Learning a reach trajectory based on binary reward feedback

Implicit reward-based motor learning

Binary feedback, providing information solely about task success or failure, can be sufficient to drive motor learning. While binary feedback can induce explicit adjustments in movement strategy, it remains unclear if this type of feedback also induces implicit learning. We examined this question in a center-out reaching task by gradually moving an invisible reward zone away from a visual target to a final rotation of 7.5° or 25° in a between-group design. Participants received binary feedback, indicating if the movement intersected the reward zone. By the end of the training, both groups modified their reach angle by about 95% of the rotation. We quantified implicit learning by measuring performance in a subsequent no-feedback aftereffect phase, in which participants were told to forgo any adopted movement strategies and reach directly to the visual target. The results showed a small, but robust (2-3°) aftereffect in both groups, highlighting that binary feedback elicits implicit learning. Notably, for both groups, reaches to two flanking generalization targets were biased in the same direction as the aftereffect. This pattern is at odds with the hypothesis that implicit learning is a form of use-dependent learning. Rather, the results suggest that binary feedback can be sufficient to recalibrate a sensorimotor map.

Conditions under which college students cease learning

Introduction

Effective learning involves the acquisition of information toward a goal and cessation upon reaching that goal. Whereas the process of learning acquisition is well understood, comparatively little is known about how or when learning ceases under naturalistic, open-ended learning conditions in which the criterion for performance is not specified. Ideally, learning should cease once there is no progress toward the goal, although this has never been directly tested in human learners. The present set of experiments explored the conditions under which college students stopped attempting to learn a series of inductive perceptual discrimination problems.

Methods

Each problem varied by whether it was solvable and had a criterion for success. The first problem was solvable and involved a pre-determined criterion. The second problem was solvable, but with no criterion for ending the problem so that learners eventually achieved a highly accurate level of performance (overlearning). The third problem was unsolvable as the correct answer varied randomly across features. Measures included the number of trials attempted and the outcome of each problem.

Results and Discussion

Results revealed that college students rarely ceased learning in the overlearning or unsolvable problems even though there was no possibility for further progress. Learning cessation increased only by manipulating time demands for completion or reducing the opportunity for reinforcement. These results suggest that human learners show laudable, but inefficient and unproductive, attempts to master problems they should cease.

Reinforcement Learning Is Impaired in the Sub-acute Post-stroke Period

Background

Neurorehabilitation approaches are frequently predicated on motor learning principles. However, much is left to be understood of how different kinds of motor learning are affected by stroke causing hemiparesis. Here we asked if two kinds of motor learning often employed in rehabilitation, (1) reinforcement learning and (2) error-based adaptation, are altered at different times after stroke.

Methods

In a cross-sectional design, we compared learning in two groups of patients with stroke, matched for their baseline motor execution deficit on the paretic side. The early group was tested within 3 months following stroke (N = 35) and the late group was tested more than 6 months after stroke (N = 30). Two types of task were studied: one based on reinforcement learning and the other on error-based learning.

Results

We found that reinforcement learning was impaired in the early but not the late group, whereas error-based learning was unaffected compared to controls. These findings could not be attributed to differences in baseline execution, cognitive impairment, gender, age, or lesion volume and location.

Conclusions

The presence of a specific impairment in reinforcement learning in the first 3 months after stroke has important implications for rehabilitation. It might be necessary to either increase the amount of reinforcement feedback given early or even delay onset of certain forms of rehabilitation training, e.g., like constraint-induced movement therapy, and instead emphasize others forms of motor learning in this early time period. A deeper understanding of stroke-related changes in motor learning capacity has the potential to facilitate the development of new, more precise treatment interventions.

Failure induces task-irrelevant exploration during a stencil task

During reward-based motor tasks, performance failure leads to an increase in movement variability along task-relevant dimensions. These increases in movement variability are indicative of exploratory behaviour in search of a better, more successful motor action. It is unclear whether failure also induces exploration along task-irrelevant dimensions that do not influence performance. In this study, we ask whether participants would explore the task-irrelevant dimension while they performed a stencil task. With a stylus, participants applied downward, normal force that influenced whether they received reward (task-relevant) as they simultaneously made erasing-like movement patterns along the tablet that did not influence performance (task-irrelevant). In this task, the movement pattern was analyzed as the distribution of movement directions within a movement. The results showed significant exploration of task-relevant force and task-irrelevant movement patterns. We conclude that failure can induce additional movement variability along a task-irrelevant dimension.

Reward boosts reinforcement-based motor learning

Besides relying heavily on sensory and reinforcement feedback, motor skill learning may also depend on the level of motivation experienced during training. Yet, how motivation by reward modulates motor learning remains unclear. In 90 healthy subjects, we investigated the net effect of motivation by reward on motor learning while controlling for the sensory and reinforcement feedback received by the participants. Reward improved motor skill learning beyond performance-based reinforcement feedback. Importantly, the beneficial effect of reward involved a specific potentiation of reinforcement-related adjustments in motor commands, which concerned primarily the most relevant motor component for task success and persisted on the following day in the absence of reward. We propose that the long-lasting effects of motivation on motor learning may entail a form of associative learning resulting from the repetitive pairing of the reinforcement feedback and reward during training, a mechanism that may be exploited in future rehabilitation protocols.