Interaction of visual and non-visual signals in the initiation of smooth pursuit eye movements in primates

Handedness Matters for Motor Control But Not for Prediction

Skilled motor behavior relies on the ability to control the body and to predict the sensory consequences of this control. Although there is ample evidence that manual dexterity depends on handedness, it remains unclear whether control and prediction are similarly impacted. To address this issue, right-handed human participants performed two tasks with either the right or the left hand. In the first task, participants had to move a cursor with their hand so as to track a target that followed a quasi-random trajectory. This hand-tracking task allowed testing the ability to control the hand along an imposed trajectory. In the second task, participants had to track with their eyes a target that was self-moved through voluntary hand motion. This eye-tracking task allowed testing the ability to predict the visual consequences of hand movements. As expected, results showed that hand tracking was more accurate with the right hand than with the left hand. In contrast, eye tracking was similar in terms of spatial and temporal gaze attributes whether the target was moved by the right or the left hand. Although these results extend previous evidence for different levels of control by the two hands, they show that the ability to predict the visual consequences of self-generated actions does not depend on handedness. We propose that the greater dexterity exhibited by the dominant hand in many motor tasks stems from advantages in control, not in prediction. Finally, these findings support the notion that prediction and control are distinct processes.

Robot End Effector Tracking Using Predictive Multisensory Integration

We propose a biologically inspired model that enables a humanoid robot to learn how to track its end effector by integrating visual and proprioceptive cues as it interacts with the environment. A key novel feature of this model is the incorporation of sensorimotor prediction, where the robot predicts the sensory consequences of its current body motion as measured by proprioceptive feedback. The robot develops the ability to perform smooth pursuit-like eye movements to track its hand, both in the presence and absence of visual input, and to track exteroceptive visual motions. Our framework makes a number of advances over past work. First, our model does not require a fiducial marker to indicate the robot hand explicitly. Second, it does not require the forward kinematics of the robot arm to be known. Third, it does not depend upon pre-defined visual feature descriptors. These are learned during interaction with the environment. We demonstrate that the use of prediction in multisensory integration enables the agent to incorporate the information from proprioceptive and visual cues better. The proposed model has properties that are qualitatively similar to the characteristics of human eye-hand coordination.

Eye Tracking of Occluded Self-Moved Targets: Role of Haptic Feedback and Hand-Target Dynamics

Previous studies on smooth pursuit eye movements have shown that humans can continue to track the position of their hand, or a target controlled by the hand, after it is occluded, thereby demonstrating that arm motor commands contribute to the prediction of target motion driving pursuit eye movements. Here, we investigated this predictive mechanism by manipulating both the complexity of the hand-target mapping and the provision of haptic feedback. Two hand-target mappings were used, either a rigid (simple) one in which hand and target motion matched perfectly or a nonrigid (complex) one in which the target behaved as a mass attached to the hand by means of a spring. Target animation was obtained by asking participants to oscillate a lightweight robotic device that provided (or not) haptic feedback consistent with the target dynamics. Results showed that as long as 7 s after target occlusion, smooth pursuit continued to be the main contributor to total eye displacement (∼60%). However, the accuracy of eye-tracking varied substantially across experimental conditions. In general, eye-tracking was less accurate under the nonrigid mapping, as reflected by higher positional and velocity errors. Interestingly, haptic feedback helped to reduce the detrimental effects of target occlusion when participants used the nonrigid mapping, but not when they used the rigid one. Overall, we conclude that the ability to maintain smooth pursuit in the absence of visual information can extend to complex hand-target mappings, but the provision of haptic feedback is critical for the maintenance of accurate eye-tracking performance.

Role of motor execution in the ocular tracking of self-generated movements

When human observers track the movements of their own hand with their gaze, the eyes can start moving before the finger (i.e., anticipatory smooth pursuit). The signals driving anticipation could come from motor commands during finger motor execution or from motor intention and decision processes associated with self-initiated movements. For the present study, we built a mechanical device that could move a visual target either in the same direction as the participant's hand or in the opposite direction. Gaze pursuit of the target showed stronger anticipation if it moved in the same direction as the hand compared with the opposite direction, as evidenced by decreased pursuit latency, increased positional lead of the eye relative to target, increased pursuit gain, decreased saccade rate, and decreased delay at the movement reversal. Some degree of anticipation occurred for incongruent pursuit, indicating that there is a role for higher-level movement prediction in pursuit anticipation. The fact that anticipation was larger when target and finger moved in the same direction provides evidence for a direct coupling between finger and eye motor commands.

Eye tracking a self-moved target with complex hand-target dynamics

Previous work has shown that the ability to track with the eye a moving target is substantially improved when the target is self-moved by the subject's hand compared with when being externally moved. Here, we explored a situation in which the mapping between hand movement and target motion was perturbed by simulating an elastic relationship between the hand and target. Our objective was to determine whether the predictive mechanisms driving eye-hand coordination could be updated to accommodate this complex hand-target dynamics. To fully appreciate the behavioral effects of this perturbation, we compared eye tracking performance when self-moving a target with a rigid mapping (simple) and a spring mapping as well as when the subject tracked target trajectories that he/she had previously generated when using the rigid or spring mapping. Concerning the rigid mapping, our results confirmed that smooth pursuit was more accurate when the target was self-moved than externally moved. In contrast, with the spring mapping, eye tracking had initially similar low spatial accuracy (though shorter temporal lag) in the self versus externally moved conditions. However, within ∼5 min of practice, smooth pursuit improved in the self-moved spring condition, up to a level similar to the self-moved rigid condition. Subsequently, when the mapping unexpectedly switched from spring to rigid, the eye initially followed the expected target trajectory and not the real one, thereby suggesting that subjects used an internal representation of the new hand-target dynamics. Overall, these results emphasize the stunning adaptability of smooth pursuit when self-maneuvering objects with complex dynamics.

Temporal evolution of 2-dimensional direction signals used to guide eye movements

The smooth pursuit system must integrate many local motion measurements into a coherent estimate of target velocity. Several laboratories have studied this integration process using eye movements elicited by targets, such as tilted bars, containing conflicts between local motion signals measured along contours [one dimensional (1D)] and those measured at the bar's endpoints, or terminators [two dimensional (2D)]. The general finding is that 1D signals dominate early responses, whereas later components of the behavior are determined by 2D signals. We studied the dynamics of the integration process in macaque monkeys by systematically varying the relative proportions of 1D and 2D signals and the retinal eccentricities at which they appeared. Predictably, longer bars produced greater and longer-lasting contour-induced deviations. The evolution of the 2D response occurred over a period of 50-400 ms, depending on the relative proportions of 1D and 2D signals. As contours were displaced from the fovea the deviation decreased but much less so for early (1st 40 ms) than for late (subsequent 40 ms) pursuit initiation. These bottom-up effects could be overcome to a limited extent by the top-down influence of predictability. Finally, we observed that when animals were free to track any part of the bar, they spontaneously made short-latency saccades to the terminators on most trials, especially when the bars were tilted. This suggests an increased saliency of moving terminators, particularly when discrepancies exist among local motion signals.

Role of arm proprioception in calibrating the arm-eye temporal coordination

When subjects track with the eyes an arm-attached target, eye latency is shorter than when tracking an external target. This improved synchrony could result from either a common command addressed to the two systems or from an influence of the arm command on eye motion initiation. According to the first hypothesis, the eyes should start moving long before the arm, because of the difference in dynamics. We recorded arm and eye motion together with biceps muscle activity in controls and a deafferented subject. Data support the second hypothesis. Moreover, the deafferented subject showed a lesser correlation between arm and eye motions than controls, suggesting a role for arm proprioception in the calibration of the temporal relationship between arm and eye movements.

Effects of stationary and moving textured backgrounds on the visuo-oculo-manual tracking in humans

We investigated the effects of stationary and moving textured backgrounds on ocular and manual pursuit of a discrete target that suddenly starts to move at constant speed (ramp motion). When a stationary textured background was superimposed to the target displacement, the gain of the steady-state eye smooth pursuit velocity was significantly reduced, while the latency of pursuit initiation did not vary significantly, as compared to a dark background condition. The initial velocity of the eye smooth pursuit was also lowered. Both the initial acceleration and the steady-state manual tracking angular velocity were slightly, but not significantly, lowered when compared to a dark background condition. Detrimental effects of the stationary textured background were of comparable amplitude (approximately 10%) for ocular and manual pursuit. In a second condition, we compared ocular and manual pursuit when the textured background was either stationary or drifting. Initial and steady-state eye velocities increased when the textured background moved in the same direction as the target. Conversely, when the background moved in the opposite direction, both velocities were decreased. Eye displacement gain remained however close to unity due to an increase in the occurrence of catch-up corrective saccades. The effects of the moving backgrounds on the initial and steady-state forearm velocities were inverse to that reported for smooth pursuit eye movements. Neither manual nor ocular smooth pursuit latencies were affected.