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 language | English (US) |
|---|---|
| Title of host publication | Advances in Neural Information Processing Systems |
| Pages | 1201-1208 |
| Number of pages | 8 |
| State | Published - 2005 |
| Externally published | Yes |
| Event | 2005 Annual Conference on Neural Information Processing Systems, NIPS 2005 - Vancouver, BC, Canada Duration: Dec 5 2005 → Dec 8 2005 |
Other
| Other | 2005 Annual Conference on Neural Information Processing Systems, NIPS 2005 |
|---|---|
| Country | Canada |
| City | Vancouver, BC |
| Period | 12/5/05 → 12/8/05 |
ASJC Scopus subject areas
- Computer Networks and Communications
- Information Systems
- Signal Processing
Cite this
A Bayesian framework for tilt perception and confidence. / Schwartz, Odelia; Sejnowski, Terrence J.; Dayan, Peter.
Advances in Neural Information Processing Systems. 2005. p. 1201-1208.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A Bayesian framework for tilt perception and confidence
AU - Schwartz, Odelia
AU - Sejnowski, Terrence J.
AU - Dayan, Peter
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84864039612&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84864039612&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84864039612
SN - 9780262232531
SP - 1201
EP - 1208
BT - Advances in Neural Information Processing Systems
ER -