The role of target position in smooth pursuit deceleration and termination

Visualization and quantification of eye tracking data for the evaluation of oculomotor function

Oculomotor dysfunction may originate from physical, physiological or psychological causes and may be a marker for schizophrenia or other disorders. Observational tests for oculomotor dysfunction are easy to administer, but are subjective and transient, and it is difficult to quantify deviations. To date, video-based eye tracking systems have not provided a contextual overview of gaze data that integrates the eye video recording with the stimulus and gaze data together with quantitative feedback of metrics in relation to typical values. A system was developed with an interactive timeline to allow the analyst to scroll through a recording frame-by-frame while comparing data from three different sources. The visual and integrated nature of the analysis allows localisation and quantification of saccadic under- and overshoots as well as determination of the frequency and amplitude of catch-up and anticipatory saccades. Clinicians will be able to apply their expertise to diagnose disorders based on abnormal patterns in the gaze plots. They can use the line charts to quantify deviations from benchmark values for reaction time, saccadic accuracy and smooth pursuit gain. A clinician can refer to the eye video at any time to confirm that observed deviations originated from gaze behaviour and not from systemic errors.

Using Smooth Pursuit Calibration for Difficult-to-Calibrate Participants

Although the 45-dots calibration routine of a previous study ( 2) provided very good accuracy, it requires intense mental effort and the routine proved to be unsuccessful for young children who struggle to maintain concentration. The calibration procedures that are normally used for difficult-to-calibrate participants, such as autistic children and infants, do not suffice since they are not accurate enough and the reliability of research results might be jeopardised. Smooth pursuit has been used before for calibration and is applied in this paper as an alternative routine for participants who are difficult to calibrate with conventional routines. Gaze data is captured at regular intervals and many calibration targets are generated while the eyes are following a moving target. The procedure could take anything between 30 s and 60 s to complete, but since an interesting target and/or a conscious task may be used, participants are assisted to maintain concentration. It was proven that the accuracy that can be attained through calibration with a moving target along an even horizontal path is not significantly worse than the accura-cy that can be attained with a standard method of watching dots appearing in random order. The routine was applied successfully for a group of children with ADD, ADHD and learning abilities. This result is important as it provides for easier calibration - especially in the case of participants who struggle to keep their gaze focused and stable on a stationary target for long enough.

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'.

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.

Pursuit responses to target steps during ongoing tracking

Brief smooth eye-velocity responses to target position steps have been reported during smooth pursuit. We investigated position-error responses in eight healthy human subjects, comparing the effects of a step-ramp change in target position when imposed on steady-state smooth pursuit, vestibuloocular reflex (VOR) slow phases, or fixation. During steady-state pursuit or VOR, the target performed a step-ramp movement in the same or in the opposite direction relative to ongoing eye movements. When the step was directed backward relative to steady-state smooth pursuit, eye velocity transiently decreased (1.3 +/- 0.4 degrees /s; average peak change in amplitude +/- SD), beginning about 100 ms after the step. The amplitude of position-error responses varied inversely with the step size. In contrast, there was little or no response in trials with forward steps during steady-state smooth pursuit, when step-ramps were imposed on VOR or when smooth pursuit began from fixation. We hypothesize that during ongoing smooth tracking when a sudden shift in target position is detected the pursuit system compares the direction of ongoing eye velocity with the relative positional error on the retina. In the case of different relative directions between ongoing tracking and a new target eccentricity, a position-error response toward the new target is initiated. Such a mechanism might help the smooth pursuit system to respond better to changes in target direction. These experimental findings were simulated by a mathematical model of smooth pursuit by implementing direction-dependent behavior with a position-error gating mechanism.

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

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

Influence of target size on vertical gaze palsy in a pathologically proven case of progressive supranuclear palsy

We document a new oculomotor phenomenon in a patient with pathologically proven progressive supranuclear gaze palsy (PSP), namely that vertical gaze excursion improves with larger pursuit targets. We used computerised video-oculography during vertical smooth pursuit eye movements (SPEM) of circular targets of diameter 0.16 degrees and 16 degrees, sinusoidally oscillating at 0.08 Hz (peak-to-peak amplitude 49 degrees). Increasing target size improved vertical gaze excursion from 10 degrees to 25 degrees. There was no concomitant increase in slow phase eye velocity. The findings could be explained by a potentiation of the position control mechanism of pursuit by target size due to increased activation of brainstem pursuit-optokinetic pathways and to higher order attentional mechanisms. This observation may be useful in the clinical assessment of PSP patients with severe neck rigidity in whom the doll's head-eye manoeuvre cannot be performed by comparing the degree of vertical gaze palsy during smooth pursuit testing between at least two differently sized targets and observing whether there is a larger excursion in response to a large target such as a newspaper.

Experimental and computational analysis of monkey smooth pursuit eye movements

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