Noise characteristics and prior expectations in human visual speed perception.
Human visual speed perception is qualitatively consistent with a Bayesian observer that optimally combines noisy measurements with a prior preference for lower speeds. Quantitative validation of this model, however, is difficult because the precise noise characteristics and prior expectations are unknown. Here, we present an augmented observer model that accounts for the variability of subjective responses in a speed discrimination task. This allowed us to infer the shape of the prior probability as well as the internal noise characteristics directly from psychophysical data. For all subjects, we found that the fitted model provides an accurate description of the data across a wide range of stimulus parameters. The inferred prior distribution shows significantly heavier tails than a Gaussian, and the amplitude of the internal noise is approximately proportional to stimulus speed and depends inversely on stimulus contrast. The framework is general and should prove applicable to other experiments and perceptual modalities.
Howard Hughes Medical Institute, Center for Neural Science and Courant Institute of Mathematical Sciences, New York University, 4 Washington Place Rm 809, New York, New York 10003, USA. alan.stocker@nyu.edu
Alan A
AA
Stocker
Eero P
EP
Simoncelli
D001499:Bayes Theorem; D005260:Female; D006801:Humans; D008297:Male; D008433:Mathematics; D008954:Models, Biological; D009039:Motion Perception / Q000502:physiology*; D015203:Reproducibility of Results; D017603:Signal Detection, Psychological; D014796:Visual Perception / Q000502:physiology*