Causal Learning with Local Computations

Autor/inn/en	Fernbach, Philip M.; Sloman, Steven A.
Titel	Causal Learning with Local Computations
Quelle	In: Journal of Experimental Psychology: Learning, Memory, and Cognition, 35 (2009) 3, S.678-693 (16 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0278-7393
DOI	10.1037/a0014928
Schlagwörter	Causal Models; Cues; Memory; Heuristics; Inferences; Bayesian Statistics + Suchen Sie Ihr Suchwort? Kausalanalyse; Stichwort; Gedächtnis; Heuristik; Inference; Inferenz
Abstract	The authors proposed and tested a psychological theory of causal structure learning based on local computations. Local computations simplify complex learning problems via cues available on individual trials to update a single causal structure hypothesis. Structural inferences from local computations make minimal demands on memory, require relatively small amounts of data, and need not respect normative prescriptions as inferences that are principled locally may violate those principles when combined. Over a series of 3 experiments, the authors found (a) systematic inferences from small amounts of data; (b) systematic inference of extraneous causal links; (c) influence of data presentation order on inferences; and (d) error reduction through pretraining. Without pretraining, a model based on local computations fitted data better than a Bayesian structural inference model. The data suggest that local computations serve as a heuristic for learning causal structure. (Contains 13 figures and 1 table.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10