Probabilistic models of cognition: exploring representations and inductive biases
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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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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