen; Classification system; Klassifikation; Klassifikationssystem
Abstract	Many theories of human cognition postulate that people are equipped with a repertoire of strategies to solve the tasks they face. This theoretical framework of a cognitive toolbox provides a plausible account of intra- and interindividual differences in human behavior. Unfortunately, it is often unclear how to rigorously test the toolbox framework. How can a toolbox model be quantitatively specified? How can the number of toolbox strategies be limited to prevent uncontrolled strategy sprawl? How can a toolbox model be formally tested against alternative theories? The authors show how these challenges can be met by using Bayesian inference techniques. By means of parameter recovery simulations and the analysis of empirical data across a variety of domains (i.e., judgment and decision making, children's cognitive development, function learning, and perceptual categorization), the authors illustrate how Bayesian inference techniques allow toolbox models to be quantitatively specified, strategy sprawl to be contained, and toolbox models to be rigorously tested against competing theories. The authors demonstrate that their approach applies at the individual level but can also be generalized to the group level with hierarchical Bayesian procedures. The suggested Bayesian inference techniques represent a theoretical and methodological advancement for toolbox theories of cognition and behavior. (Contains 12 figures and 9 footnotes.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10