Abstract
Humans are able to interact successfully with moving objects in our dynamic world and the visual system effi ciently performs the motion computation that makes this possible. Object speed and direction are estimated following the integration of information across cortical motion sensitive channels. Speed estimation along this system is not fully understood, particularly the mapping function between the actual speed of viewed objects and that perceived by observers, a question we address in this work. It has been demonstrated that perceived speed is profoundly influenced by object contrast, spatial frequency, stimulus complexity and frequency bandwidth. In a 2 interval forced choice speed discrimination task, we present a random phase textured motion stimulus to probe small shifts in perceived speed measured using fi xed stimulus sets as reference scales while mean spatial frequency and bandwidths serve as the dependent variable in a probe. The presentations are short (200ms). Using a scale of narrowband stimuli (0.2 octaves), we measured a shift in perceived speed; higher frequencies are seen as faster moving than lower ones. On the scale of broader bandwidth (1 octave), this difference across frequency was reduced and perceived speed seems to converge on a slower representation. From these results we estimated this mapping between perceived and veridical stimulus speeds. In direct comparisons, the relative speed is faster for high frequencies and increases in bandwidth make stimuli appear slower. During this early computation, when presented with a random phase stimulus it appears that the visual systems makes assumptions about expected speeds based on the richness of the frequency content and the veridical speed is not explicitly computed. In this first 200ms, the perceptual system perhaps underestimates some speeds in an optimal response for initially stabilizing the scene. Acknowledgement: CNRS & Brainscales FP7
Andrew Isaac Meso
Lecturer, King’s College London (IOPPN).
Laurent U Perrinet
Researcher in Computational Neuroscience
My research interests include Machine Learning and computational neuroscience applied to Vision.