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

Abstract

19Smooth pursuit eye movements are used by primates to track moving objects. They are initiated 20 by sensory estimates of target speed represented in the middle temporal (MT) area of extrastriate 21 visual cortex and then supported by motor feedback to maintain steady-state eye speed at target 22 speed. Here, we show that reducing the coherence in a patch of dots for a tracking target 23 degrades the eye speed both at the initiation of pursuit and during steady-state tracking, when eye 24 speed reaches an asymptote well below target speed. The deficits are quantitatively different 25 between the motor-supported steady-state of pursuit and the sensory-driven initiation of pursuit, 26 suggesting separate mechanisms. The deficit in visually-guided pursuit initiation could not 27 explain the deficit in steady-state tracking. Pulses of target speed during steady-state tracking 28 revealed lower sensitivities to image motion across the retina for lower values of dot coherence. 29However, sensitivity was not zero, implying that visual motion should still be driving eye 30 velocity towards target velocity. When we changed dot coherence from 100% to lower values 31 during accurate steady-state pursuit, we observed larger eye decelerations for lower coherences, 32 as expected if motor feedback was reduced in gain. A simple pursuit model accounts for our data 33 based on separate modulation of the strength of visual-motor transmission and motor feedback. 34 We suggest that reduced dot coherence creates less reliable target motion that impacts pursuit 35 initiation by changing the gain of visual-motor transmission and perturbs steady-state tracking by 36 modulation of the motor corollary discharges that comprise eye velocity memory. 37 67 a neural correlate of target velocity (Miles and Fuller, 1976; Lisberger and Fuchs, 1978; Stone 68 and Lisberger, 1990). Floccular Purkinje cells receive input from the sensory pathway as well as 69 positive motor feedback and generate simple-spike firing that represents a kinematic model of 70 eye movements (Shidara et al., 1993; Medina and Lisberger, 2009). In turn, Purkinje cells have 71 disynaptic connections to the motoneurons for the eye muscles and therefore drive pursuit 72 (Highstein, 1973; Scudder and Fuchs, 1992; Lisberger et al., 1994). 73Most previous thinking about the role of the floccular complex has assumed that eye velocity 74 positive feedback is fixed and immutable (Stone and Lisberger, 1990; Krauzlis and Lisberger, 75 1994; Schwartz and Lisberger, 1994). However, our analysis of the expression of motor learning 76 in pursuit raised the possibility that it is subject to modulation (Yang and Lisberger, 2010; Hall et 77 al., 2018). To determine if the steady-state of pursuit is modulated by motion reliability, and if 78the modulation is separate from that for the initiation of pursuit, we have developed a target and 79 a task that reliably perturbs both pursuit initiation and steady-state tracking. We use motion 80 coherence in a patch of dots (Newsome and Paré, 1...

Expectations about motion direction affect perception and anticipatory smooth pursuit differently

“…Congruently, anticipatory pursuit velocity is proportional to the average velocity of the target across previous trials (attraction bias in speed), and strongly affected by events in the 4 previous two trials (Maus et al 2015). Furthermore, Bayesian integration models have been used to describe how priors would lead to attraction effects in visually-guided pursuit when combined with noisy visual motion signals (Behling and Lisberger 2020;Darlington et al 2017;Deravet et al 2018).…”

Expectations about motion direction affect perception and anticipatory smooth pursuit differently

“…Congruently, anticipatory pursuit velocity is proportional to the average velocity of the target across previous trials (attraction bias in speed), and strongly affected by events in the previous two trials (Maus et al 2015). Furthermore, Bayesian integration models have been used to describe how priors would lead to attraction effects in visually-guided pursuit when combined with noisy visual motion signals (Behling and Lisberger 2020;Darlington et al 2017;Deravet et al 2018).…”

Closed loop motor-sensory dynamics in human vision

“…Such a rapid convergence can only be achieved in loops with cycle times that are significantly smaller than the convergence time–in the visual system these are low-level (certainly sub cortical; [ 77 ]) loops. These loops, thus, probably involve components that are already known to control saccades [ 78 – 80 ] and smooth pursuit eye movements [ 81 ]. Which components are shared across these control functions [ 82 ] and which are not is an exciting open question.…”

“…Which components are shared across these control functions [ 82 ] and which are not is an exciting open question. For example, smooth pursuit is hypothesized to be implemented by a feedback loop that minimizes the difference between a target velocity and the actual ocular velocity, where the target velocity is determined by other circuits of the visual system (e.g., [ 81 ]). The drift loop implicated by our results ( Fig 5A ) may or may not use the same components proposed for the velocity feedback loop allowing smooth pursuit.…”