A Bayesian framework for tilt perception and confidence

Odelia Schwartz, Terrence J. Sejnowski, Peter Dayan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 18 - Proceedings of the 2005 Conference
Pages1201-1208
Number of pages8
StatePublished - 2005
Externally publishedYes
Event2005 Annual Conference on Neural Information Processing Systems, NIPS 2005 - Vancouver, BC, Canada
Duration: Dec 5 2005Dec 8 2005

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258

Other

Other2005 Annual Conference on Neural Information Processing Systems, NIPS 2005
CountryCanada
CityVancouver, BC
Period12/5/0512/8/05

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing

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