Probabilistic models of cognition: Conceptual foundations

Trends in Cognitive Sciences

Volumn 10, Issue 7, 2006, Pages 287-291

  Chater, Nick ^a   Tenenbaum, Joshua B ^b   Yuille, Alan ^c  

^a UNIVERSITY COLLEGE LONDON   (United Kingdom)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COGNITION; COMPUTER; EDITORIAL; MATHEMATICS; PROBABILITY MODEL; PSYCHOLOGY; STATISTICAL MODEL;

ALGORITHMS; BRAIN; COGNITION; HUMANS; MODELS, STATISTICAL; NEURAL NETWORKS (COMPUTER); PROBABILITY THEORY;

EID: 33746263998     PISSN: 13646613     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.tics.2006.05.007     Document Type: Editorial

References (51)

1. Bernoulli, J. (1713) Ars conjectandi, Thurnisiorum, Basel
2. In: Kahneman D., and Tversky A. (Eds). Choices, Values, and Frames (2000), Cambridge University Press
3. Yuille, A. and Kersten, D. (2006) Vision as Bayesian inference: analysis by synthesis? Trends Cogn. Sci. DOI:10.1016/j.tics.2006.05.002
4. Grenander U. General Pattern Theory: A Mathematical Study of Regular Structures (1993), Oxford University Press
5. Pearl J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (1988), Morgan-Kaufman
6. Shepard R.N. Towards a universal law of generalization for psychological science. Science 237 (1987) 1317-1323
7. Anderson J.R. The Adaptive Character of Thought (1990), Erlbaum
8. Kersten D., and Yuille A. Bayesian models of object perception. Curr. Opin. Neurobiol. 13 (2003) 150-158
9. Weiss Y., et al. Motion illusions as optimal percepts. Nat. Neurosci. 5 (2002) 598-604
10. Zhu S.C. Embedding Gestalt laws in Markov random fields. IEEE Trans. Pattern Anal. Mach. Intell. 21 (1999) 1170-1187
11. In: Shipley T.F., and Kellman P.J. (Eds). From Fragments to Objects: Segmentation and Grouping in Vision (2001), Elsevier
12. Chater N. Reconciling simplicity and likelihood principles in perceptual organization. Psychol. Rev. 103 (1996) 566-581
13. Geman S. Composition systems. Q. Appl. Math. LX (2002) 707-736
14. Tu Z., et al. Image parsing: unifying segmentation, detection, and object recognition. Int. J. Comput. Vis. 63 (2005) 113-140
15. Manning C., and Schütze H. Foundations of Statistical Natural Language Processing (2000), MIT Press
16. Jurafsky D. Probabilistic modelling in psycholinguistics: linguistic comprehension and production. In: Bod R., et al. (Ed). Probabilistic Linguistics (2003), MIT Press
17. Chater, N. and Manning, C.D. (2006) Probabilistic models of language processing and acquisition. Trends Cogn. Sci. DOI:10.1016/j.tics.2006.05.006
18. Ernst M.O., and Banks M.S. Humans integrate visual and haptic information in a statistically optimal fashion. Nature 415 (2002) 429-433
19. Clark J.J., and Yuille A.L. Data Fusion for Sensory Information Processing Systems (1990), Kluwer Academic Publishers
20. Stankiewicz, B.J. et al. Lost in virtual space: human and ideal wayfinding behavior. J. Exp. Psychol. Hum. Percept. Perform. (in press)
21. Kaelbling L., et al. Planning and acting in partially observable stochastic domains. Artif. Intell. (1998) 101
22. Pearl J. Causality: Models, Reasoning and Inference (2000), Cambridge University Press
23. Cheng P.W. From covariation to causation: a causal power theory. Psychol. Rev. 104 (1997) 367-405
24. Tenenbaum, J.B. and Griffiths, T.L. (2001) Structure learning in human causal induction. In Advances in Neural Information Processing Systems Vol. 13. (Leen, T. et al., eds), pp. 59-65, MIT Press
25. Griffiths T.L., and Tenenbaum J.B. Structure and strength in causal induction. Cogn. Psychol. 51 (2005) 334-384
26. Steyvers M., et al. Inferring causal networks through observations and interventions. Cogn. Sci. 27 (2003) 453-489
27. Gopnik A., et al. A theory of causal learning in children: causal maps and Bayes nets. Psychol. Rev. 111 (2004) 3-32
28. Tenenbaum J.B., and Xu F. Word learning as Bayesian inference. In: Gleitman L.R., and Joshi A.K. (Eds). Proc. 22nd Annu. Conf. Cogn. Sci. Soc. (2000), Erlbaum 517-522
29. Shiffrin R.M., and Steyvers M. A model for recognition memory: REM: Retrieving effectively from memory. Psychon. Bull. Rev. 4 (1997) 145-166
30. Steyvers, M. et al. (2006) Probabilistic inference in human semantic memory. Trends Cogn. Sci. DOI:10.1016/j.tics.2006.05.005
31. Tenenbaum J.B. Bayesian modeling of human concept learning. In: Kearns M., et al. (Ed). Advances in Neural Information Processing Systems Vol. 11 (1999), MIT Press 59-68
32. Tenenbaum J.B., and Griffiths T.L. Generalization, similarity, and Bayesian inference. Behav. Brain Sci. 24 (2001) 629-641
33. Chater N., and Oaksford M. The probability heuristics model of syllogistic reasoning. Cogn. Psychol. 38 (1999) 191-258
34. Krauss S., et al. Simplifying Bayesian inference: the general case. In: Magnani L., et al. (Ed). Model-based Reasoning in Scientific Discovery (1999), Kluwer Academic/Plenum Press 165-179
35. Coughlan J.M., and Yuille A.L. Bayesian A* tree search with expected O(N) node expansions: applications to road tracking. Neural Comput. 14 (2002) 1929-1958
36. Danks, D. et al. (2003) Dynamical causal learning, Advances in Neural Information Processing Systems Vol. 15. (Becker, S. et al., eds), pp. 67-74, MIT Press
37. Danks D. Equilibria of the Rescorla-Wagner model. J. Math. Psychol. 47 (2003) 109-121
38. Yuille A.L. The Rescorla-Wagner algorithm and maximum likelihood estimation of causal parameters. Adv. Neural Inf. Process. Syst. 17 (2004) 1585-1592
39. Dayan P., et al. Learning and selective attention. Nat. Neurosci. 3 (2000) 1218-1223
40. Marr D. Vision (1982), W.H. Freeman
41. Dayan P., and Abbott L.F. Theoretical Neuroscience: Computational and Mathematical Modelling of Neural Systems (2001), MIT Press
42. Rieke F., et al. Spikes (1999), MIT Press
43. Shadlen, M.N. and Gold, J.I. (2004) The neurophysiology of decision-making as a window on cognition. In The Cognitive Neurosciences, 3rd edn (Gazzaniga, M.S., ed.), pp. 1229-1241, MIT Press
44. Hájek, A. (2003) Interpretations of probability. In The Stanford Encyclopedia of Philosophy (Zalta, E.N., ed.), http://plato.stanford.edu/archives/sum2003/entries/probability-interpret/
45. Körding, K.P. and Wolpert, D.M. (2006) Bayesian decision theory in sensorimotor control. Trends Cogn. Sci. DOI:10.1016/j.tics.2006.05.003
46. Courville, A.C. et al. (2006) Bayesian theories of conditioning in a changing world. Trends Cogn. Sci. DOI:10.1016/j.tics.2006.05.004
47. Tenenbaum, J.B. et al. (2006) Theory-based Bayesian models of inductive learning and reasoning. Trends Cogn. Sci. DOI:10.1016/j.tics.2006.05.009
48. Stewart, N. et al. Decision by sampling. Cogn. Psychol. (in press)
49. Krynski T.R., and Tenenbaum J.B. The role of causal models in statistical reasoning. In: Alterman R., and Kirsh D. (Eds). Proc. 25th Annu. Conf. Cogn. Sci. Soc. (2003), Erlbaum 693-698
50. Sloman S., and Lagnado D. Do we 'do'?. Cogn. Sci. 29 (2004) 5-39
51. Griffiths, T.L. and Tenenbaum, J.B. Optimal predictions in everyday cognition. Psychol. Sci. (in press)