title: Contrast dependency and prior expectations in human speed perception.
abstract: The perceived speed of moving objects has long been known to depend on image contrast. Lowering the contrast of first-order motion stimuli typically decreases perceived speed - the well-known "Thompson effect". It has been suggested that contrast-dependent biases are the result of optimal inference by the visual system, whereby unreliable sensory information is combined with prior beliefs. The Thompson effect is thought to result from the prior belief that objects move slowly (in Bayesian terminology, a "slow speed prior"). However, there is some evidence that the Thompson effect is attenuated or even reversed at higher speeds. Does the effect of contrast on perceived speed depend on absolute speed and what does this imply for Bayesian models with a slow speed prior? We asked subjects to compare the speeds of simultaneously presented drifting gratings of different contrasts. At low contrasts (3-15%), we found that the Thompson effect was attenuated at high speeds: at 8 and 12deg/s, perceived speed increased less with contrast than at 1 and 4deg/s; however, at higher contrasts (15-95%), the situation was reversed. A semi-parametric Bayesian model was used to extract the subjects' speed priors and was subsequently improved by combining it with a model of speed tuning. These novel findings regarding the dual, contrast-dependent effect of high speeds help reconcile existing conflicting literature and suggest that physiologically plausible mechanisms of representation of speed in the visual cortex may need to be incorporated into Bayesian models to account for certain subtleties of human speed perception.
authors: Institute for Adaptive and Neural Computation, University of Edinburgh, UK. Electronic address: g.sotiropoulos@sms.ed.ac.uk.
authors: Grigorios
authors: G
authors: Sotiropoulos
authors: Department of Psychology, University of California, Riverside, USA. Electronic address: aseitz@ucr.edu.
authors: Aaron R
authors: AR
authors: Seitz
authors: Institute for Adaptive and Neural Computation, University of Edinburgh, UK. Electronic address: pseries@inf.ed.ac.uk.
authors: Peggy
authors: P
authors: Seriès
mesh_terms: D000328:Adult; D000704:Analysis of Variance; D001499:Bayes Theorem; D015350:Contrast Sensitivity; D006801:Humans; D015233:Models, Statistical; D009039:Motion Perception; D010775:Photic Stimulation / Q000379:methods; D011601:Psychophysics
keywords: Bayesian models; Contrast; Motion perception; Speed prior; Visual psychophysics