Predicted Position Error Triggers Catch-Up Saccades during Sustained Smooth Pursuit

Predictive posture stabilization before contact with moving objects: equivalence of smooth pursuit tracking and peripheral vision

Postural stabilization is essential to effectively interact with our environment. Humans preemptively adjust their posture to counteract impending disturbances, such as those encountered during interactions with moving objects, a phenomenon known as anticipatory postural adjustments (APAs). APAs are thought to be influenced by predictive models that incorporate object motion via retinal motion and extraretinal signals. Building on our previous work that examined APAs in relation to the perceived momentum of moving objects, here we explored the impact of object motion within different visual field sectors on the human capacity to anticipate motion and prepare APAs for contact between virtual moving objects and the limb. Participants interacted with objects moving toward them under different gaze conditions. In one condition, participants fixated on either a central point (central fixation) or left-right of the moving object (peripheral fixation), whereas in another, they followed the moving object with smooth pursuit eye movements (SPEMs). We found that APAs had the smallest magnitude in the central fixation condition and that no notable differences in APAs were apparent between the SPEM and peripheral fixation conditions. This suggests that the visual system can accurately perceive motion of objects in peripheral vision for posture stabilization. Using Bayesian model averaging, we also evaluated the contribution of different gaze variables, such as eye velocity and gain (ratio of eye and object velocity) and showed that both eye velocity and gain signals were significant predictors of APAs. Taken together, our study underscores the roles of oculomotor signals in the modulation of APAs.NEW & NOTEWORTHY We show that the human visuomotor system can detect motion in peripheral vision and make anticipatory adjustments to posture before contact with moving objects, just as effectively as when the eye movement system tracks those objects with smooth pursuit eye movements. These findings pave the way for research into how age-induced changes in spatial vision, eye movements, and motion perception could affect the control of limb movements and postural stability during motion-mediated interactions with objects.

Multivariate EEG activity reflects the Bayesian integration and the integrated Galilean relative velocity of sensory motion during sensorimotor behavior

Humans integrate multiple sources of information for action-taking, using the reliability of each source to allocate weight to the data. This reliability-weighted information integration is a crucial property of Bayesian inference. In this study, participants were asked to perform a smooth pursuit eye movement task in which we independently manipulated the reliability of pursuit target motion and the direction-of-motion cue. Through an analysis of pursuit initiation and multivariate electroencephalography activity, we found neural and behavioral evidence of Bayesian information integration: more attraction toward the cue direction was generated when the target motion was weak and unreliable. Furthermore, using mathematical modeling, we found that the neural signature of Bayesian information integration had extra-retinal origins, although most of the multivariate electroencephalography activity patterns during pursuit were best correlated with the retinal velocity errors accumulated over time. Our results demonstrated neural implementation of Bayesian inference in human oculomotor behavior.

Gain Function-Based Visual Tracking Control for Inertial Stabilized Platform with Output Constraints and Disturbances

In this paper, a composite control strategy is proposed to deal with output constraints and disturbances of the visual tracking system for an inertial stabilized platform, which combines active disturbance compensation and the variable gain function technique. Firstly, the model of system considering multi-source disturbances is established, where the controlled output is the constrained position of the target in the image plane. Secondly, in order to avoid the tracked target being lost in the field of view of the camera, a control method based on the variable gain function technique is designed to ensure that the controlled output remains within the feasible range. Moreover, the active disturbance estimation and compensation method is introduced to improve the anti-disturbance ability of the system under the situation of small output error, obtaining satisfactory tracking performance. The stability analysis and the proof of constrained output are given following the controller design. Finally, results of simulation and experiments are shown to illustrate the promised advantages of the proposed composite control approach.

Properties of pursuit movements

This chapter describes dynamic properties of smooth pursuit, visual and non-visual stimuli for pursuit, smooth eye-head tracking movements, and plastic-adaptive properties of pursuit. Step-ramp visual stimulus motion has revealed important properties of pursuit, including the latency to onset, initial acceleration, accuracy, and transient oscillations-all features that have been used to develop models of the pursuit system, discussed in the chapter "Models of pursuit" by Robinson. The role of predictive neural mechanisms in generating pursuit movements that anticipate target motion, and that enable near-perfect tracking of sinusoidal target motion, are examined. Smooth pursuit can be generated in response to targets that do not move, such as stroboscopic lights and images stabilized in the periphery of vision. The view that, during combined eye-head pursuit, the pursuit signal is used to cancel the vestibulo-ocular reflex is an incomplete hypothesis, contradicted by behavioral and electrophysiological findings. Smooth pursuit shows adaptive capabilities, evident in individuals who develop extraocular muscle palsies.

Differential saccade-pursuit coordination under sleep loss and low-dose alcohol

Introduction

Ocular tracking of a moving object requires tight coordination between smooth pursuit and saccadic eye movements. Normally, pursuit drives gaze velocity to closely match target velocity, with residual position offsets corrected by catch-up saccades. However, how/if common stressors affect this coordination is largely unknown. This study seeks to elucidate the effects of acute and chronic sleep loss, and low-dose alcohol, on saccade-pursuit coordination, as well as that of caffeine.

Methods

We used an ocular tracking paradigm to assess three metrics of tracking (pursuit gain, saccade rate, saccade amplitude) and to compute "ground lost" (from reductions in steady-state pursuit gain) and "ground recouped" (from increases in steady-state saccade rate and/or amplitude). We emphasize that these are measures of relative changes in positional offsets, and not absolute offset from the fovea.

Results

Under low-dose alcohol and acute sleep loss, ground lost was similarly large. However, under the former, it was nearly completely recouped by saccades, whereas under the latter, compensation was at best partial. Under chronic sleep restriction and acute sleep loss with a caffeine countermeasure, the pursuit deficit was dramatically smaller, yet saccadic behavior remained altered from baseline. In particular, saccadic rate remained significantly elevated, despite the fact that ground lost was minimal.

Discussion

This constellation of findings demonstrates differential impacts on saccade-pursuit coordination with low-dose alcohol impacting only pursuit, likely through extrastriate cortical pathways, while acute sleep loss not only disrupts pursuit but also undermines saccadic compensation, likely through midbrain/brainstem pathways. Furthermore, while chronic sleep loss and caffeine-mitigated acute sleep loss show little residual pursuit deficit, consistent with uncompromised cortical visual processing, they nonetheless show an elevated saccade rate, suggesting residual midbrain and/or brainstem impacts.

A change in perspective: The interaction of saccadic and pursuit eye movements in oculomotor control and perception

Due to the close relationship between oculomotor behavior and visual processing, eye movements have been studied in many different areas of research over the last few decades. While these studies have brought interesting insights, specialization within each research area comes at the potential cost of a narrow and isolated view of the oculomotor system. In this review, we want to expand this perspective by looking at the interactions between the two most important types of voluntary eye movements: saccades and pursuit. Recent evidence indicates multiple interactions and shared signals at the behavioral and neurophysiological level for oculomotor control and for visual perception during pursuit and saccades. Oculomotor control seems to be based on shared position- and velocity-related information, which leads to multiple behavioral interactions and synergies. The distinction between position- and velocity-related information seems to be also present at the neurophysiological level. In addition, visual perception seems to be based on shared efferent signals about upcoming eye positions and velocities, which are to some degree independent of the actual oculomotor response. This review suggests an interactive perspective on the oculomotor system, based mainly on different types of sensory input, and less so on separate subsystems for saccadic or pursuit eye movements.

The role of eye movements in manual interception: A mini-review

When we catch a moving object in mid-flight, our eyes and hands are directed toward the object. Yet, the functional role of eye movements in guiding interceptive hand movements is not yet well understood. This review synthesizes emergent views on the importance of eye movements during manual interception with an emphasis on laboratory studies published since 2015. We discuss the role of eye movements in forming visual predictions about a moving object, and for enhancing the accuracy of interceptive hand movements through feedforward (extraretinal) and feedback (retinal) signals. We conclude by proposing a framework that defines the role of human eye movements for manual interception accuracy as a function of visual certainty and object motion predictability.

Oculomotor inhibition during smooth pursuit and its dependence on contrast sensitivity

Our eyes are never still, but tend to "freeze" in response to stimulus onset. This effect is termed "oculomotor inhibition" (OMI); its magnitude and time course depend on the stimulus parameters, attention, and expectation. We previously showed that the time course and duration of microsaccade and spontaneous eye-blink inhibition provide an involuntary measure of low-level visual properties such as contrast sensitivity during fixation. We investigated whether this stimulus-dependent inhibition also occurs during smooth pursuit, for both the catch-up saccades and the pursuit itself. Observers followed a target with continuous back-and-forth horizontal motion while a Gabor patch was briefly flashed centrally with varied spatial frequency and contrast. Catch-up saccades of the size of microsaccades had a similar pattern of inhibition as microsaccades during fixation, with stronger inhibition onset and faster inhibition release for more salient stimuli. Moreover, a similar stimulus dependency of inhibition was shown for pursuit latencies and peak velocity. Additionally, microsaccade latencies at inhibition release, peak pursuit velocities, and latencies at minimum pursuit velocity were correlated with contrast sensitivity. We demonstrated the generality of OMI to smooth pursuit for both microsaccades and the pursuit itself and its close relation to the low-level processes that define saliency, such as contrast sensitivity.

Confidence in predicted position error explains saccadic decisions during pursuit

A fundamental problem in motor control is the coordination of complementary movement types to achieve a common goal. As a common example, humans view moving objects through coordinated pursuit and saccadic eye movements. Pursuit is initiated and continuously controlled by retinal image velocity. During pursuit, eye position may lag behind the target. This can be compensated by the discrete execution of a catch-up saccade. The decision to trigger a saccade is influenced by both position and velocity errors, and the timing of saccades can be highly variable. The observed distributions of saccade frequency and trigger time remain poorly understood, and this decision process remains imprecisely quantified. Here, we propose a predictive, probabilistic model explaining the decision to trigger saccades during pursuit to foveate moving targets. In this model, expected position error and its associated uncertainty are predicted through Bayesian inference across noisy, delayed sensory observations (Kalman filtering). This probabilistic prediction is used to estimate the confidence that a saccade is needed (quantified through log-probability ratio), triggering a saccade upon accumulating to a fixed threshold. The model qualitatively explains behavioral observations on the frequency and trigger time distributions of saccades during pursuit over a range of target motion trajectories. Furthermore, this model makes novel predictions that saccade decisions are highly sensitive to uncertainty for small predicted position errors, but this influence diminishes as the magnitude of predicted position error increases. We suggest that this predictive, confidence-based decision-making strategy represents a fundamental principle for the probabilistic neural control of coordinated movements.NEW & NOTEWORTHY This is the first stochastic dynamical systems model of pursuit-saccade coordination accounting for noise and delays in the sensorimotor system. The model uses Bayesian inference to predictively estimate visual motion, triggering saccades when confidence in predicted position error accumulates to a threshold. This model explains saccade frequency and trigger time distributions across target trajectories and makes novel predictions about the influence of sensory uncertainty in saccade decisions during pursuit.