Year: 2005
Type: conference-paper
Abstract: The misjudgement of tilt in images lies at the heart of entertaining vi-sual illusions and rigorous perceptual psychophysics. A wealth of find-ings 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 percep-tual domains. Perceptual anomalies and illusions, such as the misjudgements of motion and tilt evident in so many psychophysical experiments, have intrigued researchers for decades.1–3 A Bayesian view4–8 has been particularly influential in models of motion processing, treating such anomalies as the normative product of prior information (often statistically codify-
Source: MPG.PuRe (Max Planck Society)
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Funder: Howard Hughes Medical Institute
OpenAlex ID: W2126399818