Visual guidance of smooth-pursuit eye movements: sensation, action, and what happens in between.

Howard Hughes Medical Institute, Department of Physiology, and W.M. Keck Foundation Center for Integrative Neuroscience, University of California, San Francisco, San Francisco, CA 94143-0444, USA.
Neuron (Impact Factor: 15.98). 05/2010; 66(4):477-91. DOI: 10.1016/j.neuron.2010.03.027
Source: PubMed

ABSTRACT Smooth-pursuit eye movements transform 100 ms of visual motion into a rapid initiation of smooth eye movement followed by sustained accurate tracking. Both the mean and variation of the visually driven pursuit response can be accounted for by the combination of the mean tuning curves and the correlated noise within the sensory representation of visual motion in extrastriate visual area MT. Sensory-motor and motor circuits have both housekeeping and modulatory functions, implemented in the cerebellum and the smooth eye movement region of the frontal eye fields. The representation of pursuit is quite different in these two regions of the brain, but both regions seem to control pursuit directly with little or no noise added downstream. Finally, pursuit exhibits a number of voluntary characteristics that happen on short timescales. These features make pursuit an excellent exemplar for understanding the general properties of sensory-motor processing in the brain.

  • [Show abstract] [Hide abstract]
    ABSTRACT: Smooth pursuit eye movements are driven by retinal motion and enable us to view moving targets with high acuity. Complicating the generation of these movements is the fact that different eye and head rotations can produce different retinal stimuli but giving rise to identical smooth pursuit trajectories. However, because our eyes accurately pursue targets regardless of eye and head orientation (Blohm and Lefèvre 2010), the brain must somehow take these signals into account. To learn about the neural mechanisms potentially underlying this visual-to-motor transformation, we trained a physiologically-inspired neural network model to combine 2D retinal motion signals with 3D eye and head orientation and velocity signals to generate a spatially correct 3D pursuit command. We then simulated conditions of (1) head roll-induced ocular counter-roll, (2) oblique gaze-induced retinal rotations, (3) eccentric gazes (invoking the half-angle rule) and (4) optokinetic nystagmus to investigate how units in the intermediate layers of the network accounted for different 3D constraints. Simultaneously, we simulated electrophysiological recordings (visual and motor tunings) and microstimulation experiments to quantify the reference frames of signals at each processing stage. We found a gradual retinal-to-intermediate-to-spatial feedforward transformation through the hidden layers. Our model is the first to describe the general 3D transformation for smooth pursuit mediated by eye- and head-dependent gain modulation. Based on several testable experimental predictions, our model provides a mechanism by which the brain could perform the 3D visuomotor transformation for smooth pursuit. Copyright © 2014, Journal of Neurophysiology.
    Journal of Neurophysiology 12/2014; DOI:10.1152/jn.00273.2014 · 3.04 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: People can estimate the current position of an occluded moving target. This is called motion extrapolation, and it has been suggested that the performance in such tasks is mediated by the smooth-pursuit system. Experiment 1 contrasted a standard position extrapolation task with a novel number extrapolation task. In the position extrapolation task, participants saw a horizontally moving target become occluded, and then responded when they thought the target had reached the end of the occluder. Here the stimuli can be tracked with pursuit eye movements. In the number extrapolation task, participants saw a rapid countdown on the screen that disappeared before reaching zero. Participants responded when they thought the hidden counter would have reached zero. Although this stimulus cannot be tracked with the eyes, performance was comparable on both the tasks. The response times were also found to be correlated. Experiments 2 and 3 extended these findings, using extrapolation through color space as well as number space, while Experiment 4 found modest evidence for similarities between color and number extrapolation. Although more research is certainly needed, we propose that a common rate controller guides extrapolation through physical space and feature space. This functions like the velocity store module of the smooth-pursuit system, but with a broader function than previously envisaged.
    Journal of Vision 11/2014; 14(13). DOI:10.1167/14.13.10 · 2.73 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper introduces a new paradigm that allows one to quantify the Bayesian beliefs evidenced by subjects during oculomotor pursuit. Subjects' eye tracking responses to a partially occluded sinusoidal target were recorded non-invasively and averaged. These response averages were then analysed using dynamic causal modelling (DCM). In DCM, observed responses are modelled using biologically plausible generative or forward models-usually biophysical models of neuronal activity. New method Our key innovation is to use a generative model based on a normative (Bayes-optimal) model of active inference to model oculomotor pursuit in terms of subjects' beliefs about how visual targets move and how their oculomotor system responds. Our aim here is to establish the face validity of the approach, by manipulating the content and precision of sensory information-and examining the ensuing changes in the subjects' implicit beliefs. These beliefs are inferred from their eye movements using the normative model. We show that on average, subjects respond to an increase in the 'noise' of target motion by increasing sensory precision in their models of the target trajectory. In other words, they attend more to the sensory attributes of a noisier stimulus. Conversely, subjects only change kinetic parameters in their model but not precision, in response to increased target speed. Using this technique one can estimate the precisions of subjects' hierarchical Bayesian beliefs about target motion. We hope to apply this paradigm to subjects with schizophrenia, whose pursuit abnormalities may result from the abnormal encoding of precision. Copyright © 2015. Published by Elsevier B.V.
    Journal of Neuroscience Methods 01/2015; 242. DOI:10.1016/j.jneumeth.2015.01.003 · 1.96 Impact Factor


1 Download
Available from