Cortical pooling algorithms for judging global motion direction

Physiological studies suggest that decision networks read from the neural representation in the middle temporal area to determine the perceived direction of visual motion, whereas psychophysical studies tend to characterize motion perception in terms of the statistical properties of stimuli. To reconcile these different approaches, we examined whether estimating the central tendency of the physical direction of global motion was a better indicator of perceived direction than algorithms (e.g., maximum likelihood) that read from directionally tuned mechanisms near the end of the motion pathway. The task of human observers was to discriminate the global direction of random dot kinematograms composed of asymmetrical distributions of local directions with distinct measures of central tendency. None of the statistical measures of image direction central tendency provided consistently accurate predictions of perceived global motion direction. However, regardless of the local composition of motion directions, a maximum-likelihood decoder produced global motion estimates commensurate with the psychophysical data. Our results suggest that mechanism-based, read-out algorithms offer a more accurate and robust guide to human motion perception than any stimulus-based, statistical estimate of central tendency.