[lingtalks] Alan Stocker Talk, Friday March 7 at 12pm

[Steven Ford]

The UCSD Department of Cognitive Science is pleased to announce a talk by

Alan Stocker Ph.D.

Center for Neural Science
New York University

Friday, March 7, 2008 at 12pm
Cognitive Science Building, room 003


"Bayesian Perception"


Generating a sensible and stable percept of the world is crucial. 
Ambiguities, as well as noise and other sensory limitations make this a 
hard computational problem. Yet evolution presses for optimal solutions, 
giving rise to the hypothesis that perception is the process of optimal 
statistical inference (combining noisy sensory evidence with prior 
assumptions about the world).

Based on this hypothesis, I will formulate a Bayesian observer model for 
human visual motion perception and its dependency on stimulus contrast. The 
model well accounts for the average bias and trial-to-trial variability in 
subjects' perceived speed. But more importantly, it also allows us to 
reverse-engineer the exact form of the subjects' prior assumptions and 
noise characteristics from the perceptual data. Such quantitative 
characterization is critical for validating the Bayesian hypothesis. I will 
present recent results in which the extracted prior and noise 
characteristics are used to predict subjects' perception in an entirely 
different psychophysical motion experiment.

Finally, I will address some limitations of the Bayesian modeling approach 
that are revealed in recently reported psychophysical experiments. I will 
show that human subjects exhibit a strong tendency to abandon the optimal 
Bayesian solution in order to maintain a consistent assessment of the 
sensory evidence. Interestingly, this behavior parallels human avoidance of 
cognitive dissonance, suggesting functional similarities between low-level 
perception and cognition.
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