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.     

Manuo-ocular coordination in target tracking. I. A model simulating human performance

During eye tracking of a self-moved target, human subjects' performance differs from eye-alone tracking of an external target. Typical latency between target and eye motion onsets is shorter, ocular smooth pursuit (SP) saturation velocity increases and the maximum target motion frequency at which the SP system functions correctly is higher. Based on a previous qualitative model, a quantitative model of the coordination control between the arm motor system and the SP system is presented and evaluated here. The model structure maintains a high level of parallelism with the physiological system. It contains three main parts: the eye motor control (containing a SP branch and a saccadic branch), the arm motor control and the coordination control. The coordination control is achieved via an exchange of information between the arm and the eye sensorimotor systems, mediated by sensory signals (vision, proprioception) and motor command copy. This cross-talk results in improved SP system performance. The model has been computer simulated and the results have been compared with human subjects' behavior observed during previous experiments. The model performance is seen to quantitatively fit data on human subjects. Received: 6 March 1997 / Accepted in revised form: 15 July 1997     

Effects of Attention to Auditory Motion on Cortical Activations during Smooth Pursuit Eye Tracking

Background

In contrast to traditional views that consider smooth pursuit as a relatively automatic process, evidence has been reported for the importance of attention for accurate pursuit performance. However, the exact role that attention might play in the maintenance of pursuit remains unclear.

Methodology/Principal Findings

We analysed the neuronal activity associated with healthy subjects executing smooth pursuit eye movements (SPEM) during concurrent attentive tracking of a moving sound source, which was either in-phase or in antiphase to the executed eye movements. Assuming that attentional resources must be allocated to the moving sound source, the simultaneous execution of SPEM and auditory tracking in diverging directions should result in increased load on common attentional resources. By using an auditory stimulus as a distractor rather then a visual stimulus we guaranteed that cortical activity cannot be caused by conflicts between two simultaneous visual motion stimuli. Our results revealed that the smooth pursuit task with divided attention led to significantly higher activations bilaterally in the posterior parietal cortex and lateral and medial frontal cortex, presumably containing the parietal, frontal and supplementary eye fields respectively.

Conclusions

The additional cortical activation in these areas is apparently due to the process of dividing attention between the execution of SPEM and the covert tracking of the auditory target. On the other hand, even though attention had to be divided the attentional resources did not seem to be exhausted, since the identification of the direction of the auditory target and the quality of SPEM were unaffected by the congruence between visual and auditory motion stimuli. Finally, we found that this form of task-related attention modulated not only the cortical pursuit network in general but also affected modality specific and supramodal attention regions.     

Learned timing of motor behavior in the smooth eye movement region of the frontal eye fields

Proper timing is a critical aspect of motor learning. We report a relationship between a representation of time and an expression of learned timing in neurons in the smooth eye movement region of the frontal eye fields (FEF(SEM)). During prelearning pursuit of target motion at a constant velocity, each FEF(SEM) neuron is most active at a distinct time relative to the onset of pursuit tracking. In response to an instructive change in target direction, a neuron expresses the most learning when the instruction occurs near the time of its maximal participation in prelearning pursuit. Different neurons are most active, and undergo the most learning, at distinct times during pursuit. We suggest that the representation of time in the FEF(SEM) drives learning that is temporally linked to an instructive change in target motion, and that this may be a general function of motor areas of the cortex.     

A model of visually-guided smooth pursuit eye movements based on behavioral observations

We report a model that reproduces many of the behavioral properties of smooth pursuit eye movements. The model is a negative-feedback system that uses three parallel visual motion pathways to drive pursuit. The three visual pathways process image motion, defined as target motion with respect to the moving eye, and provide signals related to image velocity, image acceleration, and a transient that occurs at the onset of target motion. The three visual motion signals are summed and integrated to produce the eye velocity output of the model. The model reproduces the average eye velocity evoked by steps of target velocity in monkeys and humans and accounts for the variation among individual responses and subjects. When its motor pathways are expanded to include positive feedback of eye velocity and a switch, the model reproduces the exponential decay in eye velocity observed when a moving target stops. Manipulation of this expanded model can mimic the effects of stimulation and lesions in the arcuate pursuit area, the middle temporal visual area (MT), and the medial superior temporal visual area (MST).     

Eye–Target Synchronization in Mild Traumatic Brain-injured Patients

Eye–target synchronization is critical for effective smooth pursuit of a moving visual target. We apply the nonlinear dynamical technique of stochastic-phase synchronization to human visual pursuit of a moving target, in both normal and mild traumatic brain-injured (mTBI) patients. We observe significant fatigue effects in all subject populations, in which subjects synchronize better with the target during the first half of the trial than in the second half. The fatigue effect differed, however, between the normal and the mTBI populations and between old and young subpopulations of each group. In some cases, the younger (≤40 years old) normal subjects performed better than mTBI subjects and also better than older (>40 years old) normal subjects. Our results, however, suggest that further studies will be necessary before a standard of “normal” smooth pursuit synchronization can be developed.     

Incorporating Prediction in Models for Two-Dimensional Smooth Pursuit

A predictive component can contribute to the command signal for smooth pursuit. This is readily demonstrated by the fact that low frequency sinusoidal target motion can be tracked with zero time delay or even with a small lead. The objective of this study was to characterize the predictive contributions to pursuit tracking more precisely by developing analytical models for predictive smooth pursuit. Subjects tracked a small target moving in two dimensions. In the simplest case, the periodic target motion was composed of the sums of two sinusoidal motions (SS), along both the horizontal and the vertical axes. Motions following the same or similar paths, but having a richer spectral composition, were produced by having the target follow the same path but at a constant speed (CS), and by combining the horizontal SS velocity with the vertical CS velocity and vice versa. Several different quantitative models were evaluated. The predictive contribution to the eye tracking command signal could be modeled as a low-pass filtered target acceleration signal with a time delay. This predictive signal, when combined with retinal image velocity at the same time delay, as in classical models for the initiation of pursuit, gave a good fit to the data. The weighting of the predictive acceleration component was different in different experimental conditions, being largest when target motion was simplest, following the SS velocity profiles.     

New methods for removing saccades in analysis of smooth pursuit eye movement

New computation methods for removing saccades in analysis of smooth pursuit eye movement characteristics were developed. They have removed saccades more completely than previous methods, and were very effective especially for noisy data recorded by the EOG method. The fully developed method was applicable to eye movement data in tracking of pseudo-random target movement as well as deterministic target movement. Furthermore, the methods were also useful for extracting the number and magnitudes of saccades more precisely.     

Tuning Properties of MT and MSTd and Divisive Interactions for Eye-Movement Compensation

The primate brain intelligently processes visual information from the world as the eyes move constantly. The brain must take into account visual motion induced by eye movements, so that visual information about the outside world can be recovered. Certain neurons in the dorsal part of monkey medial superior temporal area (MSTd) play an important role in integrating information about eye movements and visual motion. When a monkey tracks a moving target with its eyes, these neurons respond to visual motion as well as to smooth pursuit eye movements. Furthermore, the responses of some MSTd neurons to the motion of objects in the world are very similar during pursuit and during fixation, even though the visual information on the retina is altered by the pursuit eye movement. We call these neurons compensatory pursuit neurons. In this study we develop a computational model of MSTd compensatory pursuit neurons based on physiological data from single unit studies. Our model MSTd neurons can simulate the velocity tuning of monkey MSTd neurons. The model MSTd neurons also show the pursuit compensation property. We find that pursuit compensation can be achieved by divisive interaction between signals coding eye movements and signals coding visual motion. The model generates two implications that can be tested in future experiments: (1) compensatory pursuit neurons in MSTd should have the same direction preference for pursuit and retinal visual motion; (2) there should be non-compensatory pursuit neurons that show opposite preferred directions of pursuit and retinal visual motion.     

Motion Control in Saccade and Smooth Pursuit for Bionic Eye Based on Three-dimensional Coordinates

Saccade and smooth pursuit are two important functions of human eye.In order to enable bionic eye to imitate the two functions,a control method that implements saccade and smooth pursuit based on the three-dimensional coordinates of target is proposed.An optimal observation position is defined for bionic eye based on three-dimensional coordinates.A kind of motion planning method with high accuracy is developed.The motion parameters of stepper motor consisting of angle acceleration and turning time are computed according to the position deviation,the target's angular velocity and the stepper motor's current angular velocity in motion planning.The motors are controlled with the motion parameters moving to given position with desired angular velocity in schedule time.The experimental results show that the bionic eye can move to optimal observation positions in 0.6 s from initial location and the accuracy of 3D coordinates is improved.In addition,the bionic eye can track a target within the error of less than 20 pixels based on three-dimensional coordinates.It is verified that saccade and smooth pursuit of bionic eye based on three-dimensional coordinates are feasible.     

The reliability of eye movement quantitative evaluation. II. Smooth pursuit eye movements.

We adopted the estimate of the intraclass coefficient of reliability, R, to evaluate the reliability of smooth pursuit eye movement quantitative analysis. At a one-week interval, we recorded twice smooth pursuit eye movements from fifteen healthy subjects by means of the binocular electrooculographic technique. R was computed for the constant and the slope of the target velocity/gain relationships. R values were rated good for the slope and excellent for the constant. Finally, we computed for each parameter the maximum variability value according to two differing methods; on the basis of the within-subjects mean square values, we defined the normal range of biological test-retest variability for the two parameters.     

Manuo-ocular coordination in target tracking. II. Comparing the model with human behavior

Several studies have shown that humans track a moving visual target with their eyes better if the movement of this target is directly controlled by the observer's hand. The improvement in performance has been attributed to coordination control between the arm motor system and the smooth pursuit (SP) system. In such a task, the SP system shows characteristics that differ from those observed during eye-alone tracking: latency (between the target-arm and the eye motion onsets) is shorter, maximum SP velocity is higher and the maximum target motion frequency at which the SP can function effectively is also higher. The aim of this article is to qualitatively evaluate the behavior of a dynamical model simulating the oculomotor system and the arm motor system when both are involved in tracking visual targets. The evaluation is essentially based on a comparison of the behavior of the model with the behavior of human subjects tracking visual targets under different conditions. The model has been introduced and quantitatively evaluated in a companion paper. The model is based on an exchange of internal information between the two sensorimotor systems, mediated by sensory signals (vision, arm muscle proprioception) and motor signals (arm motor command copy). The exchange is achieved by a specialized structure of the central nervous system, previously identified as a part of the cerebellum. Computer simulation of the model yielded results that fit the behavior of human subjects observed during previously reported experiments, both qualitatively and quantitatively. The parallelism between physiology and human behavior on the one hand, and structure and simulation of the model on the other hand, is discussed. Received: 6 March 1997 / Accepted in revised form: 15 July 1997     

Frame of reference transformations in motion perception during smooth pursuit eye movements

Smooth pursuit eye movements change the retinal image velocity of objects in the visual field. In order to change from a retinocentric frame of reference into a head-centric one, the visual system has to take the eye movements into account. Studies on motion perception during smooth pursuit eye movements have measured either perceived speed or perceived direction during smooth pursuit to investigate this frame of reference transformation, but never both at the same time. We devised a new velocity matching task, in which participants matched both perceived speed and direction during fixation to that during pursuit. In Experiment 1, the velocity matches were determined for a range of stimulus directions, with the head-centric stimulus speed kept constant. In Experiment 2, the retinal stimulus speed was kept approximately constant, with the same range of stimulus directions. In both experiments, the velocity matches for all directions were shifted against the pursuit direction, suggesting an incomplete transformation of the frame of reference. The degree of compensation was approximately constant across stimulus direction. We fitted the classical linear model, the model of Turano and Massof (2001) and that of Freeman (2001) to the velocity matches. The model of Turano and Massof fitted the velocity matches best, but the differences between de model fits were quite small. Evaluation of the models and comparison to a few alternatives suggests that further specification of the potential effect of retinal image characteristics on the eye movement signal is needed.     

The effect of eye movement on the control of arm movement to a target

Abstract

The purpose of this study was to investigate the effect of eye movement on the control of arm movement to a target. Healthy humans flexed the elbow to a stationary target in response to a start tone. Simultaneously, the subject moved the eyes to the target (saccade eye movement), visually tracked a laser point moving with the arm (smooth pursuit eye movement), or gazed at a stationary start point at the midline of the horizontal visual angle (non-eye movement—NEM). Arm movement onset was delayed when saccade eye movement accompanied it. The onset of an electromyographic burst in the biceps muscle and the onset of saccade eye movement were almost simultaneous when both the arm and the eyes moved to the target. Arm movement duration during smooth pursuit eye movement was significantly longer than that during saccade eye movement or NEM. In spite of these findings, amplitudes of motor-evoked potential in the biceps and triceps brachii muscles were not significantly different among the eye movement conditions. These findings indicate that eye movement certainly affects the temporal control of arm movement, but may not affect corticospinal excitability in the arm muscles during arm movement.     

Saccadic adaptation to moving targets

Saccades are so called ballistic movements which are executed without online visual feedback. After each saccade the saccadic motor plan is modified in response to post-saccadic feedback with the mechanism of saccadic adaptation. The post-saccadic feedback is provided by the retinal position of the target after the saccade. If the target moves after the saccade, gaze may follow the moving target. In that case, the eyes are controlled by the pursuit system, a system that controls smooth eye movements. Although these two systems have in the past been considered as mostly independent, recent lines of research point towards many interactions between them. We were interested in the question if saccade amplitude adaptation is induced when the target moves smoothly after the saccade. Prior studies of saccadic adaptation have considered intra-saccadic target steps as learning signals. In the present study, the intra-saccadic target step of the McLaughlin paradigm of saccadic adaptation was replaced by target movement, and a post-saccadic pursuit of the target. We found that saccadic adaptation occurred in this situation, a further indication of an interaction of the saccadic system and the pursuit system with the aim of optimized eye movements.     

A ‘tachometer’ feedback model of smooth pursuit eye movements

A new model of smooth pursuit eye movements is presented. We begin by formally analyzing the stability of the proportional-derivative (PD) model of smooth pursuit eye movements using Pontryagin's theory. The PD model is the linearized version of the nonlinear Krauzlis-Lisberger (KL) model. We show that the PD model fails to account for the experimentally observed dependence of the eye velocity damping ratio and the oscillation period on the total delay in the feedback loop. To explain the data, a new `tachometer' feedback model, based on an efference copy signal of eye acceleration, is proposed and analyzed by computer simulation. The model predicts some salient features of monkey pursuit data and suggests a functional role for the extraretinal input to the medial superior temporal area (MST). Received: 9 February 1995 / Accepted in revised form: 13 June 1995     

Nonlinear parameter estimation applied to a model of smooth pursuit eye movements

We present a procedure that optimally adjusts specified parameters of a mathematical model to describe a set of measured data. The technique integrates a dynamic systems-simulation language with a robust algorithm for nonlinear parameter estimation, and it can be implemented on a microcomputer. Sensitivity functions are generated that indicate how the operation of the model is affected by each updated parameter. This procedure offers a greater resolution of optimal parameter values than other, less rigorous methods. To illustrate this technique we have applied it to the model of human smooth pursuit eye movements proposed by D.A. Robinson and colleagues (1986).     

Stochastic prediction in pursuit tracking: An experimental test of adaptive model theory

In this paper we test the proposition that in pursuit tracking, subjects compute stochastic (statistical) models of the temporal variations in position of the target and use these models to forecast target position for at least a response time interval into the future. A computer simulation of a human operator employing stochastic model prediction of target position is used to generate a synthetic pursuit tracking response signal. Actual pursuit tracking response signals are measured from 10 normal subjects using the same stimulus signal. Cross correlation and spectral analysis are employed to compute gain and phase frequency response characteristics for both synthetic and actual tracking data. The similarity of the gain and phase curves for synthetic and actual data provides compelling evidence in support of the proposition.     

Effects of vestibular and support afferentation upon visual pursuit in microgravity

Evaluation of the accuracy of eye turns (saccades) to fix a jerky pointed stimulus, and smooth pursuit of slow linear and sinusoidal movements of both pointed and optokinetic stimuli was performed in 31 cosmonauts on flight days 2-3, 5-8, 30, and once in one or two months of mission. An additional investigation of the eye pursuit function involved 10 cosmonauts, who, after testing during free floating, fulfilled stimulus tracking following a cycle of active head rotation, and 14 cosmonauts who received support afferentation. It was found that at the beginning of adaptation and periodically in the course of long mission, the systems of slow pursuit tracking adopted the strategy of saccadic approximation whereby gaze fixation was achieved through a sequence of macro- or microsaccadic movements. It was demonstrated that these disturbances, practically in all investigated cosmonauts, were consequent to the vestibular deprivation developing in microgravity. Vestibular afferentation produced by active head rotation improved characteristics of visual pursuit. Support deprivation also affects pursuit tracking by cosmonauts who form the concept of space orientation based on perception of their head and leg position. With support afferentation, these cosmonauts demonstrated improved visual pursuit characteristics.     

The representation of time for motor learning

We have identified factors that control precise motor timing by studying learning in smooth pursuit eye movements. Monkeys tracked a target that moved horizontally for a fixed time interval before changing direction through the addition of a vertical component of motion. After repeated presentations of the same target trajectory, infrequent probe trials of purely horizontal target motion evoked a vertical eye movement around the time when the change in target direction would have occurred. The pursuit system timed the vertical eye movement by keeping track of the duration of horizontal target motion and by measuring the distance the target traveled before changing direction, but not by learning the position in space where the target changed direction. We conclude that high temporal precision in motor output relies on multiple signals whose contributions to timing vary according to task requirements.