Flexible Switching of Feedback Control Mechanisms Allows for Learning of Different Task Dynamics

Abstract

To produce skilled movements, the brain flexibly adapts to different task requirements and movement contexts. Two core abilities underlie this flexibility. First, depending on the task, the motor system must rapidly switch the way it produces motor commands and how it corrects movements online, i.e. it switches between different (feedback) control policies. Second, it must also adapt to environmental changes for different tasks separately. Here we show these two abilities are related. In a bimanual movement task, we show that participants can switch on a movement-by-movement basis between two feedback control policies, depending only on a static visual cue. When this cue indicates that the hands control separate objects, reactions to force field perturbations of each arm are purely unilateral. In contrast, when the visual cue indicates a commonly controlled object, reactions are shared across hands. Participants are also able to learn different force fields associated with a visual cue. This is however only the case when the visual cue is associated with different feedback control policies. These results indicate that when the motor system can flexibly switch between different control policies, it is also able to adapt separately to the dynamics of different environmental contexts. In contrast, visual cues that are not associated with different control policies are not effective for learning different task dynamics.

Trial-to-Trial Reoptimization of Motor Behavior Due to Changes in Task Demands Is Limited

“…For instance, Knill and colleagues [2] considered that the feedforward component was unaffected by accuracy demands and that movement flexibility was determined by the adjustment of the feedback gains only [1], [2]. This trial-to-trial adjustment of the feedback controller has also been found in force-field adaptation tasks [30], [31]. The reoptimization of the control policy might be restricted to the feedback gains in the random schedule but not in the blocked schedule where the feedforward component could be reoptimized as well.…”

“…Learning two different sensorimotor transformations is very limited when the sensory input is identical [46]–[48]. However, when two different tasks that require two different control policies are used, the feedback control policy associated with each of the task can be adapted independently from the other [31], [49]. In this case, each of these feedback controllers can be adapted to a sensorimotor transformation that is in conflict with the other.…”

A switching cost for motor planning

“…Such disruption of performance due to randomization is widespread in motor tasks (Elliott and Allard, 1985;Edin et al, 1992;Horak and Diener, 1994;Khan et al, 2002;Pruszynski et al, 2008;Selen et al, 2009;Bennett et al, 2010;Afsanepurak et al, 2012) (but see White and Diedrichsen, 2013). For instance, in a recent study (Orban de Xivry, 2013), we asked participants to reach to either a narrow or wide target and found that participants reacted differently to a perturbation in function of the target width (compatible with the minimum intervention principle of optimal control, Todorov and Jordan, 2002) but unexpectedly also in function of the schedule (random or blocked).…”

“…While in the previous paper (Orban de Xivry, 2013), we merely reported the observation that randomly changing the width of the target affected the optimality of motor behavior, we present in this paper three experiments that provide an explanation for the absence of optimality in the random schedule and identify limits on the flexibility of motor planning. Importantly, in this study, like in many others (Ahmadi-Pajouh et al, 2012;Nashed et al, 2012;Dimitriou et al, 2013;Orban de Xivry, 2013;White and Diedrichsen, 2013), we consider that motor planning (i.e. before movement onset) consists in the derivation of a goaldirected feedback control law (Todorov and Jordan, 2002;Scott, 2004;Todorov, 2004;Liu and Todorov, 2007) and that responses to any perturbations during the movement are driven by this feedback law.…”

Neural model for learning-to-learn of novel task sets in the motor domain

“…Each novel contextual cue (e.g., handling a novel object) promotes the acquisition and the use of a distinct internal model that does not modify the existing neural representations used to control the limb on its own (White and Diedrichsen, 2013). Moreover, each task set is evaluated depending on the current dynamics and on the current goal we want to perform (Orban and Wolpert, 2011).…”