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      Predictive Smooth Pursuit Eye Movements

      1 , 1 , 1 , 2 , 1

      Annual Review of Vision Science

      Annual Reviews

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          Abstract

          Smooth pursuit eye movements maintain the line of sight on smoothly moving targets. Although often studied as a response to sensory motion, pursuit anticipates changes in motion trajectories, thus reducing harmful consequences due to sensorimotor processing delays. Evidence for predictive pursuit includes ( a) anticipatory smooth eye movements (ASEM) in the direction of expected future target motion that can be evoked by perceptual cues or by memory for recent motion, ( b) pursuit during periods of target occlusion, and ( c) improved accuracy of pursuit with self-generated or biologically realistic target motions. Predictive pursuit has been linked to neural activity in the frontal cortex and in sensory motion areas. As behavioral and neural evidence for predictive pursuit grows and statistically based models augment or replace linear systems approaches, pursuit is being regarded less as a reaction to immediate sensory motion and more as a predictive response, with retinal motion serving as one of a number of contributing cues.

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          Most cited references136

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          Bayesian integration in sensorimotor learning.

          When we learn a new motor skill, such as playing an approaching tennis ball, both our sensors and the task possess variability. Our sensors provide imperfect information about the ball's velocity, so we can only estimate it. Combining information from multiple modalities can reduce the error in this estimate. On a longer time scale, not all velocities are a priori equally probable, and over the course of a match there will be a probability distribution of velocities. According to bayesian theory, an optimal estimate results from combining information about the distribution of velocities-the prior-with evidence from sensory feedback. As uncertainty increases, when playing in fog or at dusk, the system should increasingly rely on prior knowledge. To use a bayesian strategy, the brain would need to represent the prior distribution and the level of uncertainty in the sensory feedback. Here we control the statistical variations of a new sensorimotor task and manipulate the uncertainty of the sensory feedback. We show that subjects internally represent both the statistical distribution of the task and their sensory uncertainty, combining them in a manner consistent with a performance-optimizing bayesian process. The central nervous system therefore employs probabilistic models during sensorimotor learning.
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            Noise characteristics and prior expectations in human visual speed perception.

            Human visual speed perception is qualitatively consistent with a Bayesian observer that optimally combines noisy measurements with a prior preference for lower speeds. Quantitative validation of this model, however, is difficult because the precise noise characteristics and prior expectations are unknown. Here, we present an augmented observer model that accounts for the variability of subjective responses in a speed discrimination task. This allowed us to infer the shape of the prior probability as well as the internal noise characteristics directly from psychophysical data. For all subjects, we found that the fitted model provides an accurate description of the data across a wide range of stimulus parameters. The inferred prior distribution shows significantly heavier tails than a Gaussian, and the amplitude of the internal noise is approximately proportional to stimulus speed and depends inversely on stimulus contrast. The framework is general and should prove applicable to other experiments and perceptual modalities.
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              Attentional modulation of visual motion processing in cortical areas MT and MST.

              The visual system is constantly inundated with information received by the eyes, only a fraction of which seems to reach visual awareness. This selection process is one of the functions ascribed to visual attention. Although many studies have investigated the role of attention in shaping neuronal representations in the visual cortex, few have focused on attentional modulation of neuronal signals related to visual motion. Here we report that the responses of direction-selective neurons in monkey visual cortex are greatly influenced by attention, and that this modulation occurs as early in the cortical hierarchy as the level of the middle temporal visual area (MT). Our finding demonstrates a stronger and earlier influence of attention on motion processing along the dorsal visual pathway than previously recognized.
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                Author and article information

                Journal
                Annual Review of Vision Science
                Annu. Rev. Vis. Sci.
                Annual Reviews
                2374-4642
                2374-4650
                September 15 2019
                September 15 2019
                : 5
                : 1
                : 223-246
                Affiliations
                [1 ]Department of Psychology, Rutgers University, Piscataway, New Jersey 08854, USA;, ,
                [2 ]Current affiliation: Department of Psychology, State University of New York, College at Oneonta, Oneonta, New York 13820, USA;
                Article
                10.1146/annurev-vision-091718-014901
                0182f955-4dea-4dbf-b1c7-b8d95e850822
                © 2019

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