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Fast movements like saccadic eye movements that occur in the absence of sensory feedback are thought to be controlled by internal feedback. Such internal feedback provides an instantaneous estimate of the output, which serves as a proxy for sensory feedback, that can be used by the controller to correct deviations from the desired plan. In the predominant view, the desired plan/input is encoded in the form of a static displacement signal (endpoint model), believed to be encoded in the spatial map of the superior colliculus (SC). However, recent evidence has shown that SC neurons have a dynamic signal that correlates with saccade velocity, suggesting that information for velocity-based control is available for generating saccades. Motivated by this observation, we used a novel optimal control framework to test whether saccadic execution could be achieved by tracking a dynamic velocity signal at the input. We validated this velocity tracking model in a task where the peak saccade velocity was modulated by the speed of a concurrent hand movement independent of the saccade endpoint. A comparison showed that in this task, the velocity tracking model performed significantly better than the endpoint model. These results suggest that the saccadic system may have additional flexibility to incorporate a velocity-based internal feedback control when imposed by task goals or context.

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Mind wandering is a state in which our mental focus shifts toward task-unrelated thoughts. Although it is known that mind wandering has a detrimental effect on concurrent task performance (e.g., decreased accuracy), its effect on executive functions is poorly studied. Yet the latter question is relevant to many real-world situations, such as rapid stopping during driving. Here, we studied how mind wandering would affect the requirement to subsequently stop an incipient motor response. In healthy adults, we tested whether mind wandering affected stopping and, if so, which component of stopping was affected: the triggering of the inhibitory brake or the implementation of the brake following triggering. We observed that during mind wandering, stopping latency increased, as did the percentage of trials with failed triggering. Indeed, 67% of the variance of the increase in stopping latency was explained by increased trigger failures. Thus, mind wandering primarily affects stopping by affecting the triggering of the brake.

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Significant progress has been made in understanding the computational and neural mechanisms that mediate eye and hand movements made in isolation. However, less is known about the mechanisms that control these movements when they are coordinated. Here, we outline our computational approaches using accumulation-to-threshold and race-to-threshold models to elucidate the mechanisms that initiate and inhibit these movements. We suggest that, depending on the behavioral context, the initiation and inhibition of coordinated eye-hand movements can operate in two modes-coupled and decoupled. The coupled mode operates when the task context requires a tight coupling between the effectors; a common command initiates both effectors, and a unitary inhibitory process is responsible for stopping them. Conversely, the decoupled mode operates when the task context demands weaker coupling between the effectors; separate commands initiate the eye and hand, and separate inhibitory processes are responsible for stopping them. We hypothesize that the higher-order control processes assess the behavioral context and choose the most appropriate mode. This computational mechanism can explain the heterogeneous results observed across many studies that have investigated the control of coordinated eye-hand movements and may also serve as a general framework to understand the control of complex multi-effector movements.

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Stopping action depends on the integrity of the right inferior frontal gyrus (rIFG). Electrocorticography from the rIFG shows an increase in beta power during action stopping. Scalp EEG shows a similar right frontal beta increase, but it is unknown whether this beta modulation relates to the underlying rIFG network. Demonstrating a causal relationship between the rIFG and right frontal beta in EEG during action stopping is important for putting this electrophysiological marker on a firmer footing. In a double-blind study with a true sham coil, we used fMRI-guided 1-Hz repetitive transcranial magnetic stimulation (rTMS) to disrupt the rIFG and to test whether this reduced right frontal beta and impaired action stopping. We found that rTMS selectively slowed stop signal reaction time (SSRT) (no effect on Go) and reduced right frontal beta (no effect on sensorimotor mu/beta related to Go); it also reduced the variance of a single-trial muscle marker of stopping. Surprisingly, sham stimulation also slowed SSRTs and reduced beta. Part of this effect, however, resulted from carryover of real stimulation in participants who received real stimulation first. A post hoc between-group comparison of those participants who received real first compared with those who received sham first showed that real stimulation reduced beta significantly more. Thus, real rTMS uniquely affected metrics of stopping in the muscle and resulted in a stronger erosion of beta. We argue that this causal test validates right frontal beta as a functional marker of action stopping.NEW & NOTEWORTHY Action stopping recruits the right inferior frontal gyrus (rIFG) and elicits increases in right frontal beta. The present study now provides causal evidence linking these stopping-related beta oscillations to the integrity of the underlying rIFG network. One-hertz transcranial magnetic stimulation (TMS) over the rIFG impaired stopping and reduced right frontal beta during a stop-signal task. Furthermore, the effect on neural oscillations was specific to stopping-related beta, with no change in sensorimotor mu/beta corresponding to the Go response.

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Eye and hand movements are often made in isolation but for reaching movements they are usually coupled. Despite this, evidence for spatial coupling between the eye and hand effector is mixed and have usually been restricted to straight-line movements, while real-world hand movements have complex trajectories. Here, using a novel obstacle avoidance task where an obstacle appeared in an infrequent number of trials, we establish a stronger link between the saccade and hand trajectory during more naturalistic curved hand trajectories. We illustrate that the hand trajectory was coupled to the end-point of the saccade which was executed just prior to the hand movement onset. Interestingly, while the saccade end-point was related to whether the hand trajectory followed a straight or a curved path, the y-component of saccade end-point was related to whether the hand took a path passing from over or below the obstacle. Further, we observed a relationship between saccade locations and hand sub-movements where the number and timing of saccades and number of hand velocity peaks were related. These results illustrate a robust spatiotemporal and kinematic coupling between saccades and complex hand movement trajectories suggesting a shared kinematic representation underlying eye-hand movements.

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Action-stopping is a canonical executive function thought to involve top-down control over the motor system. Here we aimed to validate this stopping system using high temporal resolution methods in humans. We show that, following the requirement to stop, there was an increase of right frontal beta (~13 to 30 Hz) at ~120 ms, likely a proxy of right inferior frontal gyrus; then, at 140 ms, there was a broad skeletomotor suppression, likely reflecting the impact of the subthalamic nucleus on basal ganglia output; then, at ~160 ms, suppression was detected in the muscle, and, finally, the behavioral time of stopping was ~220 ms. This temporal cascade supports a physiological model of action-stopping, and partitions it into subprocesses that are isolable to different nodes and are more precise than the behavioral latency of stopping. Variation in these subprocesses, including at the single-trial level, could better explain individual differences in impulse control.

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Eye movement evaluation is vital for diagnosis of various ophthalmological and neurological disorders. The present study proposes a novel, noninvasive, wearable device to acquire the eye movement based on a Fiber Bragg Grating (FBG) Sensor. The proposed Fiber Bragg Grating Eye Tracker (FBGET) can capture the displacement of the eyeball during its movements in the form of strain variations on a cantilever. The muscular displacement generated by the eyeball over the lower eyelid, by its swiveling action while moving the gaze on a target object, is converted into strain variations on a cantilever. The developed FBGET is investigated for dynamic tracking of the eye-gaze movement for various actions of the eye such as fixations, saccades and main sequence. This approach was validated by recording the eye movement using the developed FBGET as well as conventional camera-based eye tracker methodology simultaneously. The experimental results demonstrate the feasibility and the real-time applicability of the proposed FBGET as an eye tracking device. In conclusion, the present study illustrates a novel methodology involving displacement of lower eyelid for eye tracking application along with the employment of FBG sensors to carry out the same. The proposed FBGET can be utilized in both clinical and hospital environment for diagnostic purposes owing to its advantages of wear-ability and ease of implementation making it a point of care device.

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Whereas inhibitory control of single effector movements has been widely studied, the control of coordinated eye-hand movements has received less attention. Nevertheless, previous studies have contradictorily suggested that either a common or separate signal/s is/are responsible for inhibition of coordinated eye-hand movements. In continuation of our previous study, we varied behavioral contexts and used a stochastic accumulation-to-threshold model, which predicts a scaling of the mean reaction time distribution with its variance, to study the inhibitory control of eye-hand movements. Participants performed eye-hand movements in different task conditions, and in each condition they had to redirect movements in a fraction of trials. Task contexts where the behavior could be best explained by a common initiation signal had similar error responses for eye and hand, despite having different mean reaction times, indicating a common inhibitory signal. In contrast, behavior that could be best explained by separate initiation signals had dissimilar error responses for eye and hand indicating separate inhibitory signals. These behavioral responses were further validated using electromyography and computational models having either a common or separate inhibitory control signal/s. Interestingly, in a particular context, whereas in majority trials a common initiation and inhibitory signal could explain the behavior, in a subset of trials separate initiation and inhibitory signals predicted the behavior better. This highlights the flexibility that exists in the brain and in effect reconciles the heterogeneous results reported by previous studies. NEW & NOTEWORTHY Prior studies have contradictorily suggested either a single or separate inhibitory signal/s underlying inhibition of coordinated eye-hand movements. With the use of different tasks, we observed that when eye-hand movements were initiated by a common signal, they were controlled by a common inhibitory signal. However, when the two effectors were initiated by separate signals, they were controlled by separate inhibitory signals. This highlights the flexible control of eye-hand movements and reconciles the heterogeneous results previously reported in the literature.

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In contrast to hand movements, the existence of a neural representation of saccade kinematics is unclear. Saccade kinematics is typically thought to be specified by motor error/desired displacement and generated by brain stem circuits that are not penetrable to voluntary control. We studied the influence of instructed hand movement velocity on the kinematics of saccades executed without explicit instructions. When the hand movement was slow the saccade velocity decreased, independent of saccade amplitude. We leveraged this modulation of saccade velocity to study the optimality of saccades (in terms of velocity and endpoint accuracy) in relation to the well-known speed-accuracy tradeoff that governs voluntary movements (Fitts' law). In contrast to hand movements that obeyed Fitts' law, normometric saccades exhibited the greatest endpoint accuracy and lower reaction times, relative to saccades accompanying slow and fast hand movements. In the slow condition, where saccade endpoint accuracy suffered, we observed that targets were more likely to be foveated by two saccades resulting in step-saccades. Interestingly, the endpoint accuracy was higher in two-saccade trials, compared with one-saccade trials in both the slow and fast conditions. This indicates that step-saccades are a part of the kinematic plan for optimal control of endpoint accuracy. Taken together, these findings suggest normometric saccades are already optimized to maximize endpoint accuracy and the modulation of saccade velocity by hand velocity is likely to reflect the sharing of kinematic plans between the two effectors.NEW & NOTEWORTHY The optimality of saccade kinematics has been suggested by modeling studies but experimental evidence is lacking. However, we observed that, when subjects voluntarily modulated their hand velocity, the velocity of saccades accompanying these hand movements was also modulated, suggesting a shared kinematic plan for eye and hand movements. We leveraged this modulation to show that saccades had less endpoint accuracy when their velocity decreased, illustrating that normometric saccades have optimal speed and accuracy.

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Eye and hand movements are initiated by anatomically separate regions in the brain, and yet these movements can be flexibly coupled and decoupled, depending on the need. The computational architecture that enables this flexible coupling of independent effectors is not understood. Here, we studied the computational architecture that enables flexible eye-hand coordination using a drift diffusion framework, which predicts that the variability of the reaction time (RT) distribution scales with its mean. We show that a common stochastic accumulator to threshold, followed by a noisy effector-dependent delay, explains eye-hand RT distributions and their correlation in a visual search task that required decision-making, while an interactive eye and hand accumulator model did not. In contrast, in an eye-hand dual task, an interactive model better predicted the observed correlations and RT distributions than a common accumulator model. Notably, these two models could only be distinguished on the basis of the variability and not the means of the predicted RT distributions. Additionally, signatures of separate initiation signals were also observed in a small fraction of trials in the visual search task, implying that these distinct computational architectures were not a manifestation of the task design per se. Taken together, our results suggest two unique computational architectures for eye-hand coordination, with task context biasing the brain toward instantiating one of the two architectures. NEW & NOTEWORTHY: Previous studies on eye-hand coordination have considered mainly the means of eye and hand reaction time (RT) distributions. Here, we leverage the approximately linear relationship between the mean and standard deviation of RT distributions, as predicted by the drift-diffusion model, to propose the existence of two distinct computational architectures underlying coordinated eye-hand movements. These architectures, for the first time, provide a computational basis for the flexible coupling between eye and hand movements.

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The number of joints and muscles in a human arm is more than what is required for reaching to a desired point in 3D space. Although previous studies have emphasized how such redundancy and the associated flexibility may play an important role in path planning, control of noise, and optimization of motion, whether and how redundancy might promote motor learning has not been investigated. In this work, we quantify redundancy space and investigate its significance and effect on motor learning. We propose that a larger redundancy space leads to faster learning across subjects. We observed this pattern in subjects learning novel kinematics (visuomotor adaptation) and dynamics (force-field adaptation). Interestingly, we also observed differences in the redundancy space between the dominant hand and nondominant hand that explained differences in the learning of dynamics. Taken together, these results provide support for the hypothesis that redundancy aids in motor learning and that the redundant component of motor variability is not noise.

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