Transition dynamics between pursuit and fixation suggest different systems

Interactions between Saccades and Smooth Pursuit Eye Movements in Marmosets

Animals use a combination of eye movements to track moving objects. These different eye movements need to be coordinated for successful tracking, requiring interactions between the systems involved. Here, we study the interaction between the saccadic and smooth pursuit eye movement systems in marmosets. Using a single-target pursuit task, we show that saccades cause an enhancement in pursuit following a saccade. Using a two-target pursuit task, we show that this enhancement in pursuit is selective toward the motion of the target selected by the saccade, irrespective of any biases in pursuit prior to the saccade. These experiments highlight the similarities in the functioning of saccadic and smooth pursuit eye movement systems across primates.

Interactions between saccades and smooth pursuit eye movements in marmosets

Animals use a combination of eye movements to track moving objects. These different eye movements need to be coordinated for successful tracking, requiring interactions between the systems involved. Here, we study the interaction between the saccadic and smooth pursuit eye movement systems in marmosets. Using a single target pursuit task, we show that saccades cause an enhancement in pursuit following a saccade. Using a two-target pursuit task, we show that this enhancement in pursuit is selective towards the motion of the target selected by the saccade, irrespective of any biases in pursuit prior to the saccade. These experiments highlight the similarities in the functioning of saccadic and smooth pursuit eye movement systems across primates.

SIGNIFICANCE STATEMENT

We study the coordination between the smooth-pursuit and saccadic eye movement systems in marmosets using single and multiple object motions. We find that saccade to a target increases pursuit velocity towards the target. If multiple objects are visible, saccade choice makes pursuit more selective towards the saccade target. Our results show that coordination between different eye movement systems to successfully track moving objects is similar between marmosets and primates.

Peripheral targets attenuate miniature eye movements during fixation

Fixating a small dot is a universal technique for stabilizing gaze in vision and eye movement research, and for clinical imaging of normal and diseased retinae. During fixation, microsaccades and drifts occur that presumably benefit vision, yet microsaccades compromise image stability and usurp task attention. Previous work suggested that microsaccades and smooth pursuit catch-up saccades are controlled by similar mechanisms. This, and other previous work showing fewer catch-up saccades during smooth pursuit of peripheral targets suggested that a peripheral target might similarly mitigate microsaccades. Here, human observers fixated one of three stimuli: a small central dot, the center of a peripheral, circular array of small dots, or a central/peripheral stimulus created by combining the two. The microsaccade rate was significantly lower with the peripheral array than with the dot. However, inserting the dot into the array increased the microsaccade rate to single-dot levels. Drift speed also decreased with the peripheral array, both with and without the central dot. Eye position variability was higher with the array than with the composite stimulus. The results suggest that analogous to the foveal pursuit, foveating a stationary target engages the saccadic system likely compromising retinal-image stability. In contrast, fixating a peripheral stimulus improves stability, thereby affording better retinal imaging and releasing attention for experimental tasks.

Toward a Biomimetic Neural Circuit Model of Sensory-Motor Processing

Computational models have been a mainstay of research on smooth pursuit eye movements in monkeys. Pursuit is a sensory-motor system that is driven by the visual motion of small targets. It creates a smooth eye movement that accelerates up to target speed and tracks the moving target essentially perfectly. In this review of my laboratory's research, I trace the development of computational models of pursuit eye movements from the early control-theory models to the most recent neural circuit models. I outline a combined experimental and computational plan to move the models to the next level. Finally, I explain why research on nonhuman primates is so critical to the development of the neural circuit models I think we need.

Eye stabilization

This chapter summarizes how visual feedback could be used to stabilize the line of sight and optimize vision during attempted fixation of a stationary target. Quantitative features of oculomotor noise that causes drifts of the eye away from the target are analyzed. The sources of such noise, including the ripples in eye position due to muscle fiber twitches, and drifts of the eye away from the visual target due to vestibular imbalance, are examined. Evidence for a promptly responding stabilization system, distinct from optokinetic or pursuit eye movements is reviewed. Smooth eye movements that negate drifts of the eyes, which are discussed here, are distinct from microsaccades, which are discussed in chapter "Behavior of the saccadic system: Metrics of timing and accuracy" by Robinson.

Impaired fixation suppression of horizontal vestibular nystagmus during smooth pursuit: pathophysiology and clinical implications

Background and purpose

A peripheral spontaneous nystagmus (SN) is typically enhanced or revealed by removing fixation. Conversely, failure of fixation suppression of SN is usually a sign of a central disorder. Based on Luebke and Robinson (Vision Res 1988, vol. 28 (8), pp. 941–946), who suggested that the normal fixation mechanism is disengaged during pursuit, it is hypothesized that vertical tracking in the light would bring out or enhance a horizontal SN.

Methods

Eighteen patients with acute vestibular neuritis were studied. Eye movements were recorded using video‐oculography at straight‐ahead gaze with and without visual fixation, and during smooth pursuit. The slow‐phase velocity and the fixation suppression indices of nystagmus (relative to SN in darkness) were compared in each condition.

Results

During vertical tracking, the slow‐phase velocity of horizontal SN with eyes near straight‐ahead gaze was significantly higher (median 2.7°/s) than under static visual fixation (median 1.2°/s). Likewise, the fixation index was significantly higher (worse suppression) during pursuit (median 48%) than during fixation (median 26%). A release of SN was also suggested during horizontal pursuit, if one assumes superposition of SN on a normal and symmetrical pursuit capability.

Suppressive control of optokinetic and vestibular nystagmus by the primate frontal eye field

In this study, electrical stimulation in the frontal eye field (FEF) suppressed the quick and slow phases of optokinetic and vestibular nystagmus at an intensity subthreshold for eliciting saccades. Furthermore, the activity of fixation neurons in the FEF was related to the suppression of optokinetic and vestibular nystagmus by visual fixation. This suggests that a common neuronal assembly in the FEF may contribute to the suppressive control of different functional classes of eye movements.

The role of cortical areas hMT/V5+ and TPJ on the magnitude of representational momentum and representational gravity: a transcranial magnetic stimulation study

The perceived vanishing location of a moving target is systematically displaced forward, in the direction of motion-representational momentum-, and downward, in the direction of gravity-representational gravity. Despite a wealth of research on the factors that modulate these phenomena, little is known regarding their neurophysiological substrates. The present experiment aims to explore which role is played by cortical areas hMT/V5+, linked to the processing of visual motion, and TPJ, thought to support the functioning of an internal model of gravity, in modulating both effects. Participants were required to perform a standard spatial localization task while the activity of the right hMT/V5+ or TPJ sites was selectively disrupted with an offline continuous theta-burst stimulation (cTBS) protocol, interspersed with control blocks with no stimulation. Eye movements were recorded during all spatial localizations. Results revealed an increase in representational gravity contingent on the disruption of the activity of hMT/V5+ and, conversely, some evidence suggested a bigger representational momentum when TPJ was stimulated. Furthermore, stimulation of hMT/V5+ led to a decreased ocular overshoot and to a time-dependent downward drift of gaze location. These outcomes suggest that a reciprocal balance between perceived kinematics and anticipated dynamics might modulate these spatial localization responses, compatible with a push-pull mechanism.

Visual Fixation and Continuous Head Rotations Have Minimal Effect on Set-Point Adaptation to Magnetic Vestibular Stimulation

Background: Strong static magnetic fields such as those in an MRI machine can induce sensations of self-motion and nystagmus. The proposed mechanism is a Lorentz force resulting from the interaction between strong static magnetic fields and ionic currents in the inner ear endolymph that causes displacement of the semicircular canal cupulae. Nystagmus persists throughout an individual's exposure to the magnetic field, though its slow-phase velocity partially declines due to adaptation. After leaving the magnetic field an after effect occurs in which the nystagmus and sensations of rotation reverse direction, reflecting the adaptation that occurred while inside the MRI. However, the effects of visual fixation and of head shaking on this early type of vestibular adaptation are unknown. Methods: Three-dimensional infrared video-oculography was performed in six individuals just before, during (5, 20, or 60 min) and after (4, 15, or 20 min) lying supine inside a 7T MRI scanner. Trials began by entering the magnetic field in darkness followed 60 s later, either by light with visual fixation and head still, or by continuous yaw head rotations (2 Hz) in either darkness or light with visual fixation. Subjects were always placed in darkness 10 or 30 s before exiting the bore. In control conditions subjects remained in the dark with the head still for the entire duration. Results: In darkness with head still all subjects developed horizontal nystagmus inside the magnetic field, with slow-phase velocity partially decreasing over time. An after effect followed on exiting the magnet, with nystagmus in the opposite direction. Nystagmus was suppressed during visual fixation; however, after resuming darkness just before exiting the magnet, nystagmus returned with velocity close to the control condition and with a comparable after effect. Similar after effects occurred with continuous yaw head rotations while in the scanner whether in darkness or light. Conclusions: Visual fixation and sustained head shaking either in the dark or with fixation inside a strong static magnetic field have minimal impact on the short-term mechanisms that attempt to null unwanted spontaneous nystagmus when the head is still, so called VOR set-point adaptation. This contrasts with the critical influence of vision and slippage of images on the retina on the dynamic (gain and direction) components of VOR adaptation.

A Subconscious Interaction between Fixation and Anticipatory Pursuit

Ocular smooth pursuit and fixation are typically viewed as separate systems, yet there is evidence that the brainstem fixation system inhibits pursuit. Here we present behavioral evidence that the fixation system modulates pursuit behavior outside of conscious awareness. Human observers (male and female) either pursued a small spot that translated across a screen, or fixated it as it remained stationary. As shown previously, pursuit trials potentiated the oculomotor system, producing anticipatory eye velocity on the next trial before the target moved that mimicked the stimulus-driven velocity. Randomly interleaving fixation trials reduced anticipatory pursuit, suggesting that a potentiated fixation system interacted with pursuit to suppress eye velocity in upcoming pursuit trials. The reduction was not due to passive decay of the potentiated pursuit signal because interleaving "blank" trials in which no target appeared did not reduce anticipatory pursuit. Interspersed short fixation trials reduced anticipation on long pursuit trials, suggesting that fixation potentiation was stronger than pursuit potentiation. Furthermore, adding more pursuit trials to a block did not restore anticipatory pursuit, suggesting that fixation potentiation was not overridden by certainty of an imminent pursuit trial but rather was immune to conscious intervention. To directly test whether cognition can override fixation suppression, we alternated pursuit and fixation trials to perfectly specify trial identity. Still, anticipatory pursuit did not rise above that observed with an equal number of random fixation trials. The results suggest that potentiated fixation circuitry interacts with pursuit circuitry at a subconscious level to inhibit pursuit.SIGNIFICANCE STATEMENT When an object moves, we view it with smooth pursuit eye movements. When an object is stationary, we view it with fixational eye movements. Pursuit and fixation are historically regarded as controlled by different neural circuitry, and alternating between invoking them is thought to be guided by a conscious decision. However, our results show that pursuit is actively suppressed by prior fixation of a stationary object. This suppression is involuntary, and cannot be avoided even if observers are certain that the object will move. The results suggest that the neural fixation circuitry is potentiated by engaging stationary objects, and interacts with pursuit outside of conscious awareness.

Control of the strength of visual-motor transmission as the mechanism of rapid adaptation of priors for Bayesian inference in smooth pursuit eye movements

Bayesian inference provides a cogent account of how the brain combines sensory information with "priors" based on past experience to guide many behaviors, including smooth pursuit eye movements. We now demonstrate very rapid adaptation of the pursuit system's priors for target direction and speed. We go on to leverage that adaptation to outline possible neural mechanisms that could cause pursuit to show features consistent with Bayesian inference. Adaptation of the prior causes changes in the eye speed and direction at the initiation of pursuit. The adaptation appears after a single trial and accumulates over repeated exposure to a given history of target speeds and directions. The influence of the priors depends on the reliability of visual motion signals: priors are more effective against the visual motion signals provided by low-contrast vs. high-contrast targets. Adaptation of the direction prior generalizes to eye speed and vice versa, suggesting that both priors could be controlled by a single neural mechanism. We conclude that the pursuit system can learn the statistics of visual motion rapidly and use those statistics to guide future behavior. Furthermore, a model that adjusts the gain of visual-motor transmission predicts the effects of recent experience on pursuit direction and speed, as well as the specifics of the generalization between the priors for speed and direction. We suggest that Bayesian inference in pursuit behavior is implemented by distinctly non-Bayesian internal mechanisms that use the smooth eye movement region of the frontal eye fields to control of the gain of visual-motor transmission.NEW & NOTEWORTHY Bayesian inference can account for the interaction between sensory data and past experience in many behaviors. Here, we show, using smooth pursuit eye movements, that the priors based on past experience can be adapted over a very short time frame. We also show that a single model based on direction-specific adaptation of the strength of visual-motor transmission can explain the implementation and adaptation of priors for both target direction and target speed.

Stopping smooth pursuit

If a visual object of interest suddenly starts to move, we will try to follow it with a smooth movement of the eyes. This smooth pursuit response aims to reduce image motion on the retina that could blur visual perception. In recent years, our knowledge of the neural control of smooth pursuit initiation has sharply increased. However, stopping smooth pursuit eye movements is less well understood and will be discussed in this paper. The most straightforward way to study smooth pursuit stopping is by interrupting image motion on the retina. This causes eye velocity to decay exponentially towards zero. However, smooth pursuit stopping is not a passive response, as shown by behavioural and electrophysiological evidence. Moreover, smooth pursuit stopping is particularly influenced by active prediction of the upcoming end of the target. Here, we suggest that a particular class of inhibitory neurons of the brainstem, the omnipause neurons, could play a central role in pursuit stopping. Furthermore, the role of supplementary eye fields of the frontal cortex in smooth pursuit stopping is also discussed.This article is part of the themed issue 'Movement suppression: brain mechanisms for stopping and stillness'.

Control of the gain of visual-motor transmission occurs in visual coordinates for smooth pursuit eye movements

Sensory inputs control motor behavior with a strength, or gain, that can be modulated according to the movement conditions. In smooth pursuit eye movements, the response to a brief perturbation of target motion is larger during pursuit of a moving target than during fixation of a stationary target. As a step toward identifying the locus and mechanism of gain modulation, we test whether it acts on signals that are in visual or motor coordinates. Monkeys tracked targets that moved at 15°/s in one of eight directions, including left, right, up, down, and the four oblique directions. In eight-ninths of the trials, the target underwent a brief perturbation that consisted of a single cycle of a 10 Hz sine wave of amplitude ±5°/s in one of the same eight directions. Even for oblique directions of baseline target motion, the magnitude of the eye velocity response to the perturbation was largest for a perturbation near the axis of target motion and smallest for a perturbation along the orthogonal axis. Computational modeling reveals that our data are reproduced when the strength of visual-motor transmission is modulated in sensory coordinates, and there is a static motor bias that favors horizontal eye movements. A network model shows how the output from the smooth eye movement region of the frontal eye fields (FEF(SEM)) could implement gain control by shifting the peak of a visual population response along the axes of preferred image speed and direction.

Visual contribution to the high-frequency human angular vestibulo-ocular reflex

The vestibulo-ocular reflex (VOR) acts to maintain images stable on the retina by rotating the eyes in exactly the opposite direction, but with equal magnitude, to head velocity. When viewing a near target, this reflex has an increased response to compensate for the translation of the eyes relative to the target that acts to reduce retinal image slip. Previous studies have shown that retinal velocity error provides an important visual feedback signal to increase the low-frequency (<1 Hz) VOR response during near viewing. We sought to determine whether initial eye position and retinal image position error could provide enough information to substantially increase the high-frequency VOR gain (eye velocity/head velocity) during near viewing. Ten human subjects were tested using the scleral search coil technique during horizontal head impulses under different lighting conditions (constant dark, strobe light at 0.5, 1, 2, 4, 10, 15 Hz, constant light) while viewing near (9.5 ± 1.3 cm) and far (104 cm) targets. Our results showed that the VOR gain increased during near viewing compared to far viewing, even during constant dark. For the near target, there was an increase in VOR gain with increasing strobe frequency from 1.17 ± 0.17 in constant dark to 1.36 ± 0.27 in constant light, a 21 ± 9 % increase. For the far target, strobe frequency had no effect. Presentation order of strobe frequency (i.e. 0.5-15 vs. 15-0.5 Hz) did not affect the gain, but it did affect the vergence angle (angle between the two eye's lines of sight). The VOR gain and vergence angles were constant during each trial. Our findings show that a retinal position error signal helps increase the vergence angle and could be invoking vestibular adaptation mechanisms to increase the high-frequency VOR response during near viewing. This is in contrast to the low-frequency VOR that depends more on retinal velocity error and predictive adaptation mechanisms.

Flexibility of foveal attention during ocular pursuit

Smooth pursuit of natural objects requires flexible allocation of attention to inspect features. However, it has been reported that attention is focused at the fovea during pursuit. We ask here if foveal attention is obligatory during pursuit, or if it can be disengaged. Observers tracked a stimulus composed of a central dot surrounded by four others and identified one of the dots when it dimmed. Extinguishing the center dot before the dimming improved task performance, suggesting that attention was released from it. To determine if the center dot automatically usurped attention, we provided the pursuit system with an alternative sensory signal by adding peripheral motion that moved with the stimulus. This also improved identification performance, evidence that a central target does not necessarily require attention during pursuit. Identification performance at the central dot also improved, suggesting that the spatial extent of the background did not attract attention to the periphery; instead, peripheral motion freed pursuit attention from the central dot, affording better identification performance. The results show that attention can be flexibly allocated during pursuit and imply that attention resources for pursuit of small and large objects come from different sources.

The cerebellar nodulus/uvula integrates otolith signals for the translational vestibulo-ocular reflex

Background

The otolith-driven translational vestibulo-ocular reflex (tVOR) generates compensatory eye movements to linear head accelerations. Studies in humans indicate that the cerebellum plays a critical role in the neural control of the tVOR, but little is known about mechanisms of this control or the functions of specific cerebellar structures. Here, we chose to investigate the contribution of the nodulus and uvula, which have been shown by prior studies to be involved in the processing of otolith signals in other contexts.

Methodology/Principal Findings

We recorded eye movements in two rhesus monkeys during steps of linear motion along the interaural axis before and after surgical lesions of the cerebellar uvula and nodulus. The lesions strikingly reduced eye velocity during constant-velocity motion but had only a small effect on the response to initial head acceleration. We fit eye velocity to a linear combination of head acceleration and velocity and to a dynamic mathematical model of the tVOR that incorporated a specific integrator of head acceleration. Based on parameter optimization, the lesion decreased the gain of the pathway containing this new integrator by 62%. The component of eye velocity that depended directly on head acceleration changed little (gain decrease of 13%). In a final set of simulations, we compared our data to the predictions of previous models of the tVOR, none of which could account for our experimental findings.

Conclusions/ Significance

Our results provide new and important information regarding the neural control of the tVOR. Specifically, they point to a key role for the cerebellar nodulus and uvula in the mathematical integration of afferent linear head acceleration signals. This function is likely to be critical not only for the tVOR but also for the otolith-mediated reflexes that control posture and balance.

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

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.

Internal models of eye movement in the floccular complex of the monkey cerebellum

Internal models are a key feature of most modern theories of motor control. Yet, it has been challenging to localize internal models in the brain, or to demonstrate that they are more than a metaphor. In the present review, I consider a large body of data on the cerebellar floccular complex, asking whether floccular output has features that would be expected of the output from internal models. I argue that the simple spike firing rates of a single group of floccular Purkinje cells could reflect the output of three different internal models. (1) An eye velocity positive feedback pathway through the floccular complex provides neural inertia for smooth pursuit eye movements, and appears to operate as a model of the inertia of real-world objects. (2) The floccular complex processes and combines input signals so that the dynamics of its average simple spike output are appropriate for the dynamics of the downstream brainstem circuits and eyeball. If we consider the brainstem circuits and eyeball as a more broadly conceived "oculomotor plant," then the output from the floccular complex could be the manifestation of an inverse model of "plant" dynamics. (3) Floccular output reflects an internal model of the physics of the orbit where head and eye motion sum to produce gaze motion. The effects of learning on floccular output suggest that it is modeling the interaction of the visually-guided and vestibular-driven components of eye and gaze motion. Perhaps the insights from studying oculomotor control provide groundwork to guide the analysis of internal models for a wide variety of cerebellar behaviors.

Ocular Responses to Brief Motion of Textured Backgrounds During Smooth Pursuit in Humans

We studied the effects of horizontal smooth pursuit on the ocular responses to brief vertical perturbations of textured backgrounds in humans. When the subject was fixating on a stationary target, a brief vertical perturbation of the background elicited a small tracking response. When the subject was pursuing a target moving horizontally, the same background perturbation elicited a larger response: that is, the response to vertical background perturbations was enhanced during pursuit (pursuit-related enhancement). On the other hand, the dependencies of the ocular responses on spatial frequency, temporal frequency, and stimulus contrast were similar regardless of the ongoing behavior of the subject. We also found that a low-level energy-based mechanism underlies the ocular responses to vertical perturbations of the background during fixation and smooth pursuit. We conclude that the pursuit-related enhancement is independent of the properties of visual processing for cross-axis motion of backgrounds, which suggests that this enhancement results from uniform facilitation of the visual system and/or from facilitation of visuomotor transmission downstream of the visual processing.

Dynamics of smooth pursuit maintenance

Smooth pursuit eye movements allow the approximate stabilization of a moving visual target on the retina. To study the dynamics of smooth pursuit, we measured eye velocity during the visual tracking of a Gabor target moving at a constant velocity plus a noisy perturbation term. The optimal linear filter linking fluctuations in target velocity to evoked fluctuations in eye velocity was computed. These filters predicted eye velocity to novel stimuli in the 0- to 15-Hz band with good accuracy, showing that pursuit maintenance is approximately linear under these conditions. The shape of the filters were indicative of fast dynamics, with pure delays of merely approximately 67 ms, times-to-peak of approximately 115 ms, and effective integration times of approximately 45 ms. The gain of the system, reflected in the amplitude of the filters, was inversely proportional to the size of the velocity fluctuations and independent of the target mean speed. A modest slow-down of the dynamics was observed as the contrast of the target decreased. Finally, the temporal filters recovered during fixation and pursuit were similar in shape, supporting the notion that they might share a common underlying circuitry. These findings show that the visual tracking of moving objects by the human eye includes a reflexive-like pathway with high contrast sensitivity and fast dynamics.

A theory of the dual pathways for smooth pursuit based on dynamic gain control

The smooth pursuit eye movement (SPEM) system is much more sensitive to target motion perturbations during pursuit than during fixation. This sensitivity is commonly attributed to a dynamic gain control mechanism. Neither the neural substrate nor the functional architecture for this gain control has been fully revealed. There are at least two cortical areas that crucially contribute to smooth pursuit and are therefore eligible sites for dynamic gain control: the medial superior temporal area (MST) and the pursuit area of the frontal eye fields (FEFs), which both project to brain stem premotor structures via parallel pathways. The aim of this study was to develop a model of smooth pursuit based on behavioral, anatomical, and neurophysiological results to account for nonlinear dynamic gain control. Using a behavioral paradigm in humans consisting of a sinusoidal oscillation (4 Hz, +/-8 degrees/s) superimposed on a constant velocity target motion (0-24 degrees/s), we were able to identify relevant gain control parameters in the model. A salient feature of our model is the emergence of two parallel pathways from higher visual cortical to lower motor areas in the brain stem that correspond to the MST and FEF pathways. Detailed analysis of the model revealed that one pathway mainly carries eye velocity related signals, whereas the other is associated mostly with eye acceleration. From comparison with known neurophysiological results we conclude that the dynamic gain control can be attributed to the FEF pathway, whereas the MST pathway serves as the basic circuit for maintaining an ongoing SPEM.

Saccades exert spatial control of motion processing for smooth pursuit eye movements

Saccades modulate the relationship between visual motion and smooth eye movement. Before a saccade, pursuit eye movements reflect a vector average of motion across the visual field. After a saccade, pursuit primarily reflects the motion of the target closest to the endpoint of the saccade. We tested the hypothesis that the saccade produces a spatial weighting of motion around the endpoint of the saccade. Using a moving pursuit stimulus that stepped to a new spatial location just before a targeting saccade, we controlled the distance between the endpoint of the saccade and the position of the moving target. We demonstrate that the smooth eye velocity following the targeting saccade weights the presaccadic visual motion inputs by the distance from their location in space to the endpoint of the saccade, defining the extent of a spatiotemporal filter for driving the eyes. The center of the filter is located at the endpoint of the saccade in space, not at the position of the fovea. The filter is stable in the face of a distracter target, is present for saccades to stationary and moving targets, and affects both the speed and direction of the postsaccadic eye movement. The spatial filter can explain the target-selecting gain change in postsaccadic pursuit, and has intriguing parallels to the process by which perceptual decisions about a restricted region of space are enhanced by attention. The effect of the spatial saccade plan on the pursuit response to a given retinal motion describes the dynamics of a coordinate transformation.

Interaural Translational VOR: Suppression, Enhancement, and Cognitive Control

We investigated the influence of cognitive factors on the early response of the interaural translational vestibuloocular reflex (tVOR) in six normal subjects. Variables were prior knowledge of direction of head motion and the position of the fixation target relative to the head [head-fixed (HF) or space-fixed (SF)]. A manually driven device provided a step-like head translation (∼35 mm distance, peak acceleration, 0.6–1.3 g). Subjects looked at the SF or HF target located 15 cm in front of their heads in otherwise complete darkness. The testing paradigms were: random interleaving of SF and HF targets with unknown direction of head movement, known target location with random head direction (SFR or HFR), and known target location with known head direction (SFP or HFP). Timing was always unpredictable. A “gain” of the slow phase was calculated with respect to ideal performance (maintained fixation of the SF target, recorded/ideal eye velocity computed at time of peak head velocity). At such times, there were no significant differences in gain between HF and SF trials in the random condition; the average gain was ∼36% of ideal. On the other hand, responses in the SFR and HFR conditions differed as early as 20 ms after the head began moving. Average gain was higher (0.43 ± 0.11 vs. 0.34 ± 0.14; means ± SD, P < 0.05) for each subject in the SFR than the HFR condition. For SFP and HFP, the responses differed from the onset of head motion. Average slow-phase gain was higher (0.49 ± 0.12 vs. 0.31 ± 0.12, P < 0.02) for each subject in SFP than in HFP. The timing of corrective saccades during the tVOR was also influenced by cognitive factors. Visual error signals seemed to be more important for triggering saccades in HF trials, whereas preprogramming, probably based on labyrinthine information, seemed to be more important in SF trials. Simulations showed that the changes in slow-phase gain with cognition could be reproduced with simple parametric adjustments of the gain of activity from otolith afferents and suggest that higher-level cognitive control of the VOR could occur as early as the synapse of peripheral afferents on neurons in the vestibular nuclei, either directly from higher level centers or via the cerebellum. In sum, the tVOR—both in its slow-phase response and the saccadic corrections—is subject to “higher-level” cognitive influences including knowledge of where the line of sight must point during head motion and the impending direction of head motion.

Evidence for object permanence in the smooth-pursuit eye movements of monkeys

We recorded the smooth-pursuit eye movements of monkeys in response to targets that were extinguished (blinked) for 200 ms in mid-trajectory. Eye velocity declined considerably during the target blinks, even when the blinks were completely predictable in time and space. Eye velocity declined whether blinks were presented during steady-state pursuit of a constant-velocity target, during initiation of pursuit before target velocity was reached, or during eye accelerations induced by a change in target velocity. When a physical occluder covered the trajectory of the target during blinks, creating the impression that the target moved behind it, the decline in eye velocity was reduced or abolished. If the target was occluded once the eye had reached target velocity, pursuit was only slightly poorer than normal, uninterrupted pursuit. In contrast, if the target was occluded during the initiation of pursuit, while the eye was accelerating toward target velocity, pursuit during occlusion was very different from normal pursuit. Eye velocity remained relatively stable during target occlusion, showing much less acceleration than normal pursuit and much less of a decline than was produced by a target blink. Anticipatory or predictive eye acceleration was typically observed just prior to the reappearance of the target. Computer simulations show that these results are best understood by assuming that a mechanism of eye-velocity memory remains engaged during target occlusion but is disengaged during target blinks.

Preparatory modulation of the gain of visuo-motor transmission for smooth pursuit in monkeys

Brief movement of a foveated target is known to elicit higher velocity ocular (tracking) responses if the target is in motion rather than stationary. We determined whether similar perturbations of a stationary target would have greater ocular effects if we merely increased the probability that the target might undergo sustained motion. For this, we examined the effect of interleaving trials in which the target was always stationary with trials in which the target underwent sustained motion that required the animal to track. We found that perturbation of the stationary target had a greater effect when there were interleaved trials in which the target moved, as though the gain of the visuo-motor transmission had been increased in anticipation of future tracking.

Non-target influences on the initiation of smooth pursuit

Smooth pursuit is usually regarded as a relatively stereotyped oculomotor response in which the early part of the response reflects primarily the properties of the visual stimulus and cortical motion processing. We have investigated pursuit initiation in human subjects using the gap paradigm to alter fixation conditions, and single stationary distractors to alter visual context. The results suggest that a number of processes, distinct from motion processing, are involved in pursuit initiation. The processes which are modified by gaps and distractors are closely related and interact with each other. They may be shared with the saccade system.

Asymmetric integration recorded from vestibular-only cells in response to position transients

Angular and translational accelerations excite the semicircular canals and otolith organs, respectively. While canal afferents approximately encode head angular velocity due to the biomechanical integration performed by the canals, otolith signals have been found to approximate head translational acceleration. Because central vestibular pathways require velocity and position signals for their operation, the question has been raised as to how the integration of the otolith signals is accomplished. We recorded responses from 62 vestibular-only neurons in the vestibular nucleus of two monkeys to position transients in the naso-occipital and interaural orientations and varying directions in between. Responses to the transients were directionally asymmetric; one direction elicited a response that approximated the integral of the acceleration of the stimulus. In the opposite direction, the cells simply encoded the acceleration of the motion. We present a model that suggests that a neural integrator is not needed. Instead a neuron with a long membrane time constant and an excitatory postsynaptic potential duration that increases with the firing rate of the presynaptic cell can emulate the observed behavior.

Gain control in human smooth-pursuit eye movements

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 degrees/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 degrees/s but was also present when the amplitude of the peak-last perturbation was +/-5 degrees/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 degrees /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.

Role of arcuate frontal cortex of monkeys in smooth pursuit eye movements. II. Relation to vector averaging pursuit

When monkeys view two targets moving in different directions and are given no cues about which to track, the initiation of smooth pursuit is a vector average of the response evoked by each target singly. In the present experiments, double-target stimuli consisted of two identical targets moving in opposite directions along the preferred axis of pursuit for the neuron under study for 200 ms, followed by the continued motion for 800 ms of one target chosen randomly. Among the neurons that showed directional modulation during pursuit, recordings revealed three groups. The majority (32/60) showed responses that were intermediate to, and statistically different from, the responses to either target presented alone. Another large group (22/60) showed activity that was statistically indistinguishable from the response to the target moving in the preferred (n = 15) or opposite (n = 7) direction of the neuron under study. The minority (6/60) showed statistically higher firing during averaging pursuit than for either target presented singly. We conclude that many pursuit-related neurons in the frontal pursuit area (FPA) carry signals related to the motor output during averaging pursuit, while others encode the motion of one target or the other. Microstimulation with 200-ms trains of pulses at 50 microA while monkeys performed the same double-target tasks biased the averaging eye velocity in the direction of evoked eye movements during fixation. The effect of stimulation was compared with the predictions of three different models that placed the site of vector averaging upstream from, at, or downstream from the sites where the FPA regulates the gain of pursuit. The data were most consistent with a site for pursuit averaging downstream from the gain control, both for double-target stimuli that presented motion in opposite directions and in orthogonal directions. Thus the recording and stimulation data suggest that the FPA is both downstream and upstream from the sites of vector averaging. We resolve this paradox by suggesting that the site of averaging is really downstream from the site of gain control, while feedback of the eye velocity command from the brain stem and/or cerebellum is responsible for the firing of FPA neurons in relation to the averaged eye velocity. We suggest that eye velocity feedback allows FPA neurons to continue firing during accurate tracking, when image motion is small, and that the persistent output from the FPA is necessary to keep the internal gain of pursuit high and permit accurate pursuit.

Enhancement of multiple components of pursuit eye movement by microstimulation in the arcuate frontal pursuit area in monkeys

Periarcuate frontal cortex is involved in the control of smooth pursuit eye movements, but its role remains unclear. To better understand the control of pursuit by the "frontal pursuit area" (FPA), we applied electrical microstimulation when the monkeys were performing a variety of oculomotor tasks. In agreement with previous studies, electrical stimulation consisting of a train of 50-microA pulses at 333 Hz during fixation of a stationary target elicited smooth eye movements with a short latency (approximately 26 ms). The size of the elicited smooth eye movements was enhanced when the stimulation pulses were delivered during the maintenance of pursuit. The enhancement increased as a function of ongoing pursuit speed and was greater during pursuit in the same versus opposite direction of the eye movements evoked at a site. If stimulation was delivered during pursuit in eight different directions, the elicited eye velocity was fit best by a model incorporating two stimulation effects: a directional signal that drives eye velocity and an increase in the gain of ongoing pursuit eye speed in all directions. Separate experiments tested the effect of stimulation on the response to specific image motions. Stimulation consisted of a train of pulses at 100 or 200 Hz delivered during fixation so that only small smooth eye movements were elicited. If the stationary target was perturbed briefly during microstimulation, normally weak eye movement responses showed strong enhancement. If delivered at the initiation of pursuit, the same microstimulation caused enhancement of the presaccadic initiation of pursuit for steps of target velocity that moved the target either away from the position of fixation or in the direction of the eye movement caused by stimulation at the site. Stimulation in the FPA increased the latency of saccades to stationary or moving targets. Our results show that the FPA has two kinds of effects on the pursuit system. One drives smooth eye velocity in a fixed direction and is subject to on-line gain control by ongoing pursuit. The other causes enhancement of both the speed of ongoing pursuit and the responses to visual motion in a way that is not strongly selective for the direction of pursuit. Enhancement may operate either at a single site or at multiple sites. We conclude that the FPA plays an important role in on-line gain control for pursuit as well as possibly delivering commands for the direction and speed of smooth eye motion.

Contrast sensitivity in a dynamic environment: effects of target conditions and visual impairment

Contrast sensitivity was determined as a function of target velocity (0 degrees - 120 degrees/s) over a variety of viewing conditions. In Experiment 1, measurements of dynamic contrast sensitivity were determined for 24 male and 24 female observers as a function of target velocity for letter stimuli of 2 sizes and 2 durations. Significant main effects were found for target velocity, target size, and target duration, but significant interactions among the variables indicated especially pronounced adverse effects of increasing target velocity for small targets and brief durations. In Experiment 2, the effects of simulated cataracts on dynamic contrast sensitivity were determined for 10 male and 10 female observers. Although the simulated impairment had no effect on traditional acuity scores, dynamic contrast sensitivity was markedly reduced under all conditions but especially with the smaller targets and at higher velocities. Results are discussed in terms of dynamic contrast sensitivity as a useful composite measure of visual functioning that may provide a better overall picture of an individual's visual functioning than does traditional static acuity, dynamic acuity, or contrast sensitivity alone. The measure of dynamic contrast sensitivity may increase understanding of the practical effects of various conditions, such as aging or disease, on the visual system, or it may allow improved prediction of individuals' performance in visually dynamic, situations, such as driving and sports.

Experimental and computational analysis of monkey smooth pursuit eye movements

Smooth pursuit eye movements are guided by visual feedback and are surprisingly accurate despite the time delay between visual input and motor output. Previous models have reproduced the accuracy of pursuit either by using elaborate visual signals or by adding sources of motor feedback. Our goal was to constrain what types of signals drive pursuit by obtaining data that would discriminate between these two modeling approaches, represented by the "image motion model" and the "tachometer feedback" model. Our first set of experiments probed the visual properties of pursuit with brief square-pulse and sine-wave perturbations of target velocity. Responses to pulse perturbations increased almost linearly with pulse amplitude, while responses to sine wave perturbations showed strong saturation with increasing stimulus amplitude. The response to sine wave perturbations was strongly dependent on the baseline image velocity at the time of the perturbation. Responses were much smaller if baseline image velocity was naturally large, or was artificially increased by superimposing sine waves on pulse perturbations. The image motion model, but not the tachometer feedback model, could reproduce these features of pursuit. We used a revision of the image motion model that was, like the original, sensitive to both image velocity and image acceleration. Due to a saturating nonlinearity, the sensitivity to image acceleration declined with increasing image velocity. Inclusion of this nonlinearity was motivated by our experimental results, was critical in accounting for the responses to perturbations, and provided an explanation for the unexpected stability of pursuit in the presence of perturbations near the resonant frequency. As an emergent property, the revised image motion model was able to reproduce the frequency and damping of oscillations recorded during artificial feedback delays. Our second set of experiments replicated prior recordings of pursuit responses to multiple-cycle sine wave perturbations, presented over a range of frequencies. The image motion model was able to reproduce the responses to sine wave perturbations across all frequencies, while the tachometer feedback model failed at high frequencies. These failures resulted from the absence of image acceleration signals in the tachometer model. We conclude that visual signals related to image acceleration are important in driving pursuit eye movements and that the nonlinearity of these signals provides stability. Smooth pursuit thus illustrates that a plausible neural strategy for combating natural delays in sensory feedback is to employ information about the derivative of the sensory input.

Modulation of spatial attention with unidirectional field motion: an implication for the shift of the OKN beating field

During optokinetic nystagmus (OKN) the mean eye position of gaze (the beating field) shifts in the direction of the fast phases. The function of this shift may be to re-orient the eyes in the direction of self-motion which optic flow implies (in-coming field). This idea leads to the hypothesis that visual attention may be directed toward the In-coming field. In Experiment 1, subjects detected a visual flash presented against unidirectional field motion. The OKN beating field was shifted toward the In-coming field, and manual reaction times were shorter when the target appeared in the In-coming field. Experiment 2 revealed that this In-coming field advantage occurred even when OKN (and thus the mean eye-position shift) was suppressed. Subsequent experiments showed that the In-coming field advantage is not due to a local motion interaction (Experiment 3), survives subject's voluntary allocation of attention (Experiment 4), and develops over less than 320 ms after the onset of the motion field (Experiment 5). These results suggest that unidirectional field motion tends to automatically shift visual attention toward the In-coming field.

The role of target position in smooth pursuit deceleration and termination

Subjects smoothly pursued a target moving horizontally at 15 deg/s. After pursuit for 1 s, the target jumped 3 deg ahead of the fovea. At the moment of the jump, target velocity became 0 and 'effective visual feedback' assumed a value of either 0 (target retinally stabilized), -0.2, -0.4, or -1.0 (target fixed in space). With 0 visual feedback the eye continued to move smoothly at a moderate velocity, an apparent response to target position relative to the fovea. When negative visual feedback was present eye velocity decreased. With -0.2 and -0.4 feedback, this decrease was not a simple exponential, but often consisted of an initial fast decrease followed by slower decrease. With -1.0 feedback, eye velocity quickly decreased in an approximately exponential manner, and stopped. We were able to simulate these pursuit responses using a simple model of the pursuit system. Key features of the model are: (a) a target-velocity channel whose output decreases with target offset from the fovea, and whose gain switches from high to low as pursuit velocity approaches zero; (b) a target-position channel with a saturation non-linearity at 1-3 deg; and (c) a positive feedback loop with gain of less than 1.0. All of these features are essential to simulate the pursuit responses, especially with visual feedback values of -0.2 and -0.4. Our results and model suggest that target position serves as an important stimulus in guiding smooth pursuit as pursuit velocity decreases, and especially during pursuit termination.

Fixation stability in schizophrenia, bipolar, and control subjects

A few investigators have suggested that visual fixation abnormalities may serve as an endophenotype of liability for schizophrenia. However, the data are equivocal. Conflicting reports regarding the specificity of fixation deficits to schizophrenia may be attributable to methodological differences. Thirty-four schizophrenia patients, 20 bipolar patients, and 30 non-patient controls were presented targets for central fixation. Fixation was scored in terms of frequency of saccades as well as qualitative ratings. Analysis of variance on the number of saccades produced during fixation revealed that the three groups did not differ. Similarly, we observed that the schizophrenia patients did not differ from either bipolar patients or controls in terms of ratings of fixation quality. It appears that schizophrenia patients are not characterized by poor visual fixation. The findings are discussed in terms of the viability of visual fixation as a marker of schizophrenic diathesis, as well as possible implications for the analysis of schizophrenia patients' visual search performance.

Discharge properties of neurons in the rostral superior colliculus of the monkey during smooth-pursuit eye movements

The intermediate and deep layers of the monkey superior colliculus (SC) comprise a retinotopically organized map for eye movements. The rostral end of this map, corresponding to the representation of the fovea, contains neurons that have been referred to as "fixation cells" because they discharge tonically during active fixation and pause during the generation of most saccades. These neurons also possess movement fields and are most active for targets close to the fixation point. Because the parafoveal locations encoded by these neurons are also important for guiding pursuit eye movements, we studied these neurons in two monkeys as they generated smooth pursuit. We found that fixation cells exhibit the same directional preferences during pursuit as during small saccades-they increase their discharge during movements toward the contralateral side and decrease their discharge during movements toward the ipsilateral side. This pursuit-related activity could be observed during saccade-free pursuit and was not predictive of small saccades that often accompanied pursuit. When we plotted the discharge rate from individual neurons during pursuit as a function of the position error associated with the moving target, we found tuning curves with peaks within a few degrees contralateral of the fovea. We compared these pursuit-related tuning curves from each neuron to the tuning curves for a saccade task from which we separately measured the visual, delay, and peri-saccadic activity. We found the highest and most consistent correlation with the delay activity recorded while the monkey viewed parafoveal stimuli during fixation. The directional preferences exhibited during pursuit can therefore be attributed to the tuning of these neurons for contralateral locations near the fovea. These results support the idea that fixation cells are the rostral extension of the buildup neurons found in the more caudal colliculus and that their activity conveys information about the size of the mismatch between a parafoveal stimulus and the currently foveated location. Because the generation of pursuit requires a break from fixation, the pursuit-related activity indicates that these neurons are not strictly involved with maintaining fixation. Conversely, because activity during the delay period was found for many neurons even when no eye movement was made, these neurons are also not obligatorily related to the generation of a movement. Thus the tonic activity of these rostral neurons provides a potential position-error signal rather than a motor command-a principle that may be applicable to buildup neurons elsewhere in the SC.

Apparent motion produces multiple deficits in visually guided smooth pursuit eye movements of monkeys

We used apparent motion targets to explore how degraded visual motion alters smooth pursuit eye movements. Apparent motion targets consisted of brief stationary flashes with a spatial separation (Deltax), temporal separation (Deltat), and apparent target velocity equal to Deltax/Deltat. Changes in pursuit initiation were readily observed when holding target velocity constant and increasing the flash separation. As flash separation increased, the first deficit observed was an increase in the latency to peak eye acceleration. Also seen was a paradoxical increase in initial eye acceleration. Further increases in the flash separation produced larger increases in latency and resulted in decreased eye acceleration. By varying target velocity, we were able to discern that the visual inputs driving pursuit initiation show both temporal and spatial limits. For target velocities above 4-8 degrees /s, deficits in the initiation of pursuit were seen when Deltax exceeded 0.2-0.5 degrees, even when Deltat was small. For target velocities below 4-8 degrees /s, deficits appeared when Deltat exceeded 32-64 ms, even when Deltax was small. Further experiments were designed to determine whether the spatial limit varied as retinal and extra-retinal factors changed. Varying the initial retinal position of the target for motion at 18 degrees /s revealed that the spatial limit increased as a function of retinal eccentricity. We then employed targets that increased velocity twice, once from fixation and again during pursuit. These experiments revealed that, as expected, the spatial limit is expressed in terms of the flash separation on the retina. The spatial limit is uninfluenced by either eye velocity or the absolute velocity of the target. These experiments also demonstrate that "initiation" deficits can be observed during ongoing pursuit, and are thus not deficits in initiation per se. We conclude that such deficits result from degradation of the retino-centric motion signals that drive pursuit eye acceleration. For large flash separations, we also observed deficits in the maintenance of pursuit: sustained eye velocity failed to match the constant apparent target velocity. Deficits in the maintenance of pursuit depended on both target velocity and Deltat and did not result simply from a failure of degraded image motion signals to drive eye acceleration. We argue that such deficits result from a low gain in the eye velocity memory that normally supports the maintenance of pursuit. This low gain may appear because visual inputs are so degraded that the transition from fixation to tracking is incomplete.

Smooth pursuit eye tracking and visual fixation in psychosis-prone individuals

Subjects identified by Perceptual Aberration-Magical Ideation (Per-Mag) scores (n=97), Social Anhedonia (SocAnh) scores (n=45), and Physical Anhedonia (PhysAnh) scores (n=31) as well as normal controls (n=94), underwent psychophysiological and clinical assessment. This is the first published investigation of pursuit system functioning in three groups of questionnaire-identified at-risk individuals. Pursuit during a simple non-monitor tracking task was measured using root-mean-square error (RMSE) scores and pursuit gain scores. Fixation performance was measured in terms of number of saccades away from the central fixation point. The at-risk subjects were more likely to display aberrant smooth pursuit tracking than controls, though there were no significant differences between the at-risk subjects endorsing items relevant to positive-symptom schizotypy and those endorsing items pertaining to negative-symptom schizotypy. The groups did not differ significantly in their visual fixation performance. Participants were also evaluated for the presence of Axis I symptomatology and psychotic-like experiences. Neither the experimental subjects nor the control subjects displayed a significant association between ocular motor performance and psychotic-like experiences. These findings are consistent with prior evidence that pursuit tracking is a trait characteristic, independent of clinical status.

Cerebellar flocculus and paraflocculus Purkinje cell activity during circular pursuit in monkey

Responses from 69 Purkinje cells in the flocculus and paraflocculus of two rhesus monkeys were studied during smooth pursuit of targets moving along circular trajectories and compared with responses during sinusoidal pursuit and fixation. A variety of interesting responses was observed during circular pursuit. Although some neurons fired most strongly in a single preferred direction during clockwise (CW) and counterclockwise (CCW) pursuit, others had directional preferences that changed with rotation direction. Some of these neurons showed similar modulation amplitudes during CW and CCW pursuit, whereas other neurons showed a preference for a particular rotation direction. Response specificity also was observed during sinusoidal pursuit. Some neurons showed responses that were much stronger during centrifugal pursuit, others showed a preference for centripetal pursuit, and still others showed responses during both centripetal and centrifugal motion. Differences in preferred response direction were sometimes observed for centripetal versus centrifugal pursuit. CW/CCW and centrifugal/centripetal preferences were not explained by a breakdown in component additivity. That is, modulations in firing rate during pursuit along a circular trajectory equaled the sum of modulations during horizontal and vertical sinusoidal components as well as for diagonal components. Instead all responses were well fit by a model that expressed the instantaneous firing rate of each neuron as a multilinear function of the two-dimensional position and velocity of the eye. This model generalized well to performance at different sinusoidal frequencies. It did somewhat less well for responses during fixation, suggesting some separation in the neural mechanisms of dynamic and static positioning. The model indicates that position sensitivity accounted for approximately 36% of the modulation during circular pursuit, and velocity sensitivity accounted for approximately 64%. When position and velocity sensitivity vectors were aligned, responses were simpler and modulations were similar during CW versus CCW pursuit. In contrast, when these vectors pointed in different directions, response complexity increased. Nonaligned position and velocity influences tended to reinforce during circular pursuit in one direction and to cancel each other during pursuit in the opposite direction. They also tended to produce response differences during centripetal versus centrifugal sinusoidal pursuit. The distinct roles played by position and velocity in shaping Purkinje cell responses are compatible with the control signals required to generate smooth pursuit along circular and other two-dimensional trajectories.

Enhancement of the vestibulo-ocular reflex by prior eye movements

We investigated the effect of visually mediated eye movements made before velocity-step horizontal head rotations in eleven normal human subjects. When subjects viewed a stationary target before and during head rotation, gaze velocity was initially perturbed by approximately 20% of head velocity; gaze velocity subsequently declined to zero within approximately 300 ms of the stimulus onset. We used a curve-fitting procedure to estimate the dynamic course of the gain throughout the compensatory response to head rotation. This analysis indicated that the median initial gain of compensatory eye movements (mainly because of the vestibulo-ocular reflex, VOR) was 0. 8 and subsequently increased to 1.0 after a median interval of 320 ms. When subjects attempted to fixate the remembered location of the target in darkness, the initial perturbation of gaze was similar to during fixation of a visible target (median initial VOR gain 0.8); however, the period during which the gain increased toward 1.0 was >10 times longer than that during visual fixation. When subjects performed horizontal smooth-pursuit eye movements that ended (i.e., 0 gaze velocity) just before the head rotation, the gaze velocity perturbation at the onset of head rotation was absent or small. The initial gain of the VOR had been significantly increased by the prior pursuit movements for all subjects (P < 0.05; mean increase of 11%). In four subjects, we determined that horizontal saccades and smooth tracking of a head-fixed target (VOR cancellation with eye stationary in the orbit) also increased the initial VOR gain (by a mean of 13%) during subsequent head rotations. However, after vertical saccades or smooth pursuit, the initial gaze perturbation caused by a horizontal head rotation was similar to that which occurred after fixation of a stationary target. We conclude that the initial gain of the VOR during a sudden horizontal head rotation is increased by prior horizontal, but not vertical, visually mediated gaze shifts. We postulate that this "priming" effect of a prior gaze shift on the gain of the VOR occurs at the level of the velocity inputs to the neural integrator subserving horizontal eye movements, where gaze-shifting commands and vestibular signals converge.

Shift of visual fixation dependent on background illumination

Visual fixation, the act of maintaining the eyes directed toward a location of interest, is a highly skilled behavior necessary for high-level vision in primates. In spite of its significance, visual fixation is not well understood; it is not even clear what attributes of the visual input are used to control fixation. Here we show, in four Macaca fascicularis monkeys, that the position the eyes assume during fixation depends on the luminance of the background. Dark background yields fixation positions that are shifted upward with respect to the fixation positions obtained with a dimly illuminated, featureless background. This phenomenon was observed previously in a nutshell by Snodderly; here first we rigorously establish its existence by testing proper controls. We then study the properties of this upshift of the fixation position. We show that, although the size of the upshift varies between monkeys, for all monkeys the upshift is larger than the radius of the fovea. Hence, if the background is dim, the eyes are positioned during fixation so that the target does not fall on the fovea. The size of the upshift remains almost unchanged while the eyes fixate at different orbital positions; thus the upshift is not caused by orbital mechanics. The upshift clearly is present even at the first days of training, but with additional training in fixation with dark background, the upshift increases in size. The upshift rotates with the head. The upshift increases gradually with decreasing levels of background luminosity. Luminosity, not visual contrast, is indeed the primary variable determining the size of the upshift. The contribution of a unit area of the retina to the upshift decreases as inverse square root of distance from the target; therefore, it is the perifoveal region of the retina that mostly contributes to the upshift, while the far periphery has little influence. The upshift can be induced or be canceled in the midst of a fixation by changing the background illumination; hence, the upshift is indeed an attribute of the fixation control system. Finally, the fixation-upshift studied here is different from a previously reported upshift of the endpoints of memory-guided saccades with respect to their target locations. These two types of upshift add up to each other. In discussing the function of the upshift, we note a possible morphological analogue with the retinal rod distribution. The upshift moves the line of gaze to a point intermediate between the fovea and the "dorsal rod peak." The upshift thus may improve visual acuity in scotopic conditions. The brain structure in which the upshift is generated must be involved in both ocular control and visual sensation. We consider several possibilities, of which we regard as the most likely the cerebellum and superior colliculus.

Eye tracking disorder in schizophrenia is characterized by specific ocular motor defects and is associated with the deficit syndrome

The objective was to determine the relationships between eye tracking disorder (ETD) in schizophrenia, specific ocular motor measures, and the deficit syndrome. Twenty-five normal comparison subjects and 53 schizophrenic patients had eye movements tested with infrared oculography using a sinusoidal target. Patients were assessed with the Schedule for the Deficit Syndrome. For the patients, the distribution of position root mean square error (a global measure of pursuit) was best fit by a mixture of two normal distributions. This information was used to divide the patients into two subgroups, those with and those without ETD. ETD was almost completely accounted for by several specific ocular motor measures and was significantly associated with the deficit syndrome. The finding that ETD was almost completely accounted for by specific measures bridges a gap of interpretation in this field. ETD and the deficit syndrome of schizophrenia may share a common pathophysiology of cerebral cortical-subcortical circuits.

Offset dynamics of human smooth pursuit eye movements: effects of target presence and subject attention

Subjects made smooth pursuit eye movements with a target moving horizontally at 15 deg/sec. At a specified location the target either: (1) suddenly vanished; or (2) jumped to the fovea with target retinal velocity and feedback becoming 0 (target stabilized at the fovea). In each type of trial, the subjects either: "looked" at the target, "pushed" the target, or "passively" gazed. When the target vanished, eye velocity decreased exponentially with a short time-constant (tau approximately 0.10 sec), regardless of whether the subjects were "looking," "pushing" or "passively" gazing. However, some subjects while "pushing" (using an imaginary target) did generate low velocity smooth movement (1-2.5 deg/sec) late in the offset. When the target was stabilized at the fovea, eye velocity also decreased, but with a relatively long time-constant (tau = 0.4-0.8 sec). The time-constant was the same with both "looking," and "pushing", but was shorter for some subjects with "passive" gazing (tau = 0.1-0.5 sec). These findings show that smooth pursuit offset is influenced by the presence of a target, but is relatively independent of attentional mode. All of the pursuit offset responses can be simulated using a model of the pursuit system with target velocity and position inputs, and an internal positive feedback loop enabled by target presence.

Decreases in the latency of smooth pursuit and saccadic eye movements produced by the "gap paradigm" in the monkey

The initiation of both pursuit and saccades was affected by the presence of a temporal gap between the disappearance of a fixated visual target and the appearance of a second, eccentric, target. For pursuit, the gap paradigm produced a modest (20 msec) decrease in latency. For saccades, the gap paradigm produced a similar modest decrease in the latency of some saccades, but also revealed a population of very short latency "express" saccades. The modest changes in the latency of pursuit and regular saccades displayed a similar dependence on gap duration, with the largest decreases produced by gaps of 200-300 msec. The gap paradigm did not produce "express" pursuit, even though express saccades could be elicited on interleaved trials.

The influence of structured visual backgrounds on smooth-pursuit initiation, steady-state pursuit and smooth-pursuit termination

Smooth-pursuit eye movements were recorded in two rhesus monkeys in order to compare the influence of structured visual backgrounds on smooth-pursuit initiation, steady-state pursuit and pursuit termination. Different target trajectories were used in order to study smooth-pursuit initiation and termination. The influence of visual backgrounds on pursuit initiation was characterized by recording ocular responses elicited by step-ramp target displacements starting from straight ahead. Pursuit termination was characterized by analysing the transition from steady-state smooth-pursuit to fixation when a centripetally directed target ramp was terminated by a small target step in the direction of the ramp as soon as the target had come close to the straightahead position. The quantification of steady-state pursuit was based on ocular responses elicited by either paradigm. In accordance with previous work, we found that the onset of smooth-pursuit eye movements was delayed and initial eye acceleration reduced in the presence of a structured visual background. Likewise, mean eye velocity during steady-state pursuit was reduced by structured visual backgrounds. However, neither the latency nor the time course of smooth-pursuit termination was altered when the homogeneous background was replaced by a structured visual background. The lack of sensitivity of pursuit termination to the presence of visual structured backgrounds supports a previous contention that pursuit termination is mediated by a process which is different from the ones mediating smooth-pursuit initiation and steady-state pursuit. The absence of any noticeable effect of structured backgrounds on pursuit termination suggests that at least the fast component of the optokinetic reflex is suppressed during pursuit termination.

Changes in visual fixation and saccadic eye movements in Alzheimer's disease

Visual fixation and saccadic eye movements were assessed in 31 mild to moderately demented patients with probable Alzheimer's disease (AD) and 31 age- and education-matched nondemented elderly control subjects. Seventeen AD and 17 matched control subjects were reassessed after a 9-month interval. On a fixation task, duration of fixation and number of intrusive saccades were not different between groups at baseline or follow-up. Both AD patients and control subjects showed more intrusive saccades at follow-up than at baseline. AD patients showed increased latency to initiation of saccades at baseline and on follow-up. Amplitude and velocity of saccades were not different between groups at any visit. Changes in measures of fixation, but no saccade measure, correlated with changes in MMSE scores over testing sessions. These data suggest that fixation is more sensitive than are saccades to the progession of AD.