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Speed Estimation for Visual Tracking Emerges Dynamically from Nonlinear Frequency Interactions

Meso, Andrew Isaac; Gekas, Nikos; Mamassian, Pascal; Masson, Guillaume S.


Andrew Isaac Meso

Pascal Mamassian

Guillaume S. Masson


Sensing the movement of fast objects within our visual environments is essential for controlling actions. It requires online estimation of motion direction and speed. We probed human speed representation using ocular tracking of stimuli of different statistics. First, we compared ocular responses to single drifting gratings (DGs) with a given set of spatiotemporal frequencies to broadband motion clouds (MCs) of matched mean frequencies. Motion energy distributions of gratings and clouds are point-like, and ellipses oriented along the constant speed axis, respectively. Sampling frequency space, MCs elicited stronger, less variable, and speed-tuned responses. DGs yielded weaker and more frequency-tuned responses. Second, we measured responses to patterns made of two or three components covering a range of orientations within Fourier space. Early tracking initiation of the patterns was best predicted by a linear combination of components before nonlinear interactions emerged to shape later dynamics. Inputs are supralinearly integrated along an iso-velocity line and sublinearly integrated away from it. A dynamical probabilistic model characterizes these interactions as an excitatory pooling along the iso-velocity line and inhibition along the orthogonal “scale” axis. Such crossed patterns of interaction would appropriately integrate or segment moving objects. This study supports the novel idea that speed estimation is better framed as a dynamic channel interaction organized along speed and scale axes.

Journal Article Type Article
Acceptance Date Mar 11, 2022
Online Publication Date Apr 25, 2022
Publication Date 2022-06
Deposit Date May 18, 2022
Publicly Available Date May 19, 2022
Journal eNeuro
Publisher Society for Neuroscience
Peer Reviewed Peer Reviewed
Volume 9
Issue 3
Keywords dynamic nonlinearities, motion clouds, naturalistic stimulation, ocular following, probabilistic modelling, speed estimation
Public URL
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