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<div align="center"><font size=4><b>The UCSD Department of Cognitive
Science is pleased to announce a talk by<br><br>
</font><font size=6>Alan Stocker Ph.D.<br><br>
</font><font size=4>Center for Neural Science<br>
New York University<br><br>
</font><font face="arial" size=4>Friday, March 7, 2008 at 12pm<br>
Cognitive Science Building, room 003<br><br>
<br>
</font><font size=6>"Bayesian Perception"<br><br>
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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).<br><br>
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.<br><br>
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.<br>
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