An independent race model involving an abort and re-plan strategy explains reach redirecting movements during planning and execution

Failing to attend versus failing to stop: Single-trial decomposition of action-stopping in the stop signal task

The capacity to stop impending or ongoing actions contributes to executive control over behavior. Action-stopping, however, is difficult to directly quantify. It is therefore assayed via computational modeling of behavior in the stop signal task to estimate the latency of stopping (stop signal reaction time, SSRT) and, more recently, the reliability of stopping in terms of the distribution of SSRTs (standard deviation, SD-SSRT) and the frequency with which one outright fails to react to a stop signal (trigger failures, TF). Critically, the validity of computational estimates remains unknown because we currently have no direct readouts of behavior against which to compare them. Here, we developed a method for providing single-trial behavioral readouts of SSRT and trigger failures. The method relies on an adaptation of the stop signal task in which participants respond by moving a computer mouse. In two online experiments, we used movement kinematics to quantify stopping performance (SSRT, SD-SSRT, and TF), and then applied the standard Race Model and recent BEESTS model in order to examine the convergent validity of the methods. Overall, we demonstrate good correspondence between kinematics- and model-based estimates of stopping performance at the group and individual level. We conclude that the new method provides valid estimates of stopping performance that, unlike model-based estimates, can be read out at the level of single trials. Our approach might therefore be useful for interrogating single-trial neurophysiological correlates of stopping and for large-scale, online studies of behavioral stopping.

Preparatory activity links the frontal eye field response with small amplitude motor unit recruitment of neck muscles during gaze planning

A hallmark of intelligent behavior is that we can separate intention from action. To understand the mechanism that gates the flow of information between motor planning and execution, we compared the activity of frontal eye field neurons with motor unit activity from neck muscles in the presence of an intervening delay period in which spatial information regarding the target was available to plan a response. Although spatially specific delay period activity was present in the activity of frontal eye field neurons, it was absent in motor unit activity. Nonetheless, motor unit activity was correlated with the time it took to initiate saccades. Interestingly, we observed a heterogeneity of responses among motor units, such that only units with smaller amplitudes showed a clear modulation during the delay period. These small amplitude motor units also had higher spontaneous activity compared with the units which showed modulation only during the movement epoch. Taken together, our results suggest the activity of smaller motor units convey temporal information and explains how the delay period primes muscle activity leading to faster reaction times.NEW & NOTEWORTHY This study shows that the temporal aspects of a motor plan in the oculomotor circuitry can be accessed by peripheral neck muscles hundreds of milliseconds before the instruction to initiate a saccadic eye movement. The coupling between central and peripheral processes during the delay time is mediated by the recruitment pattern of motor units with smaller amplitude. These findings suggest that information processed in cortical areas could be read from periphery before execution.

Online Movement Correction in Response to the Unexpectedly Perturbed Initial or Final Action Goals: An ERP and sLORETA Study

In this experiment, we explored how unexpected perturbations in the initial (grip posture) and the final action goals (target position) influence movement execution and the neural mechanisms underlying the movement corrections. Participants were instructed to grasp a handle and rotate it to a target position according to a given visual cue. After participants started their movements, a secondary cue was triggered, which indicated whether the initial or final goals had changed (or not) while the electroencephalogram (EEG) was recorded. The results showed that the perturbed initial goals significantly slowed down the reaching action, compared to the perturbed final goals. In the event-related potentials (ERPs), a larger anterior P3 and a larger central-distributed late positivity (600–700 ms) time-locked to the perturbations were found for the initial than for the final goal perturbations. Source analyses found stronger left middle frontal gyrus (MFG) activations for the perturbed initial goals than for the perturbed final goals in the P3 time window. These findings suggest that perturbations in the initial goals have stronger interferences with the execution of grasp-to-rotate movements than perturbations in the final goals. The interferences seem to be derived from both inappropriate action inhibitions and new action implementations during the movement correction.

Task context determines whether common or separate inhibitory signals underlie the control of eye-hand movements

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.

Distinct mechanisms explain the control of reach speed planning: evidence from a race model framework

Previous studies have investigated the computational architecture underlying the voluntary control of reach movements that demands a change in position or direction of movement planning. Here we used a novel task in which subjects had to either increase or decrease the movement speed according to a change in target color that occurred randomly during a trial. The applicability of different race models to such a speed redirect task was assessed. We found that the predictions of an independent race model that instantiated an abort-and-replan strategy was consistent with all aspects of performance in the fast-to-slow speed condition. The results from modeling indicated a peculiar asymmetry, in that although the fast-to-slow speed change required inhibition, none of the standard race models was able to explain how movements changed from slow to fast speeds. Interestingly, a weighted averaging model that simulated the gradual merging of two kinematic plans explained behavior in the slow-to-fast speed task. In summary, our work shows how a race model framework can provide an understanding of how the brain controls different aspects of reach movement planning and help distinguish between an abort-and-replan strategy and merging of plans. NEW & NOTEWORTHY For the first time, a race model framework was used to understand how reach speeds are modified. We provide evidence that a fast-to-slow speed change required aborting the current plan and a complete respecification of a new plan, while none of the race models was able to explain an instructed increase of hand movement speed, which was instead accomplished by a merging of a new kinematic plan with the existing kinematic plan.