Cited by 569Bayesian Modeling and Causal InferenceCognitive Science and Education ResearchCategorization, perception, and language

Probabilistic models of cognition: exploring representations and inductive biases

Griffiths, Thomas L., Chater, Nick, Kemp, Charles, Perfors, Amy, Tenenbaum, Joshua B.·2010·Trends in Cognitive Sciences

Abstract

Cognitive science aims to reverse-engineer the mind, and many of the engineering challenges the mind faces involve induction. The probabilistic approach to modeling cognition begins by identifying ideal solutions to these inductive problems. Mental processes are then modeled using algorithms for approximating these solutions, and neural processes are viewed as mechanisms for implementing these algorithms, with the result being a top-down analysis of cognition starting with the function of cognitive processes. Typical connectionist models, by contrast, follow a bottom-up approach, beginning with a characterization of neural mechanisms and exploring what macro-level functional phenomena might emerge. We argue that the top-down approach yields greater flexibility for exploring the representations and inductive biases that underlie human cognition.

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Crossref · 10.1016/j.tics.2010.05.004

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  • Bayesian cognitive models optimally combine prior knowledge about the world with sensory evidence.

    “One success story is that of Bayesian cognitive models, which optimally combine prior knowledge about the world with sensory evidence21–23.”

    From Cognitive computational neuroscience (2018) · cites this paper as [21]

  • By declaring their models independent of the implementation in the brain, cognitive scientists are able to address high-level cognitive processes that are beyond the reach of current neural networks.

    “By declaring their models independent of the implementation in the brain, cognitive scientists are able to address high-level cognitive processes21,97,98 that are beyond the reach of current neural networks.”

    From Cognitive computational neuroscience (2018) · cites this paper as [21]

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Authors
Griffiths, T. L., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. B.
Affiliations
  • University of California, Berkeley (US)
  • Carnegie Mellon University (US)
  • Massachusetts Institute of Technology (US)
Corresponding author
Griffiths, Thomas L.
Venue
Trends in Cognitive Sciences
Publisher
Elsevier BV
Published
2010-08
Volume
14
Issue
8
Pages
357-364
ISSN
1364-6613 (ISSN-L)
OpenAlex
W2167956961
Cited by
568
References
65
Citations by year
2026: 23 · 2025: 33 · 2024: 41 · 2023: 48 · 2022: 53 · 2021: 40 · 2020: 29 · 2019: 28 · 2018: 44 · 2017: 36 · 2016: 37 · 2015: 30 · 2014: 29 · 2013: 28 · 2012: 32
FWCI
7.21
Type
journal article
Language
en
Open access
closed
Locations
  • Trends in Cognitive Sciences (publishedVersion)
  • PubMed (publishedVersion)
  • UWA Profiles and Research Repository (UWA) (submittedVersion)
  • Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion)
Funders
Economic and Social Research Council (RES-538-28-1001)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
  • Life Sciences › Neuroscience › Cognitive Neuroscience › Cognitive Science and Education Research
  • Social Sciences › Psychology › Experimental and Cognitive Psychology › Categorization, perception, and language
Keywords
inductive bias, mental representation, top-down modeling, connectionist models, neural mechanisms
MeSH terms
Brain, Cognition, Humans, Models, Psychological, Predictive Value of Tests, Probability, Bias

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