### Constraining a Bayesian Model of Human Visual Speed Perception

**Alan A Stocker** and

Eero P Simoncelli
NIPS Advances in Neural Information Processing Systems 17, Vancouver Canada,
December 2004, MIT Press, p. 1361-1368

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.

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#### related publications:

- this paper is a predecessor of the extended and refined journal
version NN 2006.