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Rational constructivism: A new way to bridge rationalism and empiricism

  • Alison Gopnik (a1)

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.

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N. Chater & C. D Manning . (2006) Probabilistic models of language processing and acquisition. Trends in Cognitive Sciences 10:335–44.

N. Chater , J. B. Tenenbaum & A Yuille . (2006) Probabilistic models of cognition: Conceptual foundations. Trends in Cognitive Science 10:287–91.

A. Gopnik , C. Glymour , D. M. Sobel , L. E. Schulz , T. Kushnir & D Danks . (2004) A theory of causal learning in children: Causal maps and Bayes nets. Psychological Review 111:132.

A. Gopnik & L Schulz . (2004) Mechanisms of theory-formation in young children. Trends in Cognitive Science 8:8.

A. Gopnik & L. Schulz eds. (2007) Causal learning: Psychology, philosophy, and computation. Oxford University Press.

A. Gopnik & J. Tenenbaum , eds. (2007) Special issue of Developmental Science on Bayes net and Bayesian methods in cognitive development. Developmental Science 10.

T. Regier & S Gahl . (2004) Learning the unlearnable: The role of missing evidence. Cognition 93:147–55.

J. B. Tenenbaum , T. L. Griffiths & C Kemp . (2006) Theory-based Bayesian models of inductive learning and reasoning. Trends in Cognitive Sciences 10:309–18.

F. Xu & J. B Tenenbaum . (2007) Word learning as Bayesian inference. Psychological Review 114:245–73.

A. Yuille & D Kersten . (2006) Vision as Bayesian inference: Analysis by synthesis? Trends in Cognitive Sciences 10:301308.

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Behavioral and Brain Sciences
  • ISSN: 0140-525X
  • EISSN: 1469-1825
  • URL: /core/journals/behavioral-and-brain-sciences
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