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    This article has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Hall, Simon Ali, Nilufa Chater, Nick Oaksford, Mike and Deng, Yong 2016. Discounting and Augmentation in Causal Conditional Reasoning: Causal Models or Shallow Encoding?. PLOS ONE, Vol. 11, Issue. 12, p. e0167741.

    Oaksford, Mike and Chater, Nick 2016. Probabilities, causation, and logic programming in conditional reasoning: reply to Stenning and Van Lambalgen (2016). Thinking & Reasoning, Vol. 22, Issue. 3, p. 336.

    Tentori, Katya Chater, Nick and Crupi, Vincenzo 2016. Judging the Probability of Hypotheses Versus the Impact of Evidence: Which Form of Inductive Inference Is More Accurate and Time-Consistent?. Cognitive Science, Vol. 40, Issue. 3, p. 758.

    Zednik, Carlos and Jäkel, Frank 2016. Bayesian reverse-engineering considered as a research strategy for cognitive science. Synthese, Vol. 193, Issue. 12, p. 3951.

    Stegmann, Ulrich E. 2015. Prospects for Probabilistic Theories of Natural Information. Erkenntnis, Vol. 80, Issue. 4, p. 869.

    Bonawitz, Elizabeth Denison, Stephanie Gopnik, Alison and Griffiths, Thomas L. 2014. Win-Stay, Lose-Sample: A simple sequential algorithm for approximating Bayesian inference. Cognitive Psychology, Vol. 74, p. 35.

    Oaksford, Mike and Chater, Nick 2014. Probabilistic single function dual process theory and logic programming as approaches to non-monotonicity in human vs. artificial reasoning. Thinking & Reasoning, Vol. 20, Issue. 2, p. 269.

    Piccinini, Gualtiero and Shagrir, Oron 2014. Foundations of computational neuroscience. Current Opinion in Neurobiology, Vol. 25, p. 25.

    Chater, Nick and Oaksford, Mike 2013. Programs as Causal Models: Speculations on Mental Programs and Mental Representation. Cognitive Science, Vol. 37, Issue. 6, p. 1171.

    Frank, Michael C. 2013. Throwing out the Bayesian baby with the optimal bathwater: Response to Endress (2013). Cognition, Vol. 128, Issue. 3, p. 417.

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The imaginary fundamentalists: The unshocking truth about Bayesian cognitive science

  • Nick Chater (a1), Noah Goodman (a2), Thomas L. Griffiths (a3), Charles Kemp (a4), Mike Oaksford (a5) and Joshua B. Tenenbaum (a6)...
Abstract
Abstract

If Bayesian Fundamentalism existed, Jones & Love's (J&L's) arguments would provide a necessary corrective. But it does not. Bayesian cognitive science is deeply concerned with characterizing algorithms and representations, and, ultimately, implementations in neural circuits; it pays close attention to environmental structure and the constraints of behavioral data, when available; and it rigorously compares multiple models, both within and across papers. J&L's recommendation of Bayesian Enlightenment corresponds to past, present, and, we hope, future practice in Bayesian cognitive science.

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COPYRIGHT: © Cambridge University Press 2011
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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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