Burn-in, bias, and the rationality of anchoring

Advances in Neural Information Processing Systems

Volumn 4, Issue , 2012, Pages 2690-2698

  Lieder, Falk ^a   Griffiths, Thomas L ^b   Goodman, Noah D ^c  

^a UNIVERSITY OF ZURICH   (Switzerland)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATE BAYESIAN INFERENCE; APPROXIMATE INFERENCE; COGNITIVE PROCESS MODELS; INFERENCE ALGORITHM; METROPOLIS-HASTINGS ALGORITHM; NUMERICAL ESTIMATION; POSTERIOR DISTRIBUTIONS; QUANTITATIVE PREDICTION;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS; MARKOV PROCESSES; OPTIMIZATION;

INFERENCE ENGINES;

EID: 84877775445     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. A. Tversky and D. Kahneman. Judgment under uncertainty: Heuristics and biases. Science, 185(4157):1124-1131, 1974.
2. T. L. Griffiths, C. Kemp, and J. B. Tenenbaum. Bayesian models of cognition. In R. Sun, editor, The Cambridge handbook of computational psychology, chapter 3, pages 59-100. Cambridge University Press, 2008.
3. D. Marr. Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. W. H. Freeman, 1983.
4. A. N. Sanborn, T. L. Griffiths, and D. J. Navarro. Rational approximations to rational models: Alternative algorithms for category learning. Psychological Review, 117(4):1144-1167, 2010.
5. L. Shi, T. L. Griffiths, N. H. Feldman, and A. N. Sanborn. Exemplar models as a mechanism for performing Bayesian inference. Psychonomic Bulletin & Review, 17(4):443-464, 2010.
6. E. Bonawitz, S. Denison, A. Chen, A. Gopnik, and T. L. Griffiths. A simple sequential algorithm for approximating bayesian inference. Proceedings of the 33rd Annual Conference of the Cognitive Science Society, Austin, TX: Cognitive Science Society, 2011.
7. E. Vul, N. D. Goodman, T. L. Griffiths, and J. B. Tenenbaum. One and done? Optimal decisions from very few samples. Proceedings of the 31st Annual Conference of the Cognitive Science Society, 2009.
8. T. L. Griffiths and J. B. Tenenbaum. Optimal predictions in everyday cognition. Psychological Science, 17(9):767-773, 2006.
9. T. L. Griffiths and J. B. Tenenbaum. Predicting the future as bayesian inference: People combine prior knowledge with observations when estimating duration and extent. Journal of Experimental Psychology: General, 140(4):725-743, 2011.
10. N.D. Goodman, J. B. Tenenbaum, J. Feldman, and T. L. Griffiths. A rational analysis of rulebased concept learning. Cognitive Science, 32(1):108-154, 2008.
11. S. J. Gershman, E. Vul, and J. B. Tenenbaum. Multistability and perceptual inference. Neural Computation, 24(1):1-24, 2011.
12. R. Moreno-Bote, D. C. Knill, and A. Pouget. Bayesian sampling in visual perception. Proceedings of the National Academy of Sciences, 108(30):12491-12496, 2011.
13. L. Buesing, J. Bill, B. Nessler, and W. Maass. Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons. PLoS Computational Biology, 7(11):e1002211+, 2011.
14. D. Pecevski, L. Buesing, and W. Maass. Probabilistic inference in general graphical models through sampling in stochastic networks of spiking neurons. PLoS Computational Biology, 7(12):e1002294+, 2011.
15. E. Horvitz. Reasoning about beliefs and actions under computational resource constraints. In Uncertainty in Artificial Intelligence 3 Annual Conference on Uncertainty in Artificial Intelligence (UAI-87), pages 301-324, Amsterdam, NL, 1987. Elsevier Science.
16. W. K. Hastings. Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57(1):97-109, 1970.
17. N. Epley and T. Gilovich. The anchoring-and-adjustment heuristic. Psychological Science, 17(4):311-318, 2006.
18. K. L. Mengersen and R. L. Tweedie. Rates of convergence of the Hastings and Metropolis algorithms. The Annals of Statistics, 24(1):101-121, 1996.
19. A. E. Raftery and S. Lewis. How many iterations in the Gibbs sampler. In Bayesian Statistics 4, volume 4, pages 763-773, 1992.
20. N. Epley and T. Gilovich. When effortful thinking influences judgmental anchoring: differential effects of forewarning and incentives on self-generated and externally provided anchors. Journal of Behavioral Decision Making, 18(3):199-212, 2005.
21. N. Epley, B. Keysar, L. Van Boven, and T. Gilovich. Perspective taking as egocentric anchoring and adjustment. Journal of Personality and Social Psychology, 87(3):327-339, 2004.
22. E. Vul and H. Pashler. Measuring the crowd within. Psychological Science, 19(7):645-647, 2008.
23. J. Huttenlocher, L. V. Hedges, and J. L. Vevea. Why do categories affect stimulus judgment? Journal of experimental psychology. General, 129(2):220-241, 2000.
24. P. Hemmer and M. Steyvers. A Bayesian account of reconstructive memory. Topics in Cognitive Science, 1(1):189-202, 2009.
25. J. B. Tenenbaum and T. L. Griffiths. The rational basis of representativeness. In Proceedings of the 23rd Annual Conference of the Cognitive Science Society, pages 84-98, 2001.