The DOI matches its Crossref record; 64 of the 65 references it cites were found in a registry or an index.
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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“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]
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“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]
Citations
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Whatever next? Predictive brains, situated agents, and the future of cognitive science
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Building machines that learn and think like people
Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2017) · Behavioral and Brain Sciences · doi:10.1017/s0140525x16001837 · Cited by 2,550
Self-citationHow to grow a mind: Statistics, structure, and abstraction
Tenenbaum, J. B., Kemp, C., Griffiths, T. L., & Goodman, N. D. (2011) · Science · doi:10.1126/science.1192788 · Cited by 1,815
Self-citationWhat do we mean by prediction in language comprehension?
Kuperberg, G. R., & Jaeger, T. F. (2016) · Language, Cognition and Neuroscience · doi:10.1080/23273798.2015.1102299 · Cited by 1,087
Lieder, F., & Griffiths, T. L. (2020) · Behavioral and Brain Sciences · doi:10.1017/s0140525x1900061x · Cited by 892
Self-citation
Metadata
- 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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