More is not always better: dissociation between perception and action explained by adaptive gain control


Moving objects generate motion information at different scales, which are processed in the visual system with a bank of spatiotemporal frequency channels. It is not known how the brain pools this information to reconstruct object speed and whether this pooling is generic or adaptive; that is, dependent on the behavioral task. We used rich textured motion stimuli of varying bandwidths to decipher how the human visual motion system computes object speed in different behavioral contexts. We found that, although a simple visuomotor behavior such as short-latency ocular following responses takes advantage of the full distribution of motion signals, perceptual speed discrimination is impaired for stimuli with large bandwidths. Such opposite dependencies can be explained by an adaptive gain control mechanism in which the divisive normalization pool is adjusted to meet the different constraints of perception and action.

Nature Neuroscience


Band-pass motion stimuli for perception and action tasks. (a) In the space representing temporal against spatial frequency, each line going through the origin corresponds to stimuli moving at the same speed. A simple drifting grating is a single point in this space. Our moving texture stimuli had their energy distributed within an ellipse elongated along a given speed line, keeping constant the mean spatial and temporal frequencies. The spatio-temporal bandwidth was manipulated by co-varying Bsf and Btf as illustrated by the (x,y,t) examples. Human performance was measured for two different tasks, run in parallel blocks. (b) For ocular tracking, motion stimuli were presented for a short duration (200ms) in the wake of a centering saccade to control both attention and fixation states. (c) For speed discrimination, test and reference stimuli were presented successively for the same duration and subjects were instructed to indicate whether the test stimulus was perceived as slower or faster than reference.

Laurent U Perrinet
Laurent U Perrinet
Researcher in Computational Neuroscience

My research interests include Machine Learning and computational neuroscience applied to Vision.