Voluntary tracking the moving clouds : Effects of speed variability on human smooth pursuit


The properties of motion processing for driving smooth eye movements have bee investigated using simple, artificial stimuli such as gratings, small dots or random dot patterns. Motion processing in the context of complex, natural images is less known. We have previously investigated the human ocular following responses to a novel class of random texture stimuli of parameterized naturalistic statistics: the Motion Clouds. In Fourier space, these dynamical textures are designed with a log normal distribution of spatial frequencies power multiplied by a pink noise power spectral density that reduces the high frequency contents of the stimulus (Sanz-Leon et al. 2011). We have previously shown that the precision of reflexive tracking increases with the spatial frequency bandwidth of large (> 30 deg diameter) patterns (i.e. the width of the spatial frequency distribution around a given mean spatial frequency; Simoncini et al. 2012). Now, we extend this approach to voluntary tracking and focused on the effects of spatial frequency bandwidth upon the initial phase of smooth pursuit eye movements. Participants were instructed to pursue a large patch of moving clouds (mean speeds: 5, 10 or 20 deg/s) embedded within a smoothing Gaussian window of standard deviation 5 deg. The motion stimuli were presented with four different spatial frequency bandwidths and two different mean spatial frequencies (0.3 and 1 cpd). We observed that smaller bandwidth textures exhibit a stronger spectral energy within the low spatial frequency range (below 1cpd), yielding to shorter latency of smooth pursuit eye movements. A weak and less consistent effect was found on initial eye acceleration, contrary what was previously observed with OFR. After 400ms, the steady-state tracking velocity matched the mean visual motion speed and pursuit performance was comparable with that observed with a control, small dot motion. Motion Clouds offer an efficient tool to probe the optimal window of visibility for human smooth pursuit through the manipulation of both the mean and the variability of spatial frequency.

GDR Vision, Lille, 2017
Kiana Mansour-Pour
Kiana Mansour-Pour
Phd in Computational Neuroscience

During my PhD, I focused on smooth eye movements.

Laurent U Perrinet
Laurent U Perrinet
Researcher in Computational Neuroscience

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