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  A Bayesian framework for tilt perception and confidence

Schwartz, O., Sejnowski, T., & Dayan, P. (2006). A Bayesian framework for tilt perception and confidence. In Y. Weiss, B. Schölkopf, & J. Platt (Eds.), Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference (pp. 1201-1208). Cambridge, MA, USA: MIT Press.

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 Creators:
Schwartz, O, Author
Sejnowski, TR, Author
Dayan, P1, Author           
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: The misjudgement of tilt in images lies at the heart of entertaining visual illusions and rigorous perceptual psychophysics. A wealth of findings has attracted many mechanistic models, but few clear computational principles. We adopt a Bayesian approach to perceptual tilt estimation, showing how a smoothness prior offers a powerful way of addressing much confusing data. In particular, we faithfully model recent results showing that confidence in estimation can be systematically affected by the same aspects of images that affect bias. Confidence is central to Bayesian modeling approaches, and is applicable in many other perceptual domains.

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 Dates: 2006-05
 Publication Status: Issued
 Pages: -
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Title: Nineteenth Annual Conference on Neural Information Processing Systems (NIPS 2005)
Place of Event: Vancouver, BC, Canada
Start-/End Date: 2005-12-05 - 2005-12-08

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Title: Advances in Neural Information Processing Systems 18: Proceedings of the 2005 Conference
Source Genre: Proceedings
 Creator(s):
Weiss, Y, Editor
Schölkopf, B1, Editor           
Platt, J, Editor
Affiliations:
1 Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794            
Publ. Info: Cambridge, MA, USA : MIT Press
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1201 - 1208 Identifier: ISBN: 0-262-23253-7