Recent studies have demonstrated that task success signals can modulate learning during sensorimotor adaptation tasks, primarily through engaging explicit processes. Here, we examine the influence of task outcome on implicit adaptation, using a reaching task in which adaptation is induced by feedback that is not contingent on actual performance. We imposed an invariant perturbation (rotation) on the feedback cursor while varying the target size. In this way, the cursor either hit or missed the target, with the former producing a marked attenuation of implicit motor learning. We explored different computational architectures that might account for how task outcome information interacts with implicit adaptation. The results fail to support an architecture in which adaptation operates in parallel with a model-free operant reinforcement process. Rather, task outcome may serve as a gain on implicit adaptation or provide a distinct error signal for a second, independent implicit learning process.Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (<xref ref-type="decision-letter" rid="SA1">see decision letter</xref>).

Collecting data online via crowdsourcing platforms has proven to be a very efficient way to recruit a large and diverse sample. Studies of motor learning, however, have been largely confined to the lab due to the need for special equipment to record movement kinematics and, as such, are typically only accessible to specific participants (e.g., college students).As a first foray to make motor learning studies accessible to a larger and more diverse audience, we developed an online, web-based platform (OnPoint) to collect kinematic data, serving as a template for researchers to create their own online sensorimotor control and learning experiments.As a proof-of-concept, we asked if fundamental motor learning phenomena discovered in the lab could be replicated online. In a series of three experiments, we observed a close correspondence between the results obtained online with those previously reported from research conducted in the laboratory. This web-based platform paired with online crowdsourcing can serve as a powerful new method for the study of motor control and learning.

9Recent studies have demonstrated that task success signals can modulate learning during 10 sensorimotor adaptation tasks, primarily through engaging explicit processes. Here we examine the 11 influence of task outcome on implicit adaptation, using a reaching task in which adaptation is induced 12 by feedback that is not contingent on actual performance. We imposed an invariant perturbation 13 (rotation) on the feedback cursor while varying the target size. In this way, the cursor either hit or 14 missed the target, with the former producing a marked attenuation of implicit motor learning. We 15 explored different computational architectures that might account for how task outcome information 16 interacts with implicit adaptation. The results fail to support an architecture in which adaptation operates 17in parallel with a model-free operant reinforcement process. Rather, task outcome may serve as a gain 18 on implicit adaptation or provide a distinct error signal for a second model-based process, in addition to 19 implicit adaptation. 20

Implicit motor recalibration allows us to flexibly move in novel and changing environments. Conventionally, implicit recalibration is thought to be driven by errors in predicting the sensory outcome of movement (i.e., sensory prediction errors). However, recent studies have shown that implicit recalibration is also influenced by errors in achieving the movement goal (i.e., task errors). Exactly how sensory prediction errors and task errors interact to drive implicit recalibration and, in particular, whether task errors alone might be sufficient to drive implicit recalibration remain unknown. To test this, we induced task errors in the absence of sensory prediction errors by displacing the target mid-movement. We found that task errors alone failed to induce implicit recalibration. In additional experiments, we simultaneously varied the size of sensory prediction errors and task errors. We found that implicit recalibration driven by sensory prediction errors could be continuously modulated by task errors, revealing an unappreciated dependency between these two sources of error. Moreover, implicit recalibration was attenuated when the target was simply flickered in its original location, even though this manipulation did not affect task error – an effect likely attributed to attention being directed away from the feedback cursor. Taken as a whole, the results were accounted for by a computational model in which sensory prediction errors and task errors, modulated by attention, interact to determine the extent of implicit recalibration.

Sensorimotor adaptation is driven by sensory prediction errors, the difference between the predicted and actual feedback. When the position of the feedback is made uncertain, adaptation is attenuated. This effect, in the context of optimal sensory integration models, has been attributed to a weakening of the error signal driving adaptation. Here we consider an alternative hypothesis, namely that uncertainty alters the perceived location of the feedback. We present two visuomotor adaptation experiments to compare these hypotheses, varying the size and uncertainty of a visual error signal. Uncertainty attenuated learning when the error size was small but had no effect when the error size was large. This pattern of results favors the hypothesis that uncertainty does not impact the strength of the error signal, but rather, leads to mis-localization of the error. We formalize these ideas to offer a novel perspective on the effect of visual uncertainty on implicit sensorimotor adaptation.SIGNIFICANCE STATEMENTCurrent models of sensorimotor adaptation assume that the rate of learning will be related to properties of the error signal (e.g., size, consistency, relevance). Recent evidence has challenged this view, pointing to a rigid, modular system, one that automatically recalibrates the sensorimotor map in response to movement errors, with minimal constraint. In light of these developments, this study revisits the influence of feedback uncertainty on sensorimotor adaptation. Adaptation was attenuated in response to a noisy feedback signal, but the effect was only manifest for small errors and not for large errors. This interaction suggests that uncertainty does not weaken the error signal. Rather, it may influence the perceived location of the feedback and thus the change in the sensorimotor map induced by that error. These ideas are formalized to show how the motor system remains exquisitely calibrated, even if adaptation is largely insensitive to the statistics of error signals.