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

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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

DOI 10.1016/j.tics.2010.05.004

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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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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.
ORCID: Griffiths, Thomas L.
Kemp, Charles
Tenenbaum, Joshua B.
Venue: Trends in Cognitive Sciences
Publisher: Elsevier BV
Published: 2010-08
Volume: 14
Issue: 8
Pages: 357-364
ISSN: 1364-6613 (ISSN-L)
DOI: 10.1016/j.tics.2010.05.004
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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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Probabilistic models of cognition: exploring representations and inductive biases

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