Search

Humans consider the parameters linked to movement goal during reaching to adjust their control strategy online. Indeed, rapid changes in target structure or disturbances interfering with their initial plan elicit rapid changes in behavior. Here, we hypothesize that these changes could result from the continuous use of a decision variable combining motor and cognitive components. We combine an optimal feedback controller with a real-time monitoring of the expected cost-to-go, which considers target- and movement-related costs, in a common theoretical framework. This model reproduces human behaviors in presence of changes in the target structure occurring during movement and of online decisions to flexibly change target following external perturbations. It also predicts that the time taken to decide to select a novel goal after a perturbation depends on the amplitude of the disturbance and on the rewards of the different options, which is a direct result of the continuous monitoring of the cost-to-go. We show that this result was present in our previously collected dataset. Together our developments point towards a continuous monitoring of the cost-to-go during reaching to update control online and make efficient decisions about movement goal.Author summaryThe way humans perform reaching movements is compatible with models considering that they result from the minimization of a task-related cost function. However, these models typically assume a cost function that does not change within movement, which is incompatible with experimental findings highlighting humans’ ability to adjust reaching control online and change target flexibly. We hypothesized that this later ability relied on the cost-to-go, which integrates task- and body-related information, being evaluated continuously during movement. We show that this model can optimally select and adjust control during movement in a way that reproduces human behavior in a set of tasks involving change in cost function and change in goal target. Our model predicted that decision-time to change target must be postponed when limb displacements and alternative rewards are smaller, which was borne out in our previous experimental dataset. To conclude, our model explains dynamic updates in reach control and suggests the cost-to-go as decision variable linking decision-making and motor control.

Target reward influences motor planning strategies through modulation of movement vigor. Considering current theories of sensorimotor control suggesting that movement planning consists in selecting a goal-directed control strategy, we sought to investigate the influence of reward on feedback control. Here, we explored this question in three human reaching experiments. First, we altered the explicit reward associated with the goal target and found an overall increase in feedback gains for higher target rewards, highlighted by larger velocities, feedback responses to external loads, and background muscle activity. Then, we investigated whether the differences in target rewards across multiple goals impacted rapid motor decisions during movement. We observed idiosyncratic switching strategies dependent on both target rewards and, surprisingly, the feedback gains at perturbation onset: the more vigorous movements were less likely to switch to a new goal following perturbations. To gain further insight into a causal influence of the feedback gains on rapid motor decisions, we demonstrated that biasing the baseline activity and reflex gains by means of a background load evoked a larger proportion of target switches in the direction opposite to the background load associated with lower muscle activity. Together, our results demonstrate an impact of target reward on feedback control and highlight the competition between movement vigor and flexibility.

Humans are able to perform very sophisticated reaching movements in a myriad of contexts based on flexible control strategies influenced by the task goal and environmental constraints such as obstacles. However, it remains unknown whether these control strategies can be adjusted online. The objective of this study was to determine whether the factors which determine control strategies during planning also modify the execution of an ongoing movement following sudden changes in task demand. More precisely, we investigated whether, and at which latency, feedback responses to perturbation loads followed the change in the structure of the goal target or environment. We changed the target width (square or rectangle) to alter the task redundancy, or the presence of obstacles to induce different constraints on the reach path, and assessed based on surface recordings when the change in visual display altered the feedback response to mechanical perturbations. Task-related EMG responses were detected within 150 ms of a change in target shape. Considering visuomotor delays of ∼ 100 ms, these results suggest that it takes 50 ms to change control policy within a trial. An additional 30 ms delay was observed when the change in context involved sudden appearance or disappearance of obstacles. Overall, our results demonstrate that the control policy within a reaching movement is not static: contextual factors which influence movement planning also influence movement execution at surprisingly short latencies. Moreover, the additional 30 ms associated with obstacles suggest that these two types of changes may be mediated via distinct processes.New & NoteworthyThe present work demonstrates that the control strategies used to perform reaching movements are adjusted online when the structure of the target or the presence of obstacles are altered during movements. Thus, the properties of goal-directed reaching control are not simply selected during the planning stage of a movement prior to execution. Rather, they are dynamically and rapidly adjusted online, within ∼150ms, according to changes in environment.

A hallmark of human reaching movements is that they are appropriately tuned to the task goal and to the environmental context. This was demonstrated by the way humans flexibly respond to mechanical and visual perturbations that happen during movement. Furthermore, it was previously showed that the properties of goal-directed control can change within a movement, following abrupt changes in the goal structure. Such online adjustment was characterized by a modulation of feedback gains following switches in target shape. However, it remains unknown whether the underlying mechanism merely switches between prespecified policies, or whether it results from continuous and potentially dynamic adjustments. Here, we address this question by investigating participants’ feedback control strategies in presence of various changes in target width during reaching. More specifically, we studied whether the feedback responses to mechanical perturbations were sensitive to the rate of change in target width, which would be inconsistent with the hypothesis of a single, discrete switch. Based on movement kinematics and surface EMG data, we observed a modulation of feedback response clearly dependent on dynamical changes in target width. Together, our results demonstrate a continuous and online transformation of task-related parameters into suitable control policies.Significance statementHumans can adjust their control policy online in response to changes in the goal structure. However, it was unknown whether this adjustment resulted from a switch between two policies, or from dynamic and continuous adjustments. To address this question, we investigated whether online adjustments were tuned to dynamic changes in goal target which varied at different rates. Our results demonstrated that online adjustments were tuned to the rate of change in target width, suggesting that human reaching control policies are derived based on continuous monitoring of task-related parameters supporting online and dynamic adjustments.