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.
15Action-stopping is a canonical executive function thought to involve top-down control 16 over the motor system. Here we aimed to validate this stopping system using high temporal 17 resolution methods in humans. We show that, following the requirement to stop, there was an 18 increase of right frontal beta (~13 to 30 Hz) at ~120 ms, likely a proxy of right inferior frontal 19 gyrus; then, at 140 ms, there was a broad skeletomotor suppression, likely reflecting the impact 20 of the subthalamic nucleus on basal ganglia output; then, at ~160 ms, suppression was detected 21 in the muscle, and, finally, the behavioral time of stopping was ~220 ms. This temporal cascade 22 confirms a detailed model of action-stopping, and partitions it into subprocesses that are isolable 23 to different nodes and are more precise than the behavioral speed of stopping. Variation in these 24 subprocesses, including at the single-trial level, could better explain individual differences in 25 impulse control. 26 27 28The ability to control one's actions and thoughts is important for our daily lives; for 29 example: changing gait when there is an obstacle in the path 1 , resisting the temptation to eat 30 when on a diet 2 , overcoming the tendency to say something hurtful 3 . While many processes 31 contribute to such forms of control, one important process is response inhibition -the prefrontal 32 (top-down) stopping of initiated response tendencies 4 . In the laboratory, response inhibition is 33 often studied with the stop-signal task 5 . On each trial, the participant initiates a motor response, 34 and then, when a subsequent Stop signal occurs, tries to stop. From the behavioral data one can 35 estimate a latent variable; the speed of stopping known as Stop Signal Reaction Time (SSRT), 36 which is typically 200-250 ms in healthy adults 5 . SSRT has been useful in neuropsychiatry 37where it is often longer for patients vs. controls [6][7][8][9][10][11] . The task has also provided a rich test-bed, 38 3 across species, for mapping out a putative neural architecture of prefrontal-basal-ganglia-regions 39 for rapidly suppressing motor output areas 6,12,13 . Given this rich literature, this task is one of the 40 few paradigms included in the longitudinal Adolescent Brain Cognitive Development study 14 of 41 10,000 adolescents over 10 years. 42Against this background, a puzzle is that the relation between SSRT and 'real-world' 43 self-reported impulsivity is often weak 15-20 . One explanation is that SSRT may not accurately 44 index the brain's true stopping speed. Indeed, recent mathematical modelling of behavior during 45 the stop-signal task suggests that standard calculations of SSRT may overestimate the brain's 46 stopping speed by ~100 ms 15 [also see 21 ]. Further, in a recent study 22 , electromyographic (EMG) 47 recordings revealed an initial increase in EMG activity in response to the Go cue, followed by a 48 sudden decline at ~150 ms after the Stop signal. This decline in EMG could be because of the 49 Stop process 'kicking in'...
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.supervised learning | minimum-intervention principle | reaching | motor noise | motor control R edundancy is a ubiquitous property that renders biological systems robust to disruptions. Goal-directed movements also display redundancy because a given movement, such as touching one's nose, can be made in many different ways with different combinations of joint angles. Although redundancy generates flexibility, it also poses a fundamental problem for the motor system because a large component of motor variability is attributed to muscle noise (1). Thus, if muscles operated independently, the total noise (reflected in the trajectory) would be a summation of the noise due to the component parts. However, if the covariation of muscles or joints occurs such that the effect of individual muscle or joint variability is mitigated, it is possible to maintain acceptable levels of variability while maintaining a reasonable degree of redundancy that allows flexibility in behavior. Consistent with this view, it has been observed in a wide range of tasks (2-7) that variability is not eliminated but optimized (8-11) to accumulate in a task-relevant dimensions using a minimum-intervention principle (12). Such variability, that is a consequence of redundancy, can be quantified as an uncontrolled manifold (13-15) in which task-independent variability is constrained to a redundant subspace (or "uncontrolled manifold").Although minimizing variability is expected to improve taskrelated performance, recent evidence suggests that motor variability paradoxically helps in motor learning (16)(17)(18)(19)(20). Such findings are supported by ideas in reinforcement learning (21,22), which suggest that baseline variability increases exploration, which in turn facilitates learning. Interestingly, motor variability has also been shown to help learning during supervised error-based learning tasks (16), suggesting a more general role of variability in motor learning. In this study, we tested whether variability arising ...
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.
Stopping actions 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 if 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 1Hz repetitive transcranial magnetic stimulation (rTMS) to disrupt the rIFG, and to test if this eroded 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 also slowed SSRTs and reduced beta. Part of this effect, however, resulted from carry-over of real stimulation in participants who received real first. A post-hoc between-group comparison of those participants who received real first compared to those who received sham first showed that real stimulation eroded 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.
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