scholarly journals The neural basis for response latency in a sensory-motor behavior

2019 ◽  
Author(s):  
Joonyeol Lee ◽  
Timothy R. Darlington ◽  
Stephen G. Lisberger
Keyword(s):  
Reaction Time ◽  
Eye Movement ◽  
Smooth Pursuit ◽  
Visual Motion ◽  
Threshold Model ◽  
Field Potential ◽  
Reaction Times ◽  
Neural Basis ◽  
Sensory Motor ◽  
Preparatory Activity

AbstractWe seek a neural circuit explanation for sensory-motor reaction times. We have found evidence that two of three possible mechanisms could contribute to reaction times in smooth pursuit eye movements. In the smooth eye movement region of the frontal eye fields (FEFSEM), an area that causally affects the initiation of smooth pursuit eye movement, neural and behavioral latencies have significant trial-by-trial correlations that can account for 40% to 100% of the variation in behavioral latency. The amplitude of preparatory activity, which represents the motor system’s expectations for target motion, shows negative trial-by-trial correlations with behavioral latency and could contribute to the neural computation of reaction time. In contrast, the traditional “ramp-to-threshold” model is contradicted by the responses of many, but not all FEFSEM neurons. As evidence of neural processing that determines reaction time, the local field potential in FEFSEM includes a brief wave in the 5-15 Hz frequency range that precedes pursuit initiation and whose phase is correlated with the latency of pursuit in individual trials. We suggest that the latency of the incoming visual motion signals combines with the state of preparatory activity to determine the latency of the transient response that drives eye movement.

scholarly journals The Neural Basis for Response Latency in a Sensory-Motor Behavior

2019 ◽  
Vol 30 (5) ◽  
pp. 3055-3073 ◽  
Author(s):  
Joonyeol Lee ◽  
Timothy R Darlington ◽  
Stephen G Lisberger
Keyword(s):  
Reaction Time ◽  
Eye Movement ◽  
Visual Motion ◽  
Neural Circuit ◽  
Threshold Model ◽  
Field Potential ◽  
Motor Reaction ◽  
Neural Basis ◽  
Sensory Motor ◽  
Preparatory Activity

Abstract We seek a neural circuit explanation for sensory-motor reaction times. In the smooth eye movement region of the frontal eye fields (FEFSEM), the latencies of pairs of neurons show trial-by-trial correlations that cause trial-by-trial correlations in neural and behavioral latency. These correlations can account for two-third of the observed variation in behavioral latency. The amplitude of preparatory activity also could contribute, but the responses of many FEFSEM neurons fail to support predictions of the traditional “ramp-to-threshold” model. As a correlate of neural processing that determines reaction time, the local field potential in FEFSEM includes a brief wave in the 5–15-Hz frequency range that precedes pursuit initiation and whose phase is correlated with the latency of pursuit in individual trials. We suggest that the latency of the incoming visual motion signals combines with the state of preparatory activity to determine the latency of the transient response that controls eye movement. Impact statement The motor cortex for smooth pursuit eye movements contributes to sensory-motor reaction time through the amplitude of preparatory activity and the latency of transient, visually driven responses.

scholarly journals Asymmetric smooth pursuit eye movements and visual motion reaction time

2019 ◽  
Vol 7 (14) ◽  
Author(s):  
Seiji Ono ◽  
Kenichiro Miura ◽  
Takashi Kawamura ◽  
Tomohiro Kizuka
Keyword(s):  
Eye Movements ◽  
Reaction Time ◽  
Smooth Pursuit ◽  
Visual Motion ◽  
Smooth Pursuit Eye Movements ◽  
Pursuit Eye Movements

scholarly journals An Electrophysiological Correlate of Visual Motion Awareness in Man

1998 ◽  
Vol 10 (4) ◽  
pp. 464-471 ◽  
Author(s):  
Thomas Haarmeier ◽  
Peter Thier
Keyword(s):  
Eye Movement ◽  
Smooth Pursuit ◽  
Visual Processing ◽  
Retinal Image ◽  
Visual Motion ◽  
Reference Signal ◽  
Subjective Perception ◽  
Internal Reference ◽  
Signal Encoding ◽  
Physical Attributes

It is usually held that perceptual spatial stability, despite smooth pursuit eye movements, is accomplished by comparing a signal reflecting retinal image slip with an internal reference signal, encoding the eye movement. The important consequence of this concept is that our subjective percept of visual motion reflects the outcome of this comparison rather than retinal image slip. In an attempt to localize the cortical networks underlying this comparison and therefore our subjective percept of visual motion, we exploited an imperfection inherent in it, which results in a movement illusion. If smooth pursuit is carried out across a stationary background, we perceive a tiny degree of illusionary background motion (Filehne illusion, or FI), rather than experiencing the ecologically optimal percept of stationarity. We have recently shown that this illusion can be modified substantially and predictably under laboratory conditions by visual motion unrelated to the eye movement. By making use of this finding, we were able to compare cortical potentials evoked by pursuit-induced retinal image slip under two conditions, which differed perceptually, while being identical physically. This approach allowed us to discern a pair of potentials, a parieto-occipital negativity (N300) followed by a frontal positivity (P300), whose amplitudes were solely determined by the subjective perception of visual motion irrespective of the physical attributes of the situation. This finding strongly suggests that subjective awareness of visual motion depends on neuronal activity in a parietooccipito-frontal network, which excludes the early stages of visual processing.

Neural basis for motor learning in the vestibuloocular reflex of primates. III. Computational and behavioral analysis of the sites of learning

1994 ◽  
Vol 72 (2) ◽  
pp. 974-998 ◽  
Author(s):  
S. G. Lisberger
Keyword(s):  
Motor Learning ◽  
Eye Movement ◽  
Computer Model ◽  
Visual Motion ◽  
Transfer Functions ◽  
Vestibular Nucleus ◽  
Vestibuloocular Reflex ◽  
Neural Basis ◽  
Ventral Paraflocculus ◽  
Eye Velocity

1. We have used a combination of eye movement recordings and computer modeling to study long-term adaptive modification (motor learning) in the vestibuloocular reflex (VOR). The eye movement recordings place constraints on possible sites for motor learning. The computer model abides by these constraints, as well as constraints provided by data in previous papers, to formalize a new hypothesis about the sites of motor learning. The model was designed to reproduce as much of the existing neural and behavioral data as possible. 2. Motor learning was induced in monkeys by fitting them with spectacles that caused the gain of the VOR (eye speed divided by head speed) to increase to values > 1.6 or to decrease to values < 0.4. We elicited pursuit by providing ramp motion of a small target at 30 degrees/s along the horizontal axis. Changes in the gain of the VOR caused only small and inconsistent changes in the eye acceleration in the first 100 ms after the onset of pursuit and had no effect on the eye velocity during tracking of steady target motion. Electrical stimulation in the flocculus and ventral paraflocculus with single pulses or trains of pulses caused smooth eye movement toward the side of stimulation after latencies of 9–11 ms. Neither the latency, the peak eye velocity, nor the initial eye acceleration varied as a consistent function of the gain of the VOR. 3. The computer model contained nodes that represented position-vestibular-pause cells (PVP-cells) and flocculus target neurons (FTNs) in the vestibular nucleus, and horizontal gaze-velocity Purkinje cells (HGVP-cells) in the cerebellar flocculus and ventral paraflocculus. Node FTN represented only the “E-c FTNs,” which show increased firing for eye motion away from the side of recording. The transfer functions in the model included dynamic elements (filters) as well as static elements (summing junctions, gain elements, and time delays). Except for the transfer functions that converted visual motion inputs into commands for smooth eye movement, the model was linear. 4. The performance of the model was determined both by computer simulation and, for the VOR in the dark, by analytic solution of linear equations. For simulation, we adjusted the parameters by hand to match the output of the model to the eye velocity of monkeys and to match the activity of the relevant nodes in the model to the firing of HGVP-cells, FTNs, and PVP-cells when the gain of the VOR was 0.4, 1.0, and 1.6.(ABSTRACT TRUNCATED AT 400 WORDS)

The effect of a moving distractor on the initiation of smooth-pursuit eye movements

1997 ◽  
Vol 14 (2) ◽  
pp. 323-338 ◽  
Author(s):  
Vincent P. Ferrera ◽  
Stephen G. Lisberger
Keyword(s):  
Eye Movements ◽  
Eye Movement ◽  
Smooth Pursuit ◽  
Visual Motion ◽  
Target Selection ◽  
Motion Processing ◽  
Smooth Pursuit Eye Movements ◽  
Response Preparation ◽  
Pursuit Eye Movements ◽  
Visual Motion Processing

AbstractAs a step toward understanding the mechanism by which targets are selected for smooth-pursuit eye movements, we examined the behavior of the pursuit system when monkeys were presented with two discrete moving visual targets. Two rhesus monkeys were trained to select a small moving target identified by its color in the presence of a moving distractor of another color. Smooth-pursuit eye movements were quantified in terms of the latency of the eye movement and the initial eye acceleration profile. We have previously shown that the latency of smooth pursuit, which is normally around 100 ms, can be extended to 150 ms or shortened to 85 ms depending on whether there is a distractor moving in the opposite or same direction, respectively, relative to the direction of the target. We have now measured this effect for a 360 deg range of distractor directions, and distractor speeds of 5–45 deg/s. We have also examined the effect of varying the spatial separation and temporal asynchrony between target and distractor. The results indicate that the effect of the distractor on the latency of pursuit depends on its direction of motion, and its spatial and temporal proximity to the target, but depends very little on the speed of the distractor. Furthermore, under the conditions of these experiments, the direction of the eye movement that is emitted in response to two competing moving stimuli is not a vectorial combination of the stimulus motions, but is solely determined by the direction of the target. The results are consistent with a competitive model for smooth-pursuit target selection and suggest that the competition takes place at a stage of the pursuit pathway that is between visual-motion processing and motor-response preparation.

scholarly journals Delay of Movement Caused by Disruption of Cortical Preparatory Activity

2007 ◽  
Vol 97 (1) ◽  
pp. 348-359 ◽  
Author(s):  
Mark M. Churchland ◽  
Krishna V. Shenoy
Keyword(s):  
Reaction Time ◽  
Primary Motor Cortex ◽  
Premotor Cortex ◽  
Reaction Times ◽  
Intracortical Microstimulation ◽  
Movement Preparation ◽  
Preparation Time ◽  
Preparatory Activity ◽  
Specific Increase ◽  
Optimal Subspace

We tested the hypothesis that delay-period activity in premotor cortex is essential to movement preparation. During a delayed-reach task, we used subthreshold intracortical microstimulation to disrupt putative “preparatory” activity. Microstimulation led to a highly specific increase in reach reaction time. Effects were largest when activity was disrupted around the time of the go cue. Earlier disruptions, which presumably allowed movement preparation time to recover, had only a weak impact. Furthermore, saccadic reaction time showed little or no increase. Finally, microstimulation of nearby primary motor cortex, even when slightly suprathreshold, had little effect on reach reaction time. These findings provide the first evidence, of a causal and temporally specific nature, that activity in premotor cortex is fundamental to movement preparation. Furthermore, although reaction times were increased, the movements themselves were essentially unperturbed. This supports the suggestion that movement preparation is an active and actively monitored process and that movement can be delayed until inaccuracies are repaired. These results are readily interpreted in the context of the recently developed optimal-subspace hypothesis.

scholarly journals The interaction between elapsed time and decision accuracy differs between humans and rats

2019 ◽  
Author(s):  
Carly A Shevinsky ◽  
Pamela Reinagel
Keyword(s):  
Decision Making ◽  
Reaction Time ◽  
Visual Motion ◽  
Reaction Times ◽  
Local Analysis ◽  
Decision Strategy ◽  
Motion Discrimination ◽  
Motion Coherence ◽  
The Many ◽  
The Individual

AbstractA stochastic visual motion discrimination task is widely used to study rapid decision-making in humans and animals. Among trials of the same sensory difficulty within a block of fixed decision strategy, humans and monkeys are widely reported to make more errors in the individual trials with longer reaction times. This finding has posed a challenge for the drift-diffusion model of sensory decision-making, which in its basic form predicts that errors and correct responses should have the same reaction time distributions. We previously reported that rats also violate this model prediction, but in the opposite direction: for rats, motion discrimination accuracy was highest in the trials with the longest reaction times. To rule out task differences as the cause of our divergent finding in rats, the present study tested humans and rats using the same task and analyzed their data identically. We confirmed that rats’ accuracy increased with reaction time, whereas humans’ accuracy decreased with reaction time in the same task. These results were further verified using a new temporally-local analysis method, ruling out that the observed trend was an artifact of non-stationarity in the data of either species. The main effect was found whether the signal strength (motion coherence) was varied in randomly interleaved trials or held constant within a block. The magnitude of the effects increased with motion coherence. These results provide new constraints useful for refining and discriminating among the many alternative mathematical theories of decision-making.

Oculomotor biomarkers of illness, risk, and pharmacogenetic treatment effects across the psychosis spectrum

2020 ◽  
pp. 234-244
Author(s):  
James L. Reilly ◽  
Jennifer McDowell ◽  
Jeffrey Bishop ◽  
Andreas Sprenger ◽  
Rebekka Lencer
Keyword(s):  
Eye Movement ◽  
Smooth Pursuit ◽  
Visual Motion ◽  
Gene Variants ◽  
Motion Information ◽  
Antisaccade Task ◽  
Movement Performance ◽  
Research Findings ◽  
Sensorimotor Processes ◽  
Psychotic Bipolar Disorder

Eye movements are used to assess alterations in brain systems involved in cognitive and sensorimotor processes in psychiatric disorders. This chapter summarizes findings comparing saccadic and smooth pursuit eye movement performance across psychotic proband and relative groups, with an emphasis on schizophrenia, schizoaffective disorder, and psychotic bipolar disorder. Inhibitory errors on the antisaccade task represent a robust and graded deficit across probands, with greatest impairment observed in schizophrenia, and among relatives, particularly those with elevated psychosis spectrum traits. Abnormalities in the use of early visual motion information during smooth pursuit is apparent among both probands and relatives, while deficient use of visual feedback for dynamical pursuit appears restricted to probands. Select eye movement measures appear differentially affected by glutamate and dopamine gene variants. Overall, research findings support eye movement measures as promising biomarkers of altered brain systems underlying select cognitive and sensorimotor processes across the psychosis spectrum.

Sign in / Sign up

Export Citation Format

Share Document