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Cited by 507

The DOI matches its Crossref record; 117 of the 117 references it cites were found in a registry or an index.

Bayesian Models of Cognition

Open access
Griffiths, Thomas L., Kemp, Charles, Tenenbaum, Joshua B.·2001·The Cambridge Handbook of Computational Psychology
DOI 10.1017/cbo9780511816772.006
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Abstract

For over 200 years, philosophers and mathematicians have be en using probability theory to describe human cognition. While the theory of prob abilities was first developed as a means of analyzing games of chance, it quickly took on a larger and deeper significance as a formal account of how rational agents should reason in situations of uncertainty (Gigerenzer et al., 1989; Hacking, 1975). Our goal in this ch apter is to illustrate the kinds of computational models of cognition that we can build if we assume that human learning and inference approximately follow the principles of Bayesian probabilistic inference, and to explain some of the mathematical ideas and techniques underlying those models

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Citations

Cites

Show all 117 references

Research works printed but not linked: 33

  1. Griffiths, 2005, Advances in Neural Information Processing Systems 17 (pp. 537–544)
  2. Tenenbaum, 2001, Advances in neural information processing systems 13 (pp. 59–65)
  3. Griffiths, 2004, Proceedings of the 26th Annual Meeting of the Cognitive Science Society (pp. 446–451)
  4. Nosofsky, 1998, Rational models of cognition (pp. 218–247)
  5. Osherson, 1990, 97, 185
  6. Hofmann, 1999, Proceedings of the Twenty-Second Annual International SIGIR Conference (pp. 50–57)
  7. Buehner, 1997, Proceedings of the 19th Annual Conference of the Cognitive Science Society (pp. 55–61)
  8. 10.1007/978-1-4612-4374-8 · doi:10.1007/978-1-4612-4374-8
  9. Tenenbaum, 2007, Causal learning: Psychology, philosophy, and computation (pp. 301–322)
  10. Heckerman, 1998, Learning in graphical models (pp. 301–354)
  11. Good, 1980, Bayesian statistics (pp. 489–519)
  12. 10.1207/s15516709cog2703_6 · doi:10.1207/s15516709cog2703_6
  13. 10.1037/0033-295X.104.2.367 · doi:10.1037/0033-295x.104.2.367 · Paper page
  14. 10.2307/1130543 · doi:10.2307/1130543 · Paper page
  15. Griffiths, 2003, Neural information processing systems 15 (pp. 11–18)
  16. Tenenbaum, 2003, Proceedings of the 25th Annual Meeting of the Cognitive Science Society (pp. 1152–1157)
  17. Griffiths, 2005, Causes, coincidences, and theories
  18. Friedman, 2000, Proceedings of the 16th Annual Conference on Uncertainty in AI (pp. 201–210)
  19. Tenenbaum, 2003, Advances in neural information processing systems 15 (pp. 35–42)
  20. Mansinghka, 2006, Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence
  21. Marr, 1982, Vision
  22. Minka, 2002, Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence (UAI) (pp. 352–359)
  23. Griffiths, 2005, Infinite latent feature models and the Indian buffet process
  24. Ghahramani, 2004, Advanced lectures on machine learning (pp. 72–122)
  25. 10.1201/9780429258411 · doi:10.1201/9780429258411 · Paper page
  26. Hagmayer, Causal learning: Psychology, philosophy, and computation. (pp. 323–345)
  27. Baker, 2007, Proceedings of the 29th Annual Meeting of the Cognitive Science Society (pp. 779–784)
  28. Danks, 2003, Advances Neural Information Processing Systems 15 (pp. 67–74)
  29. Griffiths, 2002, Proceedings of the Twenty-Fourth Annual Conference of the Cognitive Science Society (pp. 381–386)
  30. Kemp, 2004, Proceedings of the 26th Annual Conference of the Cognitive Science Society (pp. 658–683)
  31. Myung, 2000, Model selection [Special issue], Journal of Mathematical Psychology, 44
  32. 10.1037/0096-3445.115.1.39 · DOI · Paper page
  33. Blei, 2004, Advances in Neural Information Processing Systems 16 (pp. 17–24)

Cited by

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Metadata

Authors
Griffiths, T. L., Kemp, C., & Tenenbaum, J. B.
Venue
The Cambridge Handbook of Computational Psychology
Publisher
Cambridge University Press
Published
2001-01-01
Pages
59-100
DOI
10.1017/cbo9780511816772.006
OpenAlex
W137420995
Cited by
507
References
117
FWCI
0.00
Type
book chapter
Language
en
Open access
green
Licence
www.cambridge.org/core/terms (unspecified, 4173 d)

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CoChat. (2026). Citation check: Bayesian Models of Cognition. https://app.cochat.ai/papers/10.1017/cbo9780511816772.006
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Griffiths, T. L., Kemp, C., & Tenenbaum, J. B. (2001). Bayesian Models of Cognition. The Cambridge Handbook of Computational Psychology. https://doi.org/10.1017/cbo9780511816772.006

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.