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Rational constructivism: A new way to bridge rationalism and empiricism

Published online by Cambridge University Press:  23 April 2009

Alison Gopnik
Affiliation:
Department of Psychology, University of California at Berkeley, Berkeley, CA 94704. Gopnik@berkeley.edu

Abstract

Recent work in rational probabilistic modeling suggests that a kind of propositional reasoning is ubiquitous in cognition and especially in cognitive development. However, there is no reason to believe that this type of computation is necessarily conscious or resource-intensive.

Type
Open Peer Commentary
Copyright
Copyright © Cambridge University Press 2009

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References

Chater, N. & Manning, C. D. (2006) Probabilistic models of language processing and acquisition. Trends in Cognitive Sciences 10:335–44.CrossRefGoogle ScholarPubMed
Chater, N., Tenenbaum, J. B. & Yuille, A. (2006) Probabilistic models of cognition: Conceptual foundations. Trends in Cognitive Science 10:287–91.CrossRefGoogle ScholarPubMed
Gopnik, A., Glymour, C., Sobel, D. M., Schulz, L. E., Kushnir, T. & Danks, D. (2004) A theory of causal learning in children: Causal maps and Bayes nets. Psychological Review 111:132.CrossRefGoogle ScholarPubMed
Gopnik, A. & Schulz, L. (2004) Mechanisms of theory-formation in young children. Trends in Cognitive Science 8:8.CrossRefGoogle ScholarPubMed
Gopnik, A. & Schulz, L. eds. (2007) Causal learning: Psychology, philosophy, and computation. Oxford University Press.CrossRefGoogle Scholar
Gopnik, A. & Tenenbaum, J., eds. (2007) Special issue of Developmental Science on Bayes net and Bayesian methods in cognitive development. Developmental Science 10.Google Scholar
Regier, T. & Gahl, S. (2004) Learning the unlearnable: The role of missing evidence. Cognition 93:147–55.CrossRefGoogle ScholarPubMed
Tenenbaum, J. B., Griffiths, T. L. & Kemp, C. (2006) Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Sciences 10:309–18.CrossRefGoogle ScholarPubMed
Xu, F. & Tenenbaum, J. B. (2007) Word learning as Bayesian inference. Psychological Review 114:245–73.CrossRefGoogle ScholarPubMed
Yuille, A. & Kersten, D. (2006) Vision as Bayesian inference: Analysis by synthesis? Trends in Cognitive Sciences 10:301308.CrossRefGoogle ScholarPubMed