Speed Estimation for Visual Tracking Emerges Dynamically from Nonlinear Frequency Interactions

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

Abstract

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.

Citation

Meso, A. I., Gekas, N., Mamassian, P., & Masson, G. S. (2022). Speed Estimation for Visual Tracking Emerges Dynamically from Nonlinear Frequency Interactions. eNeuro, 9(3), https://doi.org/10.1523/ENEURO.0511-21.2022

• 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
• Electronic ISSN: 2373-2822
• Publisher: Society for Neuroscience
• Peer Reviewed: Peer Reviewed
• Volume: 9
• Issue: 3
• DOI: https://doi.org/10.1523/ENEURO.0511-21.2022
• Keywords: dynamic nonlinearities, motion clouds, naturalistic stimulation, ocular following, probabilistic modelling, speed estimation
• Public URL: http://researchrepository.napier.ac.uk/Output/2872539
• Publisher URL: https://www.eneuro.org/content/9/3/ENEURO.0511-21.2022