A model of the smooth pursuit eye movement system

Gain Control in Predictive Smooth Pursuit Eye Movements: Evidence for an Acceleration-Based Predictive Mechanism

The smooth pursuit eye movement system incorporates various control features enabling adaptation to specific tracking situations. In this work, we analyzed the interplay between two of these mechanisms: gain control and predictive pursuit. We tested human responses to high-frequency perturbations during step-ramp pursuit, as well as the pursuit of a periodically moving target. For the latter task, we found a nonlinear interaction between perturbation response and carrier acceleration. Responses to perturbations where the initial perturbation acceleration was contradirectional to carrier acceleration increased with carrier velocity, in a manner similar to that observed during step-ramp pursuit. In contrast, responses to perturbations with ipsidirectional initial perturbation and carrier acceleration were large for all carrier velocities. Modeling the pursuit system suggests that gain control and short-term prediction are separable elements. The observed effect may be explained by combining the standard gain control mechanism with a derivative-based short-term predictive mechanism. The nonlinear interaction between perturbation and carrier acceleration can be reproduced by assuming a signal saturation, which is acting on the derivative of the target velocity signal. Our results therefore argue for the existence of an internal estimate of target acceleration as a basis for a simple yet efficient short-term predictive mechanism.

Saccadic suppression as a perceptual consequence of efficient sensorimotor estimation

Humans perform saccadic eye movements two to three times per second. When doing so, the nervous system strongly suppresses sensory feedback for extended periods of time in comparison to movement time. Why does the brain discard so much visual information? Here we suggest that perceptual suppression may arise from efficient sensorimotor computations, assuming that perception and control are fundamentally linked. More precisely, we show theoretically that a Bayesian estimator should reduce the weight of sensory information around the time of saccades, as a result of signal dependent noise and of sensorimotor delays. Such reduction parallels the behavioral suppression occurring prior to and during saccades, and the reduction in neural responses to visual stimuli observed across the visual hierarchy. We suggest that saccadic suppression originates from efficient sensorimotor processing, indicating that the brain shares neural resources for perception and control.

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'.

Visual and non-visual motion information processing during pursuit eye tracking in schizophrenia and bipolar disorder

Despite many reports on visual processing deficits in psychotic disorders, studies are needed on the integration of visual and non-visual components of eye movement control to improve the understanding of sensorimotor information processing in these disorders. Non-visual inputs to eye movement control include prediction of future target velocity from extrapolation of past visual target movement and anticipation of future target movements. It is unclear whether non-visual input is impaired in patients with schizophrenia. We recorded smooth pursuit eye movements in 21 patients with schizophrenia spectrum disorder, 22 patients with bipolar disorder, and 24 controls. In a foveo-fugal ramp task, the target was either continuously visible or was blanked during movement. We determined peak gain (measuring overall performance), initial eye acceleration (measuring visually driven pursuit), deceleration after target extinction (measuring prediction), eye velocity drifts before onset of target visibility (measuring anticipation), and residual gain during blanking intervals (measuring anticipation and prediction). In both patient groups, initial eye acceleration was decreased and the ability to adjust eye acceleration to increasing target acceleration was impaired. In contrast, neither deceleration nor eye drift velocity was reduced in patients, implying unimpaired non-visual contributions to pursuit drive. Disturbances of eye movement control in psychotic disorders appear to be a consequence of deficits in sensorimotor transformation rather than a pure failure in adding cognitive contributions to pursuit drive in higher-order cortical circuits. More generally, this deficit might reflect a fundamental imbalance between processing external input and acting according to internal preferences.

Temporal dynamics of retinal and extraretinal signals in the FEFsem during smooth pursuit eye movements

Neurons in the smooth eye movement subregion of the frontal eye field (FEFsem) are known to play an important role in voluntary smooth pursuit eye movements. Underlying this function are projections to parietal and prefrontal visual association areas and subcortical structures, all known to play vital but differing roles in the execution of smooth pursuit. Additionally, the FEFsem has been shown to carry a diverse array of signals (e.g., eye velocity, acceleration, gain control). We hypothesized that distinct subpopulations of FEFsem neurons subserve these diverse functions and projections, and that the relative weights of retinal and extraretinal signals could form the basis for categorization of units. To investigate this, we used a step-ramp tracking task with a target blink to determine the relative contributions of retinal and extraretinal signals in individual FEFsem neurons throughout pursuit. We found that the contributions of retinal and extraretinal signals to neuronal activity and behavior change throughout the time course of pursuit. A clustering algorithm revealed three distinct neuronal subpopulations: cluster 1 was defined by a higher sensitivity to eye velocity, acceleration, and retinal image motion; cluster 2 had greater activity during blinks; and cluster 3 had significantly greater eye position sensitivity. We also performed a comparison with a sample of medial superior temporal neurons to assess similarities and differences between the two areas. Our results indicate the utility of simple tests such as the target blink for parsing the complex and multifaceted roles of cortical areas in behavior.NEW & NOTEWORTHY The frontal eye field (FEF) is known to play a critical role in volitional smooth pursuit, carrying a variety of signals that are distributed throughout the brain. This study used a novel application of a target blink task during step ramp tracking to determine, in combination with a clustering algorithm, the relative contributions of retinal and extraretinal signals to FEF activity and the extent to which these contributions could form the basis for a categorization of neurons.

Orientation-dependent biases in length judgments of isolated stimuli

Vertical line segments tend to be perceived as longer than horizontal ones of the same length, but this may in part be due to configuration effects. To minimize such effects, we used isolated line segments in a two-interval, forced choice paradigm, not limiting ourselves to horizontal and vertical. We fitted psychometric curves using a Bayesian method that assumes that, for a given subject, the lapse rate is the same across all conditions. The closer a line segment's orientation was to vertical, the longer it was perceived to be. Moreover, subjects tended to report the standard line (in the second interval) as longer. The data were well described by a model that contains both an orientation-dependent and an interval-dependent multiplicative bias. Using this model, we estimated that a vertical line was on average perceived as 9.2% ± 2.1% longer than a horizontal line, and a second-interval line was on average perceived as 2.4% ± 0.9% longer than a first-interval line. Moving from a descriptive to an explanatory model, we hypothesized that anisotropy in the polar angle of lines in three dimensions underlies the horizontal-vertical illusion, specifically, that line segments more often have a polar angle of 90° (corresponding to the ground plane) than any other polar angle. This model qualitatively accounts not only for the empirical relationship between projected length and projected orientation that predicts the horizontal-vertical illusion, but also for the empirical distribution of projected orientation in photographs of natural scenes and for paradoxical results reported earlier for slanted surfaces.

The Role of Dopamine in Anticipatory Pursuit Eye Movements: Insights from Genetic Polymorphisms in Healthy Adults

There is a long history of eye movement research in patients with psychiatric diseases for which dysfunctions of neurotransmission are considered to be the major pathologic mechanism. However, neuromodulation of oculomotor control is still hardly understood. We aimed to investigate in particular the impact of dopamine on smooth pursuit eye movements. Systematic variability in dopaminergic transmission due to genetic polymorphisms in healthy subjects offers a noninvasive opportunity to determine functional associations. We measured smooth pursuit in 110 healthy subjects genotyped for two well-documented polymorphisms, the COMT Val¹⁵⁸Met polymorphism and the SLC6A3 3′-UTR-VNTR polymorphism. Pursuit paradigms were chosen to particularly assess the ability of the pursuit system to initiate tracking when target motion onset is blanked, reflecting the impact of extraretinal signals. In contrast, when following a fully visible target sensory, retinal signals are available. Our results highlight the crucial functional role of dopamine for anticipatory, but not for sensory-driven, pursuit processes. We found the COMT Val¹⁵⁸Met polymorphism specifically associated with anticipatory pursuit parameters, emphasizing the dominant impact of prefrontal dopamine activity on complex oculomotor control. In contrast, modulation of striatal dopamine activity by the SLC6A3 3′-UTR-VNTR polymorphism had no significant functional effect. Though often neglected so far, individual differences in healthy subjects provide a promising approach to uncovering functional mechanisms and can be used as a bridge to understanding deficits in patients.

The same oculomotor vermal Purkinje cells encode the different kinematics of saccades and of smooth pursuit eye movements

Saccades and smooth pursuit eye movements (SPEM) are two types of goal-directed eye movements whose kinematics differ profoundly, a fact that may have contributed to the notion that the underlying cerebellar substrates are separated. However, it is suggested that some Purkinje cells (PCs) in the oculomotor vermis (OMV) of monkey cerebellum may be involved in both saccades and SPEM, a puzzling finding in view of the different kinematic demands of the two types of eye movements. Such ‘dual’ OMV PCs might be oddities with little if any functional relevance. On the other hand, they might be representatives of a generic mechanism serving as common ground for saccades and SPEM. In our present study, we found that both saccade- and SPEM-related responses of individual PCs could be predicted well by linear combinations of eye acceleration, velocity and position. The relative weights of the contributions that these three kinematic parameters made depended on the type of eye movement. Whereas in the case of saccades eye position was the most important independent variable, it was velocity in the case of SPEM. This dissociation is in accordance with standard models of saccades and SPEM control which emphasize eye position and velocity respectively as the relevant controlled state variables.

Monocular and binocular smooth pursuit in central field loss

Macular degeneration results in heterogeneous central field loss (CFL) and often has asymmetrical effects in the two eyes. As such, it is not clear to what degree the movements of the two eyes are coordinated. To address this issue, we examined smooth pursuit quantitatively in CFL participants during binocular viewing and compared it to the monocular viewing case. We also examined coordination of the two eyes during smooth pursuit and how this coordination was affected by interocular ratios of acuity and contrast, as well as CFL-specific interocular differences, such as scotoma sizes and degree of binocular overlap. We hypothesized that the coordination of eye movements would depend on the binocularity of the two eyes. To test our hypotheses, we used a modified step-ramp paradigm, and measured pursuit in both eyes while viewing was binocular, or monocular with the dominant or non-dominant eye. Data for CFL participants and age-matched controls were examined at the group, within-group, and individual levels. We found that CFL participants had a broader range of smooth pursuit gains and a significantly lower correlation between the two eyes, as compared to controls. Across both CFL and control groups, smooth pursuit gain and correlation between the eyes are best predicted by the ratio of contrast sensitivity between the eyes. For the subgroup of participants with measurable stereopsis, both smooth pursuit gain and correlation are best predicted by stereoacuity. Therefore, our results suggest that coordination between the eyes during smooth pursuit depends on binocular cooperation between the eyes.

Switching between two targets with non-constant velocity profiles reveals shared internal model of target motion

Several experiments have shown that smooth pursuit and saccades interact while tracking an object moving across the visual scene. It was proposed two decades ago that the amplitude of saccades triggered during smooth pursuit ('catch-up saccades') were corrected by a delayed sensory signal to account for the ongoing target displacement during catch-up saccades. However, recent studies used targets with non-constant velocity profiles and suggested that the correction of catch-up saccade amplitude must be done through an internal model of target motion. It is widely accepted that an internal model of target motion is also used by the central nervous system (CNS) to cancel inherent delays between visual input and smooth pursuit motor output, ensuring accurate tracking of moving targets. Our study proposes a new paradigm in which the target switches unexpectedly from one target with a non-constant periodic velocity profile to another with a non-constant aperiodic velocity profile. Our results confirm the hypothesis that the CNS uses an internal model of target motion to correct catch-up saccade amplitude. In addition, we reconcile the sensory delayed and the internal model of target motion hypotheses and show that a common internal model of target motion is shared within the CNS to control smooth pursuit and to correct catch-up saccade amplitude.

Neural correlate of spatial (mis-)localization during smooth eye movements

The dependence of neuronal discharge on the position of the eyes in the orbit is a functional characteristic of many visual cortical areas of the macaque. It has been suggested that these eye-position signals provide relevant information for a coordinate transformation of visual signals into a non-eye-centered frame of reference. This transformation could be an integral part for achieving visual perceptual stability across eye movements. Previous studies demonstrated close to veridical eye-position decoding during stable fixation as well as characteristic erroneous decoding across saccadic eye-movements. Here we aimed to decode eye position during smooth pursuit. We recorded neural activity in macaque area VIP during steady fixation, saccades and smooth-pursuit and investigated the temporal and spatial accuracy of eye position as decoded from the neuronal discharges. Confirming previous results, the activity of the majority of neurons depended linearly on horizontal and vertical eye position. The application of a previously introduced computational approach (isofrequency decoding) allowed eye position decoding with considerable accuracy during steady fixation. We applied the same decoder on the activity of the same neurons during smooth-pursuit. On average, the decoded signal was leading the current eye position. A model combining this constant lead of the decoded eye position with a previously described attentional bias ahead of the pursuit target describes the asymmetric mislocalization pattern for briefly flashed stimuli during smooth pursuit eye movements as found in human behavioral studies.

Information fusion control with time delay for smooth pursuit eye movement

Smooth pursuit eye movement depends on prediction and learning, and is subject to time delays in the visual pathways. In this paper, an information fusion control method with time delay is presented, implementing smooth pursuit eye movement with prediction and learning as well as solving the problem of time delays in the visual pathways. By fusing the soft constraint information of the target trajectory of eyes and the ideal control strategy, and the hard constraint information of the eye system state equation and the output equation, optimal estimations of the co-state sequence and the control variable are obtained. The proposed control method can track not only constant velocity, sinusoidal target motion, but also arbitrary moving targets. Moreover, the absolute value of the retinal slip reaches steady state after 0.1 sec. Information fusion control method elegantly describes in a function manner how the brain may deal with arbitrary target velocities, how it implements the smooth pursuit eye movement with prediction, learning, and time delays. These two principles allowed us to accurately describe visually guided, predictive and learning smooth pursuit dynamics observed in a wide variety of tasks within a single theoretical framework. The tracking control performance of the proposed information fusion control with time delays is verified by numerical simulation results.

The visual representations of motion and of gravity are functionally independent: Evidence of a differential effect of smooth pursuit eye movements

The memory for the final position of a moving object which suddenly disappears has been found to be displaced forward, in the direction of motion, and downwards, in the direction of gravity. These phenomena were coined, respectively, Representational Momentum and Representational Gravity. Although both these and similar effects have been systematically linked with the functioning of internal representations of physical variables (e.g. momentum and gravity), serious doubts have been raised for a cognitively based interpretation, favouring instead a major role of oculomotor and perceptual factors which, more often than not, were left uncontrolled and even ignored. The present work aims to determine the degree to which Representational Momentum and Representational Gravity are epiphenomenal to smooth pursuit eye movements. Observers were required to indicate the offset locations of targets moving along systematically varied directions after a variable imposed retention interval. Each participant completed the task twice, varying the eye movements' instructions: gaze was either constrained or left free to track the targets. A Fourier decomposition analysis of the localization responses was used to disentangle both phenomena. The results show unambiguously that constraining eye movements significantly eliminates the harmonic components which index Representational Momentum, but have no effect on Representational Gravity or its time course. The found outcomes offer promising prospects for the study of the visual representation of gravity and its neurological substrates.

Dynamic causal modelling of eye movements during pursuit: Confirming precision-encoding in V1 using MEG

This paper shows that it is possible to estimate the subjective precision (inverse variance) of Bayesian beliefs during oculomotor pursuit. Subjects viewed a sinusoidal target, with or without random fluctuations in its motion. Eye trajectories and magnetoencephalographic (MEG) data were recorded concurrently. The target was periodically occluded, such that its reappearance caused a visual evoked response field (ERF). Dynamic causal modelling (DCM) was used to fit models of eye trajectories and the ERFs. The DCM for pursuit was based on predictive coding and active inference, and predicts subjects' eye movements based on their (subjective) Bayesian beliefs about target (and eye) motion. The precisions of these hierarchical beliefs can be inferred from behavioural (pursuit) data. The DCM for MEG data used an established biophysical model of neuronal activity that includes parameters for the gain of superficial pyramidal cells, which is thought to encode precision at the neuronal level. Previous studies (using DCM of pursuit data) suggest that noisy target motion increases subjective precision at the sensory level: i.e., subjects attend more to the target's sensory attributes. We compared (noisy motion-induced) changes in the synaptic gain based on the modelling of MEG data to changes in subjective precision estimated using the pursuit data. We demonstrate that imprecise target motion increases the gain of superficial pyramidal cells in V1 (across subjects). Furthermore, increases in sensory precision – inferred by our behavioural DCM – correlate with the increase in gain in V1, across subjects. This is a step towards a fully integrated model of brain computations, cortical responses and behaviour that may provide a useful clinical tool in conditions like schizophrenia.

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The brain encodes states of the world probabilistically with means and precisions.

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Precision (inverse variance) may be encoded by the synaptic gain of pyramidal cells.

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We estimate subjects' sensory precision using a model of oculomotor pursuit and DCM.

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We estimate subjects' synaptic gain in V1 using DCM of MEG data during pursuit.

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Estimates of synaptic gain in V1 and sensory precision are significantly correlated.

A foveal target increases catch-up saccade frequency during smooth pursuit

Images that move rapidly across the retina of the human eye blur because the retina has sluggish temporal dynamics. Voluntary smooth pursuit eye movements are modeled as matching object velocity to minimize retinal motion and prevent retinal blurring. However, "catch-up" saccades that are ubiquitous during pursuit interrupt it and disrupt clear vision. But catch-up saccades may not be a common feature of ocular pursuit, because their existence has been documented with a small moving spot, the classic pursuit stimulus, which is a weak motion stimulus that may poorly emulate larger pursuit objects. We found that spot pursuit does not generalize to that of larger objects. Observers pursued a spot or a larger virtual object with or without a superimposed spot target. Single-spot targets produced lower pursuit acceleration than larger objects. Critically, more saccadic intrusions occurred when stimuli had a central dot, even when position and velocity errors were equated, suggesting that catch-up saccades result from pursuing a single, small object or a feature on a large one. To determine what differentiates a large object from a small one, we progressively shrank the featureless virtual object and found that catch-up saccade frequency was highest when it fit in the fovea. The results suggest that pursuit of a small target or an object feature recruits a saccade mechanism that does not compensate for a weak motion signal; rather, the target compels foveation. Furthermore, catch-up saccades are likely generated by neural circuitry typically used to foveate small objects or features.

Reading from a Head-Fixed Display during Walking: Adverse Effects of Gaze Stabilization Mechanisms

Reading performance during standing and walking was assessed for information presented on earth-fixed and head-fixed displays by determining the minimal duration during which a numerical time stimulus needed to be presented for 50% correct naming answers. Reading from the earth-fixed display was comparable during standing and walking, with optimal performance being attained for visual character sizes in the range of 0.2° to 1°. Reading from the head-fixed display was impaired for small (0.2-0.3°) and large (5°) visual character sizes, especially during walking. Analysis of head and eye movements demonstrated that retinal slip was larger during walking than during standing, but remained within the functional acuity range when reading from the earth-fixed display. The detrimental effects on performance of reading from the head-fixed display during walking could be attributed to loss of acuity resulting from large retinal slip. Because walking activated the angular vestibulo-ocular reflex, the resulting compensatory eye movements acted to stabilize gaze on the information presented on the earth-fixed display but destabilized gaze from the information presented on the head-fixed display. We conclude that the gaze stabilization mechanisms that normally allow visual performance to be maintained during physical activity adversely affect reading performance when the information is presented on a display attached to the head.

Smooth pursuit preparation modulates neuronal responses in visual areas MT and MST

Primates are able to track small moving visual targets using smooth pursuit eye movements. Target motion for smooth pursuit is signaled by neurons in visual cortical areas MT and MST. In this study, we trained monkeys to either initiate or withhold smooth pursuit in the presence of a moving target to test whether this decision was reflected in the relative strength of "go" and "no-go" processes. We found that the gain of the motor response depended strongly on whether monkeys were instructed to initiate or withhold pursuit, thus demonstrating voluntary control of pursuit initiation. We found that the amplitude of the neuronal response to moving targets in areas MT and MST was also significantly lower on no-go trials (by 2.1 spikes/s on average). The magnitude of the neural response reduction was small compared with the behavioral gain reduction. There were no significant differences in neuronal direction selectivity, spatial selectivity, or response reliability related to pursuit initiation or the absence thereof. Variability in eye speed was negatively correlated with firing rate variability after target motion onset during go trials but not during no-go trials, suggesting that MT and MST activity represents an error signal for a negative feedback controller. We speculate that modulation of the visual motion signals in areas MT and MST may be one of the first visual cortical events in the initiation of smooth pursuit and that the small early response modulation may be amplified to produce an all-or-none motor response by downstream areas.

Motion dependence of smooth pursuit eye movements in the marmoset

Smooth pursuit eye movements stabilize slow-moving objects on the retina by matching eye velocity with target velocity. Two critical components are required to generate smooth pursuit: first, because it is a voluntary eye movement, the subject must select a target to pursue to engage the tracking system; and second, generating smooth pursuit requires a moving stimulus. We examined whether this behavior also exists in the common marmoset, a New World primate that is increasingly attracting attention as a genetic model for mental disease and systems neuroscience. We measured smooth pursuit in two marmosets, previously trained to perform fixation tasks, using the standard Rashbass step-ramp pursuit paradigm. We first measured the aspects of visual motion that drive pursuit eye movements. Smooth eye movements were in the same direction as target motion, indicating that pursuit was driven by target movement rather than by displacement. Both the open-loop acceleration and closed-loop eye velocity exhibited a linear relationship with target velocity for slow-moving targets, but this relationship declined for higher speeds. We next examined whether marmoset pursuit eye movements depend on an active engagement of the pursuit system by measuring smooth eye movements evoked by small perturbations of motion from fixation or during pursuit. Pursuit eye movements were much larger during pursuit than from fixation, indicating that pursuit is actively gated. Several practical advantages of the marmoset brain, including the accessibility of the middle temporal (MT) area and frontal eye fields at the cortical surface, merit its utilization for studying pursuit movements.

Active inference and oculomotor pursuit: the dynamic causal modelling of eye movements

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We use a normative (Bayes optimal) model of oculomotor pursuit.

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We average the empirical responses of subjects performing a pursuit paradigm.

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We invert these responses using the pursuit model and dynamic causal modelling.

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We thereby estimate the precision of subjects’ Bayesian beliefs from their pursuit.

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This could be used to quantify abnormal precision encoding in schizophrenia.

Background

This paper introduces a new paradigm that allows one to quantify the Bayesian beliefs evidenced by subjects during oculomotor pursuit. Subjects’ eye tracking responses to a partially occluded sinusoidal target were recorded non-invasively and averaged. These response averages were then analysed using dynamic causal modelling (DCM). In DCM, observed responses are modelled using biologically plausible generative or forward models – usually biophysical models of neuronal activity.

New method

Our key innovation is to use a generative model based on a normative (Bayes-optimal) model of active inference to model oculomotor pursuit in terms of subjects’ beliefs about how visual targets move and how their oculomotor system responds. Our aim here is to establish the face validity of the approach, by manipulating the content and precision of sensory information – and examining the ensuing changes in the subjects’ implicit beliefs. These beliefs are inferred from their eye movements using the normative model.

Results

We show that on average, subjects respond to an increase in the ‘noise’ of target motion by increasing sensory precision in their models of the target trajectory. In other words, they attend more to the sensory attributes of a noisier stimulus. Conversely, subjects only change kinetic parameters in their model but not precision, in response to increased target speed.

Conclusions

Using this technique one can estimate the precisions of subjects’ hierarchical Bayesian beliefs about target motion. We hope to apply this paradigm to subjects with schizophrenia, whose pursuit abnormalities may result from the abnormal encoding of precision.

The visibility of color breakup and a means to reduce it

Color breakup is an artifact seen on displays that present colors sequentially. When the eye tracks a moving object on such a display, different colors land on different places on the retina, and this gives rise to visible color fringes at the object's leading and trailing edges. Interestingly, color breakup is also observed when the eye is stationary and an object moves by. Using a novel psychophysical procedure, we measured breakup both when viewers tracked and did not track a moving object. Breakup was somewhat more visible in the tracking than in the non-tracking condition. The video frames contained three subframes, one each for red, green, and blue. We spatially offset the green and blue stimuli in the second and third subframes, respectively, to find the values that minimized breakup. In the tracking and non-tracking conditions, spatial offsets of Δx/3 in the second subframe (where Δx is the displacement of the object in one frame) and 2Δx/3 in the third eliminated breakup. Thus, this method offers a way to minimize or even eliminate breakup whether the viewer is tracking or not. We suggest ways to implement the method with real video content. We also developed a color-breakup model based on spatiotemporal filtering in color-opponent pathways in early vision. We found close agreement between the model's predictions and the experimental results. The model can be used to predict breakup for a wide variety of conditions.

Active inference, eye movements and oculomotor delays

This paper considers the problem of sensorimotor delays in the optimal control of (smooth) eye movements under uncertainty. Specifically, we consider delays in the visuo-oculomotor loop and their implications for active inference. Active inference uses a generalisation of Kalman filtering to provide Bayes optimal estimates of hidden states and action in generalised coordinates of motion. Representing hidden states in generalised coordinates provides a simple way of compensating for both sensory and oculomotor delays. The efficacy of this scheme is illustrated using neuronal simulations of pursuit initiation responses, with and without compensation. We then consider an extension of the generative model to simulate smooth pursuit eye movements-in which the visuo-oculomotor system believes both the target and its centre of gaze are attracted to a (hidden) point moving in the visual field. Finally, the generative model is equipped with a hierarchical structure, so that it can recognise and remember unseen (occluded) trajectories and emit anticipatory responses. These simulations speak to a straightforward and neurobiologically plausible solution to the generic problem of integrating information from different sources with different temporal delays and the particular difficulties encountered when a system-like the oculomotor system-tries to control its environment with delayed signals.

The neuronal basis of on-line visual control in smooth pursuit eye movements

Smooth pursuit eye movements allow us to maintain the image of a moving target on the fovea. Smooth pursuit consists of separate phases such as initiation and steady-state. These two phases are supported by different visual-motor mechanisms in cortical areas including the middle temporal (MT), the medial superior temporal (MST) areas and the frontal eye field (FEF). Retinal motion signals are responsible for beginning the process of pursuit initiation, whereas extraretinal signals play a role in maintaining tracking speed. Smooth pursuit often requires on-line gain adjustments during tracking in response to a sudden change in target motion. For example, a brief sinusoidal perturbation of target motion induces a corresponding perturbation of eye motion. Interestingly, the perturbation ocular response is enhanced when baseline pursuit velocity is higher, even though the stimulus frequency and amplitude are constant. This on-line gain control mechanism is not simply due to visually driven activity of cortical neurons. Visual and pursuit signals are primarily processed in cortical MT/MST and the magnitude of perturbation responses could be regulated by the internal gain parameter in FEF. Furthermore, the magnitude and the gain slope of perturbation responses are altered by smooth pursuit adaptation using repeated trials of a step-ramp tracking with two different velocities (double-velocity paradigm). Therefore, smooth pursuit adaptation, which is attributed to the cerebellar plasticity mechanism, could affect the on-line gain control mechanism.

Eye Velocity Gain Fields in MSTd During Optokinetic Stimulation

Lesion studies argue for an involvement of cortical area dorsal medial superior temporal area (MSTd) in the control of optokinetic response (OKR) eye movements to planar visual stimulation. Neural recordings during OKR suggested that MSTd neurons directly encode stimulus velocity. On the other hand, studies using radial visual flow together with voluntary smooth pursuit eye movements showed that visual motion responses were modulated by eye movement-related signals. Here, we investigated neural responses in MSTd during continuous optokinetic stimulation using an information-theoretic approach for characterizing neural tuning with high resolution. We show that the majority of MSTd neurons exhibit gain-field-like tuning functions rather than directly encoding one variable. Neural responses showed a large diversity of tuning to combinations of retinal and extraretinal input. Eye velocity-related activity was observed prior to the actual eye movements, reflecting an efference copy. The observed tuning functions resembled those emerging in a network model trained to perform summation of 2 population-coded signals. Together, our findings support the hypothesis that MSTd implements the visuomotor transformation from retinal to head-centered stimulus velocity signals for the control of OKR.

The effects of smooth pursuit adaptation on the gain of visuomotor transmission in monkeys

Smooth pursuit eye movements are supported by visual-motor systems, where visual motion information is transformed into eye movement commands. Adaptation of the visuomotor systems for smooth pursuit is an important factor to maintain pursuit accuracy and high acuity vision. Short-term adaptation of initial pursuit gain can be produced experimentally using by repeated trials of a step-ramp tracking with two different velocities (double-step paradigm) that step-up (10-30°/s) or step-down (20-5°/s). It is also known that visuomotor gain during smooth pursuit is regulated by a dynamic gain control mechanism by showing that eye velocity evoked by a target perturbation during pursuit increases bidirectionally when ongoing pursuit velocity is higher. However, it remains uncertain how smooth pursuit adaptation alters the gain of visuomotor transmission. Therefore, a single cycle of sinusoidal motion (2.5 Hz, ± 10°/s) was introduced during step-ramp tracking pre- and post-adaptation to determine whether smooth pursuit adaptation affects the perturbation response. The results showed that pursuit adaptation had a significant effect on the perturbation response that was specific to the adapted direction. These results indicate that there might be different visuomotor mechanisms between adaptation and dynamic gain control. Furthermore, smooth pursuit adaptation altered not only the gain of the perturbation response, but also the gain slope (regression curve) at different target velocities (5, 10 and 15°/s). Therefore, pursuit adaptation could affect the dynamic regulation of the visuomotor gain at different pursuit velocities.

Cerebellum-dependent motor learning: lessons from adaptation of eye movements in primates

In order to ameliorate the consequences of ego motion for vision, human and nonhuman observers generate reflexive, compensatory eye movements based on visual as well as vestibular information, helping to stabilize the images of visual scenes on the retina despite ego motion. And in order to fully exploit the advantages of foveal vision, they make saccades to shift the image of an object onto the fovea and smooth pursuit eye movements to stabilize it there despite continuing object movement relative to the observer. With the exception of slow visually driven eye movements, which can be understood as manifestations of relatively straightforward feedback systems, most eye movements require a direct conversion of sensory input into appropriate motor responses in the absence of immediate sensory feedback. Hence, in order to generate appropriate oculomotor responses, the parameters linking input and output must be chosen suitably. Moreover, as the parameters may change from one manifestation of a movement to the next, for instance because of oculomotor fatigue, the choices should also be quickly modifiable. This chapter will present evidence showing that this fast parametric optimization, understood as a functionally distinct example of motor learning, is an accomplishment of specific parts of the cerebellum devoted to the control of eye movements. It will also discuss recent electrophysiological results suggesting how this specific form of motor learning may emerge from information processing in cerebellar circuits.

Kalman filtering naturally accounts for visually guided and predictive smooth pursuit dynamics

The brain makes use of noisy sensory inputs to produce eye, head, or arm motion. In most instances, the brain combines this sensory information with predictions about future events. Here, we propose that Kalman filtering can account for the dynamics of both visually guided and predictive motor behaviors within one simple unifying mechanism. Our model relies on two Kalman filters: (1) one processing visual information about retinal input; and (2) one maintaining a dynamic internal memory of target motion. The outputs of both Kalman filters are then combined in a statistically optimal manner, i.e., weighted with respect to their reliability. The model was tested on data from several smooth pursuit experiments and reproduced all major characteristics of visually guided and predictive smooth pursuit. This contrasts with the common belief that anticipatory pursuit, pursuit maintenance during target blanking, and zero-lag pursuit of sinusoidally moving targets all result from different control systems. This is the first instance of a model integrating all aspects of pursuit dynamics within one coherent and simple model and without switching between different parallel mechanisms. Our model suggests that the brain circuitry generating a pursuit command might be simpler than previously believed and only implement the functional equivalents of two Kalman filters whose outputs are optimally combined. It provides a general framework of how the brain can combine continuous sensory information with a dynamic internal memory and transform it into motor commands.

Kinematic property of target motion conditions gaze behavior and eye-hand synergy during manual tracking

This study investigated how frequency demand and motion feedback influenced composite ocular movements and eye-hand synergy during manual tracking. Fourteen volunteers conducted slow and fast force-tracking in which targets were displayed in either line-mode or wave-mode to guide manual tracking with target movement of direct position or velocity nature. The results showed that eye-hand synergy was a selective response of spatiotemporal coupling conditional on target rate and feedback mode. Slow and line-mode tracking exhibited stronger eye-hand coupling than fast and wave-mode tracking. Both eye movement and manual action led the target signal during fast-tracking, while the latency of ocular navigation during slow-tracking depended on the feedback mode. Slow-tracking resulted in more saccadic responses and larger pursuit gains than fast-tracking. Line-mode tracking led to larger pursuit gains but fewer and shorter gaze fixations than wave-mode tracking. During slow-tracking, incidences of saccade and gaze fixation fluctuated across a target cycle, peaking at velocity maximum and the maximal curvature of target displacement, respectively. For line-mode tracking, the incidence of smooth pursuit was phase-dependent, peaking at velocity maximum as well. Manual behavior of slow or line-mode tracking was better predicted by composite eye movements than that of fast or wave-mode tracking. In conclusion, manual tracking relied on versatile visual strategies to perceive target movements of different kinematic properties, which suggested a flexible coordinative control for the ocular and manual sensorimotor systems.

Motion-based prediction explains the role of tracking in motion extrapolation

During normal viewing, the continuous stream of visual input is regularly interrupted, for instance by blinks of the eye. Despite these frequents blanks (that is the transient absence of a raw sensory source), the visual system is most often able to maintain a continuous representation of motion. For instance, it maintains the movement of the eye such as to stabilize the image of an object. This ability suggests the existence of a generic neural mechanism of motion extrapolation to deal with fragmented inputs. In this paper, we have modeled how the visual system may extrapolate the trajectory of an object during a blank using motion-based prediction. This implies that using a prior on the coherency of motion, the system may integrate previous motion information even in the absence of a stimulus. In order to compare with experimental results, we simulated tracking velocity responses. We found that the response of the motion integration process to a blanked trajectory pauses at the onset of the blank, but that it quickly recovers the information on the trajectory after reappearance. This is compatible with behavioral and neural observations on motion extrapolation. To understand these mechanisms, we have recorded the response of the model to a noisy stimulus. Crucially, we found that motion-based prediction acted at the global level as a gain control mechanism and that we could switch from a smooth regime to a binary tracking behavior where the dot is tracked or lost. Our results imply that a local prior implementing motion-based prediction is sufficient to explain a large range of neural and behavioral results at a more global level. We show that the tracking behavior deteriorates for sensory noise levels higher than a certain value, where motion coherency and predictability fail to hold longer. In particular, we found that motion-based prediction leads to the emergence of a tracking behavior only when enough information from the trajectory has been accumulated. Then, during tracking, trajectory estimation is robust to blanks even in the presence of relatively high levels of noise. Moreover, we found that tracking is necessary for motion extrapolation, this calls for further experimental work exploring the role of noise in motion extrapolation.

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.

Enhanced top-down control during pursuit eye tracking in schizophrenia

Alterations in sensorimotor processing and predictive mechanisms have both been proposed as the primary cause of eye tracking deficits in schizophrenia. 20 schizophrenia patients and 20 healthy controls were assessed on blocks of predictably moving visual targets at constant speeds of 10, 15 or 30°/s. To assess internal drive to the eye movement system based on predictions about the ongoing target movement, targets were blanked off for either 666 or 1,000 ms during the ongoing pursuit movement in additional conditions. Main parameters of interest were eye deceleration after extinction of the visual target and residual eye velocity during blanking intervals. Eye deceleration after target extinction, reflecting persistence of predictive signals, was slower in patients than in controls, implying greater rather than diminished utilization of predictive mechanisms for pursuit in schizophrenia. Further, residual gain was not impaired in patients indicating a basic integrity of internal predictive models. Pursuit velocity gain in patients was reduced in all conditions with visible targets replicating previous findings about a sensorimotor transformation deficit in schizophrenia. A pattern of slower eye deceleration and unimpaired residual gain during blanking intervals implies greater adherence to top-down predictive models for pursuit tracking in schizophrenia. This suggests that predictive modeling is relatively intact in schizophrenia and that the primary cause of abnormal visual pursuit is impaired sensorimotor transformation of the retinal error signal needed for the maintenance of accurate visually driven pursuit. This implies that disruption in extrastriate and sensorimotor systems rather than frontostriatal predictive mechanisms may underlie this widely reported endophenotypes for schizophrenia.

Cognitive processes involved in smooth pursuit eye movements: behavioral evidence, neural substrate and clinical correlation

Smooth-pursuit eye movements allow primates to track moving objects. Efficient pursuit requires appropriate target selection and predictive compensation for inherent processing delays. Prediction depends on expectation of future object motion, storage of motion information and use of extra-retinal mechanisms in addition to visual feedback. We present behavioral evidence of how cognitive processes are involved in predictive pursuit in normal humans and then describe neuronal responses in monkeys and behavioral responses in patients using a new technique to test these cognitive controls. The new technique examines the neural substrate of working memory and movement preparation for predictive pursuit by using a memory-based task in macaque monkeys trained to pursue (go) or not pursue (no-go) according to a go/no-go cue, in a direction based on memory of a previously presented visual motion display. Single-unit task-related neuronal activity was examined in medial superior temporal cortex (MST), supplementary eye fields (SEF), caudal frontal eye fields (FEF), cerebellar dorsal vermis lobules VI–VII, caudal fastigial nuclei (cFN), and floccular region. Neuronal activity reflecting working memory of visual motion direction and go/no-go selection was found predominantly in SEF, cerebellar dorsal vermis and cFN, whereas movement preparation related signals were found predominantly in caudal FEF and the same cerebellar areas. Chemical inactivation produced effects consistent with differences in signals represented in each area. When applied to patients with Parkinson's disease (PD), the task revealed deficits in movement preparation but not working memory. In contrast, patients with frontal cortical or cerebellar dysfunction had high error rates, suggesting impaired working memory. We show how neuronal activity may be explained by models of retinal and extra-retinal interaction in target selection and predictive control and thus aid understanding of underlying pathophysiology.

Self-monitoring of gaze in high functioning autism

Atypical visual behaviour has been recently proposed to account for much of social misunderstanding in autism. Using an eye-tracking system and a gaze-contingent lens display, the present study explores self-monitoring of eye motion in two conditions: free visual exploration and guided exploration via blurring the visual field except for the focal area of vision. During these conditions, thirteen students with High Functioning Autism Spectrum Disorders (HFASD) and fourteen typical individuals were presented naturalistic and interactive social stimuli using virtual reality. Fixation data showed a weaker modulation of eye movements according to the conditions in the HFASD group, thus suggesting impairments in self-monitoring of gaze. Moreover, the gaze-contingent lens induced a visual behaviour whereby social understanding scores were correlated with the time spent gazing at faces. The device could be useful for treating gaze monitoring deficiencies in HFASD.

Smooth pursuit and visual occlusion: active inference and oculomotor control in schizophrenia

This paper introduces a model of oculomotor control during the smooth pursuit of occluded visual targets. This model is based upon active inference, in which subjects try to minimise their (proprioceptive) prediction error based upon posterior beliefs about the hidden causes of their (exteroceptive) sensory input. Our model appeals to a single principle – the minimisation of variational free energy – to provide Bayes optimal solutions to the smooth pursuit problem. However, it tries to accommodate the cardinal features of smooth pursuit of partially occluded targets that have been observed empirically in normal subjects and schizophrenia. Specifically, we account for the ability of normal subjects to anticipate periodic target trajectories and emit pre-emptive smooth pursuit eye movements – prior to the emergence of a target from behind an occluder. Furthermore, we show that a single deficit in the postsynaptic gain of prediction error units (encoding the precision of posterior beliefs) can account for several features of smooth pursuit in schizophrenia: namely, a reduction in motor gain and anticipatory eye movements during visual occlusion, a paradoxical improvement in tracking unpredicted deviations from target trajectories and a failure to recognise and exploit regularities in the periodic motion of visual targets. This model will form the basis of subsequent (dynamic causal) models of empirical eye tracking measurements, which we hope to validate, using psychopharmacology and studies of schizophrenia.

Smooth-pursuit changes after the tenotomy and reattachment procedure for infantile nystagmus syndrome: model predictions and patient data

PURPOSE

Patients with infantile nystagmus syndrome (INS) often cannot quickly locate new visual targets or track moving objects. Dynamic demands on visual function are not measured by static measures (eg, visual acuity); they require time-sensitive measures. The authors investigated how dynamic properties of INS (pursuit-target acquisition times) were affected by the tenotomy and reattachment (T&R) procedure in both patients with INS and behavioral ocular motor system model predictions.

METHODS

Responses of 3 patients with different INS waveforms were compared before and after T&R to test the model's predictions. A high-speed digital video system was used to take eye-movement data. Human responses to target-ramp stimuli were analyzed.

RESULTS

T&R did not improve the smooth-pursuit responses of patients with INS; pursuit-target acquisition times did not show marked improvements. However, in one case, T&R allowed the patient to pursue targets "faster" in a specific direction.

CONCLUSION

T&R can improve peak visual acuity, broaden the high-acuity gaze-angle range, and reduce target acquisition times to static targets but not moving targets. When the target moves simultaneously with an ongoing saccade in the nystagmus cycle, the steady-state errors and elongated target acquisition times observed might be part of the intrinsic characteristics of normal pursuit responses.

Modulation of motor control in saccadic behaviors by TMS over the posterior parietal cortex

The right posterior parietal cortex (rPPC) has been found to be critical in shaping visual selection and distractor-induced saccade curvature in the context of predictive as well as nonpredictive visual cues by means of transcranial magnetic stimulation (TMS) interference. However, the dynamic details of how distractor-induced saccade curvatures are affected by rPPC TMS have not yet been investigated. This study aimed to elucidate the key dynamic properties that cause saccades to curve away from distractors with different degrees of curvature in various TMS and target predictability conditions. Stochastic optimal feedback control theory was used to model the dynamics of the TMS saccade data. This allowed estimation of torques, which was used to identify the critical dynamic mechanisms producing saccade curvature. The critical mechanisms of distractor-induced saccade curvatures were found to be the motor commands and torques in the transverse direction. When an unpredictable saccade target occurred with rPPC TMS, there was an initial period of greater distractor-induced torque toward the side opposite the distractor in the transverse direction, immediately followed by a relatively long period of recovery torque that brought the deviated trace back toward the target. The results imply that the mechanisms of distractor-induced saccade curvature may be comprised of two mechanisms: the first causing the initial deviation and the second bringing the deviated trace back toward the target. The pattern of the initial torque in the transverse direction revealed the former mechanism. Conversely, the later mechanism could be well explained as a consequence of the control policy in this model. To summarize, rPPC TMS increased the initial torque away from the distractor as well as the recovery torque toward the target.

Optimal control of natural eye-head movements minimizes the impact of noise

When shifting gaze to foveate a new target, humans mostly choose a unique set of eye and head movements from an infinite number of possible combinations. This stereotypy suggests that a general principle governs the movement choice. Here, we show that minimizing the impact of uncertainty, i.e., noise affecting motor performance, can account for the choice of combined eye-head movements. This optimization criterion predicts all major features of natural eye-head movements-including the part where gaze is already on target and the eye counter-rotates-such as movement durations, relative eye-head contributions, velocity profiles, and the dependency of gaze shifts on initial eye position. As a critical test of this principle, we show that it also correctly predicts changes in eye and head movement imposed by an experimental increase in the head moment of inertia. This suggests that minimizing the impact of noise is a simple and powerful principle that explains the choice of a unique set of movement profiles and segment coordination in goal-directed action.

Neural dynamics of saccadic and smooth pursuit eye movement coordination during visual tracking of unpredictably moving targets

How does the brain coordinate saccadic and smooth pursuit eye movements to track objects that move in unpredictable directions and speeds? Saccadic eye movements rapidly foveate peripheral visual or auditory targets, and smooth pursuit eye movements keep the fovea pointed toward an attended moving target. Analyses of tracking data in monkeys and humans reveal systematic deviations from predictions of the simplest model of saccade-pursuit interactions, which would use no interactions other than common target selection and recruitment of shared motoneurons. Instead, saccadic and smooth pursuit movements cooperate to cancel errors of gaze position and velocity, and thus to maximize target visibility through time. How are these two systems coordinated to promote visual localization and identification of moving targets? How are saccades calibrated to correctly foveate a target despite its continued motion during the saccade? The neural model proposed here answers these questions. Modeled interactions encompass motion processing areas MT, MST, FPA, DLPN and NRTP; saccade planning and execution areas FEF, LIP, and SC; the saccadic generator in the brain stem; and the cerebellum. Simulations illustrate the model's ability to functionally explain and quantitatively simulate anatomical, neurophysiological and behavioral data about coordinated saccade-pursuit tracking.

Flexible interpretation of a decision rule by supplementary eye field neurons

Since the environment is in constant flux, decision-making capabilities of the brain must be rapid and flexible. Yet in sensory motion processing pathways of the primate brain where decision making has been extensively studied, the flexibility of neurons is limited by inherent selectivity to motion direction and speed. The supplementary eye field (SEF), an area involved in decision making on moving stimuli, is not strictly a sensory or motor structure, and hence may not suffer such limitations. Here we test whether neurons in the SEF can flexibly interpret the rule of a go/nogo task when the decision boundary in the task changes with each trial. The task rule specified that the animal pursue a moving target with its eyes if and when the target entered a visible zone. The size of the zone was changed from trial to trial in order to shift the decision boundary, and thereby assign different go/nogo significance to the same motion trajectories. Individual SEF neurons interpreted the rule appropriately, signaling go or nogo in compliance with the rule and not the direction of motion. The results provide the first evidence that individual neurons in frontal cortex can flexibly interpret a rule that governs the decision to act.

Encoding of smooth-pursuit eye movement initiation by a population of vermal Purkinje cells

Lesion studies suggest that the oculomotor vermis (OMV) is critical for the initiation of smooth-pursuit eye movements (SPEMs); yet, its specific role has remained elusive. In this study, we tested the hypothesis that vermal Purkinje cells (PCs) may be needed to fine-tune the kinematic description of SPEM initiation. Recording from identified PCs from the monkey OMV, we observed that SPEM-related PCs were characterized by a formidable diversity of response profiles with typically only modest reflection of eye movement kinematics. In contrast, the PC population discharge could be perfectly predicted based on a linear combination of eye acceleration, velocity, and position. This finding is in full accord with a role of the OMV in shaping eye movement kinematics. It, moreover, supports the notion that this shaping action is based on a population code, whose anatomic basis is the convergence of PCs on target neurons in the cerebellar nuclei.

Quick phases control ocular torsion during smooth pursuit

One of the open questions in oculomotor control of visually guided eye movements is whether it is possible to smoothly track a target along a curvilinear path across the visual field without changing the torsional stance of the eye. We show in an experimental study of three-dimensional eye movements in subhuman primates (Macaca mulatta) that although the pursuit system is able to smoothly change the orbital orientation of the eye's rotation axis, the smooth ocular motion was interrupted every few hundred milliseconds by a small quick phase with amplitude <1.5° while the animal tracked a target along a circle or ellipse. Specifically, during circular pursuit of targets moving at different angular eccentricities (5°, 10°, and 15°) relative to straight ahead at spatial frequencies of 0.067 and 0.1 Hz, the torsional amplitude of the intervening quick phases was typically around 1° or smaller and changed direction for clockwise vs. counterclockwise tracking. Reverse computations of the eye rotation based on the recorded angular eye velocity showed that the quick phases facilitate the overall control of ocular orientation in the roll plane, thereby minimizing torsional disturbances of the visual field. On the basis of a detailed kinematic analysis, we suggest that quick phases during curvilinear smooth tracking serve to minimize deviations from Donders' law, which are inevitable due to the spherical configuration space of smooth eye movements.

The influence of cues and stimulus history on the non-linear frequency characteristics of the pursuit response to randomized target motion

When humans pursue motion stimuli composed of alternating constant velocity segments of randomised duration (RD), they nevertheless initiate anticipatory eye deceleration before stimulus direction changes at a pre-programmed time based on averaging prior stimulus timing. We investigated, in both the time and frequency domains, how averaging interacts with deceleration cues by comparing responses to stimuli composed of segments that were either constant-velocity ramps or half-cycle sinusoids. RDs were randomized within 6 ranges, each comprising 8 RDs and having differing mean RD. In sine responses, deceleration cues could be used to modulate eye velocity for long-range stimuli (RD = 840-1,200 ms) but in the shortest range (RD = 240-660 ms) cues became ineffective, so that sine responses resembled ramp responses, and anticipatory timing was primarily dependent on averaging. Additionally, inclusion of short duration (240 ms) segments reduced peak eye velocity for all RDs within a range, even when longer RDs in the range (up to 1,080 ms) would normally elicit much higher velocities. These effects could be attributed to antagonistic interactions between visually driven pursuit components and pre-programmed anticipatory deceleration components. In the frequency domain, the changes in peak velocity and anticipatory timing with RD range were translated into non-linear gain and phase characteristics similar to those evoked by sum-of-sines stimuli. Notably, a reduction in pursuit gain occurred when high-frequency components associated with short duration segments were present. Results appear consistent with an adapted pursuit model, in which pre-programmed timing information derived from an internally reconstructed stimulus signal is stored in short-term memory and controls the initiation of predictive responses.

Inactivation and stimulation of the frontal pursuit area change pursuit metrics without affecting pursuit target selection

The frontal pursuit area (FPA) lies posterior to the frontal eye fields in the frontal cortex and contains neurons that are directionally selective for pursuit eye movements. Lesions of the FPA (alternately called "FEFsem") cause deficits in pursuit acceleration and velocity, which are largest for movements directed toward the lesioned side. Conversely, stimulation of the FPA evokes pursuit from fixation and increases the gain of the pursuit response. On the basis of these properties, it has been hypothesized that the FPA could underlie the selection of pursuit direction. To test this possibility, we manipulated FPA activity and measured the effect on target selection behavior in rhesus monkeys. First, we unilaterally inactivated the FPA with the GABA agonist muscimol. We then measured the monkeys' performance on a pursuit-choice task. Second, we applied microstimulation unilaterally to the FPA during pursuit initiation while monkeys performed the same pursuit-choice task. Both of these manipulations produced significant effects on pursuit metrics; the inactivation decreased pursuit velocity and acceleration, and microstimulation evoked pursuit directly. Despite these changes, both manipulations failed to significantly alter choice behavior. These results show that FPA activity is not necessary for pursuit target selection.

Neural activity in the frontal pursuit area does not underlie pursuit target selection

The frontal pursuit area (FPA) contains neurons that are directionally selective for pursuit eye-movements. We found that FPA neurons discriminate target from distracter too late to account for pursuit directional selection. Rather, the timing of neuronal discrimination is linked to pursuit onset, suggesting a role in motor execution. We also found buildup of activity of FPA neurons prior to pursuit onset that correlated with eye acceleration. These results show that the FPA is unlikely to be involved in selection of initial pursuit direction, but could be involved in motor preparation by increasing pursuit gain prior to pursuit onset.

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.

Extraction of visual motion information for the control of eye and head movement during head-free pursuit

We investigated how effectively briefly presented visual motion could be assimilated and used to track future target motion with head and eyes during target disappearance. Without vision, continuation of eye and head movement is controlled by internal (extra-retinal) mechanisms, but head movement stimulates compensatory vestibulo-ocular reflex (VOR) responses that must be countermanded for gaze to remain in the direction of target motion. We used target exposures of 50–200 ms at the start of randomised step-ramp stimuli, followed by >400 ms of target disappearance, to investigate the ability to sample target velocity and subsequently generate internally controlled responses. Subjects could appropriately grade gaze velocity to different target velocities without visual feedback, but responses were fully developed only when exposure was >100 ms. Gaze velocities were sustained or even increased during target disappearance, especially when there was expectation of target reappearance, but they were always less than for controls, where the target was continuously visible. Gaze velocity remained in the direction of target motion throughout target extinction, implying that compensatory (VOR) responses were suppressed by internal drive mechanisms. Regression analysis revealed that the underlying compensatory response remained active, but with gain slightly less than unity (0.85), resulting in head-free gaze responses that were very similar to, but slightly greater than, head-fixed. The sampled velocity information was also used to grade head velocity, but in contrast to gaze, head velocity was similar whether the target was briefly or continuously presented, suggesting that head motion was controlled by internal mechanisms alone, without direct influence of visual feedback.