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Speed Estimation for Visual Tracking Emerges Dynamically from Nonlinear Frequency Interactions

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

Original languageEnglish
Article numberENEURO.0511-21.2022
JournaleNeuro
Volume9
Issue number3
DOIs
Published27 Apr 2022

Bibliographical note

Funding Information: This work was supported by Agence Nationale de la Recherche Grants ANR-BLAN-13-SHS2-0006 (SPEED; to G.S.M. and P.M.) and ANR-18-CE37-0019 (PredictEye) and ANR-20-CRCNS-001 (PrioSens; to G.S.M.). This work was also supported by the Agence Nationale de la Recherche Grant ANR-17-EURE-0017 (FrontCog; to P.M.). G.S.M. is also supported by the Fondation pour la Recherche Médicale (Equipe FRM 2018). Publisher Copyright: © 2022 Meso et al.

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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.

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