scholarly journals Gain Control in Human Smooth-Pursuit Eye Movements

2002 ◽  
Vol 87 (6) ◽  
pp. 2936-2945 ◽  
Author(s):  
Anne K. Churchland ◽  
Stephen G. Lisberger
Keyword(s):  
Eye Movements ◽  
Smooth Pursuit ◽  
Gain Control ◽  
Sine Wave ◽  
Target Motion ◽  
Smooth Pursuit Eye Movements ◽  
Target Speed ◽  
Pursuit Eye Movements ◽  
On Line ◽  
Visual Motor

In previous experiments, on-line modulation of the gain of visual-motor transmission for pursuit eye movements was demonstrated in monkeys by showing that the response to a brief perturbation of target motion was strongly enhanced during pursuit relative to during fixation. The present paper elaborates the properties of on-line gain control by recording the smooth-pursuit eye movements of human subjects during tracking of a spot target. When perturbations consisted of one cycle of a 5-Hz sine wave, responses were significantly larger during pursuit than during fixation. Furthermore, responses grew as a function of eye/target velocity at the time of the perturbation and of perturbation amplitude. Thus human pursuit, like monkey pursuit, is modulated by on-line gain control. For larger perturbations consisting of a single sine wave at 2.8 Hz, ±19°/s, the degree of enhancement depended strongly on the phase of the perturbation. Enhancement was present when “peak-first” perturbations caused the target speed to increase first and was attenuated when “peak-last” perturbations caused target speed to decrease first. This effect was most profound when the perturbation was 2.8 Hz, ±19°/s but was also present when the amplitude of the peak-last perturbation was ±5o/s. For peak-last perturbations, the eye velocity evoked by the later peak of the perturbation was inversely related to that evoked by the preceding trough of the perturbation. We interpret these effects of perturbation phase as evidence that peak-last perturbations cause a decrease in the on-line gain of visual-motor transmission for pursuit. We conclude that gain control is modulated dynamically as behavioral conditions change. Finally, when perturbations were presented as a sequence of three large, peak-last sine waves starting at the onset of target motion at 10°/s, repeating the conditions used in prior studies on humans, we were able to replicate the prior finding that the response to the perturbations was equal during pursuit and fixation. We conclude that on-line gain control modulates human pursuit and that it can be probed most reliably with small, brief perturbations that do not affect the on-line gain themselves.

scholarly journals Different mechanisms for modulation of the initiation and steady-state of smooth pursuit eye movements

2020 ◽  
Vol 123 (3) ◽  
pp. 1265-1276 ◽  
Author(s):  
Stuart Behling ◽  
Stephen G. Lisberger
Keyword(s):  
Steady State ◽  
Eye Movements ◽  
Smooth Pursuit ◽  
Visual Motion ◽  
Smooth Pursuit Eye Movements ◽  
Target Speed ◽  
Pursuit Eye Movements ◽  
Visual Motor ◽  
State Tracking ◽  
Eye Velocity

Smooth pursuit eye movements are used by primates to track moving objects. They are initiated by sensory estimates of target speed represented in the middle temporal (MT) area of extrastriate visual cortex and then supported by motor feedback to maintain steady-state eye speed at target speed. Here, we show that reducing the coherence in a patch of dots for a tracking target degrades the eye speed both at the initiation of pursuit and during steady-state tracking, when eye speed reaches an asymptote well below target speed. The deficits are quantitatively different between the motor-supported steady-state of pursuit and the sensory-driven initiation of pursuit, suggesting separate mechanisms. The deficit in visually guided pursuit initiation could not explain the deficit in steady-state tracking. Pulses of target speed during steady-state tracking revealed lower sensitivities to image motion across the retina for lower values of dot coherence. However, sensitivity was not zero, implying that visual motion should still be driving eye velocity toward target velocity. When we changed dot coherence from 100% to lower values during accurate steady-state pursuit, we observed larger eye decelerations for lower coherences, as expected if motor feedback was reduced in gain. A simple pursuit model accounts for our data based on separate modulation of the strength of visual-motor transmission and motor feedback. We suggest that reduced dot coherence allows us to observe evidence for separate modulations of the gain of visual-motor transmission during pursuit initiation and of the motor corollary discharges that comprise eye velocity memory and support steady-state tracking. NEW & NOTEWORTHY We exploit low-coherence patches of dots to control the initiation and steady state of smooth pursuit eye movements and show that these two phases of movement are modulated separately by the reliability of visual motion signals. We conclude that the neural circuit for pursuit includes separate modulation of the strength of visual-motor transmission for movement initiation and of eye velocity positive feedback to support steady-state tracking.

scholarly journals Different mechanisms for modulation of the initiation and steady-state of smooth pursuit eye movements

2019 ◽  
Author(s):  
Stuart Behling ◽  
Stephen G. Lisberger
Keyword(s):  
Steady State ◽  
Eye Movements ◽  
Smooth Pursuit ◽  
Smooth Pursuit Eye Movements ◽  
Target Speed ◽  
Pursuit Eye Movements ◽  
Pursuit Initiation ◽  
Visual Motor ◽  
State Tracking ◽  
Eye Velocity

AbstractSmooth pursuit eye movements are used by primates to track moving objects. They are initiated by sensory estimates of target speed represented in the middle temporal (MT) area of extrastriate visual cortex and then supported by motor feedback to maintain steady-state eye speed at target speed. Here, we show that reducing the coherence in a patch of dots for a tracking target degrades the eye speed both at the initiation of pursuit and during steady-state tracking, when eye speed reaches an asymptote well below target speed. The deficits are quantitatively different between the motor-supported steady-state of pursuit and the sensory-driven initiation of pursuit, suggesting separate mechanisms. The deficit in visually-guided pursuit initiation could not explain the deficit in steady-state tracking. Pulses of target speed during steady-state tracking revealed lower sensitivities to image motion across the retina for lower values of dot coherence. However, sensitivity was not zero, implying that visual motion should still be driving eye velocity towards target velocity. When we changed dot coherence from 100% to lower values during accurate steady-state pursuit, we observed larger eye decelerations for lower coherences, as expected if motor feedback was reduced in gain. A simple pursuit model accounts for our data based on separate modulation of the strength of visual-motor transmission and motor feedback. We suggest that reduced dot coherence creates less reliable target motion that impacts pursuit initiation by changing the gain of visual-motor transmission and perturbs steady-state tracking by modulation of the motor corollary discharges that comprise eye velocity memory.

scholarly journals Spatial and Temporal Integration of Visual Motion Signals for Smooth Pursuit Eye Movements in Monkeys

2009 ◽  
Vol 102 (4) ◽  
pp. 2013-2025 ◽  
Author(s):  
Leslie C. Osborne ◽  
Stephen G. Lisberger
Keyword(s):  
Random Walk ◽  
Eye Movements ◽  
Smooth Pursuit ◽  
Visual Motion ◽  
Target Motion ◽  
Linear Filter ◽  
Smooth Pursuit Eye Movements ◽  
Spatial Averaging ◽  
Spatial Integration ◽  
Pursuit Eye Movements

To probe how the brain integrates visual motion signals to guide behavior, we analyzed the smooth pursuit eye movements evoked by target motion with a stochastic component. When each dot of a texture executed an independent random walk such that speed or direction varied across the spatial extent of the target, pursuit variance increased as a function of the variance of visual pattern motion. Noise in either target direction or speed increased the variance of both eye speed and direction, implying a common neural noise source for estimating target speed and direction. Spatial averaging was inefficient for targets with >20 dots. Together these data suggest that pursuit performance is limited by the properties of spatial averaging across a noisy population of sensory neurons rather than across the physical stimulus. When targets executed a spatially uniform random walk in time around a central direction of motion, an optimized linear filter that describes the transformation of target motion into eye motion accounted for ∼50% of the variance in pursuit. Filters had widths of ∼25 ms, much longer than the impulse response of the eye, and filter shape depended on both the range and correlation time of motion signals, suggesting that filters were products of sensory processing. By quantifying the effects of different levels of stimulus noise on pursuit, we have provided rigorous constraints for understanding sensory population decoding. We have shown how temporal and spatial integration of sensory signals converts noisy population responses into precise motor responses.

Single-neuron activity in the dorsomedial frontal cortex during smooth-pursuit eye movements to predictable target motion

1997 ◽  
Vol 14 (5) ◽  
pp. 853-865 ◽  
Author(s):  
S. J. Heinen ◽  
M. Liu
Keyword(s):  
Eye Movements ◽  
Frontal Cortex ◽  
Smooth Pursuit ◽  
Saccadic Eye Movements ◽  
Target Motion ◽  
Neuron Activity ◽  
Smooth Pursuit Eye Movements ◽  
Pursuit Eye Movements ◽  
Supplementary Eye Field ◽  
Dorsomedial Frontal Cortex

AbstractA region of dorsomedial frontal cortex (DMFC) has been implicated in planning and executing saccadic eye movements; hence it has been referred to as a supplementary eye field (SEF). Recently, activity related to executing smooth-pursuit eye movements has been recorded from the DMFC, and microstimulation here has been shown to evoke smooth eye movements. This report documents neuronal activity present in smooth-pursuit tasks where the predictability of target motion was manipulated. The activity of many neurons in the DMFC reached a peak when a predictable change in target motion occurred. Furthermore, the peak activity of some cells was systematically shifted by manipulating the duration of the target event, indicating that the network these neurons were in could learn the temporal characteristics of new target motion. Finally, the activity of most neurons tested was greater when target motion was predictable than when it was unpredictable. The results suggest that the DMFC participates in planning smooth-pursuit eye movements based on past stimulus history.

scholarly journals Signals That Modulate Gain Control for Smooth Pursuit Eye Movements in Monkeys

2004 ◽  
Vol 91 (2) ◽  
pp. 623-631 ◽  
Author(s):  
Megan R. Carey ◽  
Stephen G. Lisberger
Keyword(s):  
Eye Movements ◽  
Smooth Pursuit ◽  
Target Motion ◽  
Smooth Pursuit Eye Movements ◽  
Velocity Signal ◽  
Adaptive Modification ◽  
The Gaze ◽  
Pursuit Eye Movements ◽  
Internal Gain ◽  
Vor Adaptation

The generation of primate smooth pursuit eye movements involves two processes. One process transforms the direction and speed of target motion into a motor command and the other regulates the strength, or “gain,” of the visual-motor transformation. We have conducted a behavioral analysis to identify the signals that modulate the internal gain of pursuit. To test whether the modulatory signals are related to eye velocity in the orbit or in the world (gaze velocity), we used brief perturbations of target motion to probe the gain of pursuit during tracking conditions that used head rotation to dissociate eye and gaze velocity. We found that the responses to perturbations varied primarily as a function of gaze velocity. To further understand the gaze velocity signals that control internal pursuit gain, we used adaptive modification of the gain of the vestibulo-ocular reflex (VOR) to dissociate physical gaze velocity from the component of gaze velocity that is driven by visual inputs. After VOR adaptation, perturbation responses were altered; the smallest perturbation responses now occurred during tracking conditions that required nonzero physical gaze velocity. However, perturbation responses during tracking conditions that mimicked the modified VOR were still enhanced relative to those obtained during fixation. We conclude that the signals that modulate the internal gain of pursuit are modified by VOR adaptation so that they are rendered intermediate between physical and visually driven gaze velocity. Similar changes in the gaze velocity signal have been reported in the cerebellar floccular complex following adaptive modification of the VOR and could be present in other brain areas that carry putative gaze velocity signals.

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