Measuring speed of moving textures: Different pooling of motion information for human ocular following and perception.
Abstract
To measure speed and direction of moving objects, the cortical motion system pools information across different spatiotemporal channels. One yet unsolved question is to understand how the brain pools this information and whether this pooling is generic or adaptive at the behavioral contexts. Here, we investigate in humans this integration process for two different tasks: psychophysical speed discrimination and ocular following eye movements, which are a probe of early motion detection and integration (Masson & Perrinet, 2011). For both tasks, we used short presentations of ``moving textures’’ stimuli (Schrater et al., 2000) in which the width of the spatial frequency distribution (Bsf) was varied. We found that larger Bsf elicited stronger initial eye velocity during the open-loop part of tracking responses. Moreover, richer stimuli resulted in more accurate and reliable motor responses. By contrast, larger Bsf had a detrimental effect upon speed discrimination performance: speed discrimination thresholds linearly decreased when the width of spatial frequency distribution increased. These opposite results can be explained by a different decoding strategy where speed information is under the control of different gain setting mechanisms. We tested this model by measuring contrast response functions of both ocular following and speed discrimination for each Bsf. We found that varying spatial frequency distribution had opposite effect upon contrast gain control. Increasing Bsf lowered half-saturation contrast for ocular following but increased it for perception. Our results supports the view that speed-based perception and tracking eye movements are under the control of different early decoding mechanism. References Masson, G.S. & Perrinet, L.U. The behavioural receptive field underlying motion integration for primate tracking eye movements. Neurosci. BioBehav. Review 36, 1-25 (2011). Schrater, P.R., Knill, D.C. & Simoncelli, E.P. Mechanism of visual motion detection. Nat. Neurosci. 3, 64-68 (2000).
Laurent U Perrinet
Researcher in Computational Neuroscience
My research interests include Machine Learning and computational neuroscience applied to Vision.
