Compound eyes''

Visual motion processing and human tracking behavior

The accurate visual tracking of a moving object is a human fundamental skill that allows to reduce the relative slip and instability of the object's image on the retina, thus granting a stable, high-quality vision. In order to optimize tracking performance across time, a quick estimate of the object's global motion properties needs to be fed to the oculomotor system and dynamically updated. Concurrently, performance can be greatly improved in terms of latency and accuracy by taking into account predictive cues, especially under variable conditions of visibility and in presence of ambiguous retinal information. Here, we review several recent studies focusing on the integration of retinal and extra-retinal information for the control of human smooth pursuit.

By dynamically probing the tracking performance with well established paradigms in the visual perception and oculomotor literature we provide the basis to test theoretical hypotheses within the framework of dynamic probabilistic inference. We will in particular present the applications of these results in light of state-of-the-art computer vision algorithms.

A lateral view of the macaque cortex. The neural network corresponding to our hierarchical Bayesian model of smooth pursuit is made of three main cortico-cortical loop. The first loop between primary visual cortex (V1) and the medio-temporal (MT) computes image motion and infers the optimal low-level solution for object motion direction and speed. Its main output is the medio-superior temporal (MST) area that acts as a gear between the sensory loop and the object motion computation loop. Retinal and extra-retinal signals are integrated in both MST and FEF areas. Such dynamical integration computes the perceived trajectory of the moving object and implements an online prediction that can be used on the course of a tracking eye movement to compensate for target perturbation such as transient blanking. FEF and MST areas signals are sent to the supplementary eye field (SEF) and the interconnected prefrontal cortical areas. This third loop can elaborate a motion memory of the target trajectory and is interfaced with higher cognitive processes such as cue instruction or reinforcement learning. It also implements offline predictions that can be used across trials, in particular to drive anticipatory responses to highly predictable targets.''A lateral view of the macaque cortex. The neural network corresponding to our hierarchical Bayesian model of smooth pursuit is made of three main cortico-cortical loop. The first loop between primary visual cortex (V1) and the medio-temporal (MT) computes image motion and infers the optimal low-level solution for object motion direction and speed. Its main output is the medio-superior temporal (MST) area that acts as a gear between the sensory loop and the object motion computation loop. Retinal and extra-retinal signals are integrated in both MST and FEF areas. Such dynamical integration computes the perceived trajectory of the moving object and implements an online prediction that can be used on the course of a tracking eye movement to compensate for target perturbation such as transient blanking. FEF and MST areas signals are sent to the supplementary eye field (SEF) and the interconnected prefrontal cortical areas. This third loop can elaborate a motion memory of the target trajectory and is interfaced with higher cognitive processes such as cue instruction or reinforcement learning. It also implements offline predictions that can be used across trials, in particular to drive anticipatory responses to highly predictable targets.

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All material (c) L. Perrinet. Please check the copyright notice.


TagYear15 TagPublicationsArticles TagMotion TagBicv

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