Predicting the future as Bayesian inference: People combine prior knowledge with observations when estimating duration and extent

Journal of Experimental Psychology: General

Volumn 140, Issue 4, 2011, Pages 725-743

  Griffiths, Thomas L ^a   Tenenbaum, Joshua B ^b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Bayesian inference; Heuristics; Mathematical modeling; Predicting the future

Indexed keywords

ADULT; ARTICLE; BAYES THEOREM; DECISION MAKING; FORECASTING; HUMAN; KNOWLEDGE; OBSERVATION; PHYSIOLOGY; PSYCHOLOGIC TEST; THEORETICAL MODEL; TIME PERCEPTION;

ADULT; BAYES THEOREM; FORECASTING; HUMANS; JUDGMENT; KNOWLEDGE; MODELS, THEORETICAL; OBSERVATION; PSYCHOLOGICAL TESTS; TIME PERCEPTION; YOUNG ADULT;

EID: 82855178982     PISSN: 00963445     EISSN: None     Source Type: Journal    
DOI: 10.1037/a0024899     Document Type: Article

References (36)

1. Allan L.G. The perception of time. Perception & Psychophysics 1979, 26:340-354. 10.3758/BF03204158.
2. Anderson J.R. The adaptive character of thought 1990, Erlbaum, Hillsdale, NJ.
3. Anderson J.R. The adaptive nature of human categorization. Psychological Review 1991, 98:409-429. 10.1037/0033-295X.98.3.409.
4. Anderson J.R., Schooler L.J. Reflections of the environment in memory. Psychological Science 1991, 2:396-408. 10.1111/j.1467-9280.1991.tb00174.x.
5. Barsalou L.W., Huttenlocher J., Lamberts K. Basing categorization on individuals and events. Cognitive Psychology 1998, 36:203-272. 10.1006/cogp.1998.0687.
6. Bernardo J.M., Smith A.F.M. Bayesian theory 1994, Wiley, New York, NY.
7. Bostrom N. Anthropic bias: Observation selection effects in science and philosophy 2002, Routledge, New York, NY.
8. Buch P. Future prospects discussed. Nature 1994, March 10, 368:107-108. 10.1038/368107b0.
9. Eagleman D.M., Holcombe A.O. Causality and the perception of time. Trends in Cognitive Sciences 2002, 6:323-325. 10.1016/S1364-6613(02)01945-9.
10. Fiedler K., Juslin P. Information sampling and adaptive cognition 2006, Cambridge University Press, Cambridge, England.
11. Gigerenzer G., Brighton H. Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science 2009, 1:107-143. 10.1111/j.1756-8765.2008.01006.x.
12. Goodman N. Fact, fiction, and forecast 1955, Harvard University Press, Cambridge, MA.
13. Gott J.R. Implications of the Copernican principle for our future prospects. Nature 1993, May 27, 363:315-319. 10.1038/363315a0.
14. Gott J.R. Future prospects discussed. Nature 1994, March 10, 368:108. 10.1038/368108a0.
15. Griffiths T.L., Tenenbaum J.B. Optimal predictions in everyday cognition. Psychological Science 2006, 17:767-773. 10.1111/j.1467-9280.2006.01780.x.
16. Hertwig R., Barron G., Weber E.U., Erev I. Decisions from experience and the effect of rare events in risky choice. Psychological Science 2004, 15:534-539. 10.1111/j.0956-7976.2004.00715.x.
17. Huettel S.A., Mack P.B., McCarthy G. Perceiving patterns in random series: Dynamic processing of sequence in prefrontal cortex. Nature Neuroscience 2002, 5:485-490.
18. Jaynes E.T. Probability theory: The logic of science 1994, http://omega.math.albany.edu:8008/JaynesBook.html, Fragmentary ed.
19. Jaynes E.T. Probability theory: The logic of science 2003, Cambridge University Press., Cambridge, England. 10.1017/CBO9780511790423.
20. Jeffreys H. Theory of probability 1961, Oxford University Press, Oxford, England.
21. Kruschke J.K. Locally Bayesian learning with applications to retrospective revaluation and highlighting. Psychological Review 2006, 113:677-699. 10.1037/0033-295X.113.4.677.
22. Landsberg P.T., Dewynne J.N., Please C.P. Rise and fall. Nature 1993, September 30, 365:384. 10.1038/365384e0.
23. Lewandowsky S., Griffiths T.L., Kalish M.L. The wisdom of individuals: Exploring people's knowledge about everyday events using iterated learning. Cognitive Science: A Multidisciplinary Journal 2009, 33:969-998. 10.1111/j.1551-6709.2009.01045.x.
24. Marr D. Vision 1982, Freeman, San Francisco, CA.
25. Mozer M.C., Pashler H., Homaei H. Optimal predictions in everyday cognition: The wisdom of individuals or crowds?. Cognitive Science: A Multidisciplinary Journal 2008, 32:1133-1147. 10.1080/03640210802353016.
26. Press S.J. Bayesian statistics: Principles, models, and applications 1989, Wiley, New York, NY.
27. Ross L., Nisbett R.E. The person and the situation 1991, McGraw-Hill, New York, NY.
28. Shepard R.N. Toward a universal law of generalization for psychological science. Science 1987, September 11, 237:1317-1323. 10.1126/science.3629243.
29. Shi L., Griffiths T.L., Feldman N.H., Sanborn A.N. Exemplar models as a mechanism for performing Bayesian inference. Psychonomic Bulletin & Review 2010, 17:443-464. 10.3758/PBR.17.4.443.
30. Sternberg R.J., Kalmar D.A. When will the milk spoil? Everyday induction in human intelligence. Intelligence 1997, 25:185-203. 10.1016/S0160-2896(97)90042-8.
31. Tenenbaum, J. B. (1999). A Bayesian framework for concept learning. Unpublished doctoral dissertation. Massachusetts Institute of Technology, Cambridge, MA.
32. Tenenbaum J.B., Griffiths T.L. Structure learning in human causal induction. Advances in neural information processing systems 2001, 13:59-65. MIT Press, Cambridge, MA. T.K. Leen, T.G. Dietterich, V. Tresp (Eds.).
33. Tversky A., Kahneman D. Judgment under uncertainty: Heuristics and biases. Science 1974, September 27, 185:1124-1131. 10.1126/science.185.4157.1124.
34. Vul E., Goodman N.D., Griffiths T.L., Tenenbaum J.B. One and done? Optimal decisions from very few samples. CogSci 2009 Proceedings 2009, 148-153. Cognitive Science Society, Wheat Ridge, CO. http://csjarchive.cogsci.rpi.edu/Proceedings/2009/papers/28/paper28.pdf, N. Taatgen, H. van Rijn (Eds.).
35. Xu F., Tenenbaum J.B. Sensitivity to sampling in Bayesian word learning. Developmental Science 2007, 10:288-297. 10.1111/j.1467-7687.2007.00590.x.
36. Xu F., Tenenbaum J.B. Word learning as Bayesian inference. Psychological Review 2007, 114:245-272. 10.1037/0033-295X.114.2.245.