Constraining a Bayesian model of human visual speed perception
Presented at:
Neural Information Processing Systems (NIPS*04), Vancouver BC, Dec 2004.
Published in:
Advances in Neural Information Processing Systems 17
eds. L. K. Saul, Y. Weiss, and Leon Bottou, pp. 1361-1368, May 2005.
© MIT Press, Cambridge, MA.
It has been demonstrated that basic aspects of human visual motion
perception are qualitatively consistent with a Bayesian estimation
framework, where the prior probability distribution on velocity favors
slow speeds. Here, we present a refined probabilistic model that can
account for the typical trial-to-trial variabilities observed in
psychophysical speed perception experiments. We also show that data
from such experiments can be used to constrain both the likelihood and
prior functions of the model. Specifically, we measured matching
speeds and thresholds in a two-alternative forced choice speed
discrimination task. Parametric fits to the data reveal that the
likelihood function is well approximated by a LogNormal distribution
with a characteristic contrast-dependent variance, and that the prior
distribution on velocity exhibits significantly heavier tails than a
Gaussian, and approximately follows a power-law function.
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