Reward feedback accelerates motor learning (original) (raw)

Neural mechanisms of reward-related motor learning

Current opinion in neurobiology, 2003

The analysis of the neural mechanisms responsible for rewardrelated learning has benefited from recent studies of the effects of dopamine on synaptic plasticity. Dopamine-dependent synaptic plasticity may lead to strengthening of selected inputs on the basis of an activity-dependent conjunction of sensory afferent activity, motor output activity, and temporally related firing of dopamine cells. Such plasticity may provide a link between the reward-related firing of dopamine cells and the acquisition of changes in striatal cell activity during learning. This learning mechanism may play a special role in the translation of reward signals into context-dependent response probability or directional bias in movement responses. Abbreviations HFS high-frequency stimulation ICSS intracranial self-stimulation LTP long-term potentiation www.current-opinion.com Current Opinion in Neurobiology 2003, 13:685-690 30. Tremblay L, Hollerman JR, Schultz W: Modifications of reward expectation-related neuronal activity during learning in primate striatum.

Reward boosts reinforcement-based motor learning

iScience, 2021

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.

The effect of feedback after good and poor trials on the continuous motor tasks learning

Acta Gymnica, 2018

, the low frequency of the feedback and, in addition, feedback after poor trials and large errors when the learner is directed towards the correct movement pattern is considered to be more effective than feedback after good trials and small errors (Salmoni, Schmidt, & Walter, 1984). However, recent studies in this area have provided real insights into the motivational role of the augmented feedback for motor learning (

Learning from sensory and reward prediction errors during motor adaptation

2011

Voluntary motor commands produce two kinds of consequences. Initially, a sensory consequence is observed in terms of activity in our primary sensory organs (e.g., vision, proprioception). Subsequently, the brain evaluates the sensory feedback and produces a subjective measure of utility or usefulness of the motor commands (e.g., reward). As a result, comparisons between predicted and observed consequences of motor commands produce two forms of prediction error. How do these errors contribute to changes in motor commands? Here, we considered a reach adaptation protocol and found that when high quality sensory feedback was available, adaptation of motor commands was driven almost exclusively by sensory prediction errors. This form of learning had a distinct signature: as motor commands adapted, the subjects altered their predictions regarding sensory consequences of motor commands, and generalized this learning broadly to neighboring motor commands. In contrast, as the quality of the sensory feedback degraded, adaptation of motor commands became more dependent on reward prediction errors. Reward prediction errors produced comparable changes in the motor commands, but produced no change in the predicted sensory consequences of motor commands, and generalized only locally. Because we found that there was a within subject correlation between generalization patterns and sensory remapping, it is plausible that during adaptation an individual's relative reliance on sensory vs. reward prediction errors could be inferred. We suggest that while motor commands change because of sensory and reward prediction errors, only sensory prediction errors produce a change in the neural system that predicts sensory consequences of motor commands.

Reward abundance interferes with error-based learning in a visuomotor adaptation task

PLOS ONE, 2018

The brain rapidly adapts reaching movements to changing circumstances by using visual feedback about errors. Providing reward in addition to error feedback facilitates the adaptation but the underlying mechanism is unknown. Here, we investigate whether the proportion of trials rewarded (the 'reward abundance') influences how much participants adapt to their errors. We used a 3D multi-target pointing task in which reward alone is insufficient for motor adaptation. Participants (N = 423) performed the pointing task with feedback based on a shifted hand-position. On a proportion of trials we gave them rewarding feedback that their hand hit the target. Half of the participants only received this reward feedback. The other half also received feedback about endpoint errors. In different groups, we varied the proportion of trials that was rewarded. As expected, participants who received feedback about their errors did adapt, but participants who only received reward-feedback did not. Critically, participants who received abundant rewards adapted less to their errors than participants who received less reward. Thus, reward abundance negatively influences how much participants learn from their errors. Probably participants used a mechanism that relied more on the reward feedback when the reward was abundant. Because participants could not adapt to the reward, this interfered with adaptation to errors.

Online and post-trial feedback differentially affect implicit adaptation to a visuomotor rotation

Experimental Brain Research, 2014

Mulitple motor learning processes can be discriminated in visuomotor rotation paradigms. At least four processes have been proposed: Implicit adaptation updates an internal model based on prediction errors. Model-free reinforcement reinforces actions that achieve task success. Usedependent learning favors repetition of prior movements, and strategic learning uses explicit knowledge about the task.

Practicing one thing at a time: the secret to reward-based learning?

2019

Binary reward feedback on movement success is sufficient for learning in some simple reaching tasks, but not in some more complex ones. It is unclear what the critical conditions for learning are. Here, we ask how reward-based sensorimotor learning depends on the number of factors that are task-relevant. In a task that involves two factors, we test whether learning improves by giving feedback on each factor in a separate phase of the learning. Participants learned to perform a 3D trajectory matching task on the basis of binary reward-feedback in three phases. In the first and second phase, the reward could be based on the produced slant, the produced length or the combination of the two. In the third phase, the feedback was always based on the combination of the two factors. The results showed that reward-based learning did not depend on the number of factors that were task-relevant. Consistently, providing feedback on a single factor in the first two phases did not improve motor le...

Effect of sensory experience on motor learning strategy

Journal of Neurophysiology, 2014

2 cally reduces a mismatch in the visuo-motor coordination. Can the 3 underlying learning strategy be modified by environmental factors 4 or a subject's learning experiences? To elucidate this matter, two 5 groups of subjects learned to execute reaching arm movements in 6 environments with task-irrelevant visual cues. However, one group 7 had previous experience of learning these movements using task-8 relevant visual cues. The results demonstrate that the two groups 9 used different learning strategies for the same visual environment, 10 and that the learning strategy was influenced by prior learning ex-11 perience.

Emotion and reward are dissociable from error during motor learning

Experimental Brain Research, 2016

motor performance even when concurrent sensorimotor adaptation was taking place in a perpendicular direction. Thus, these experiments demonstrate that affective states were dissociable from error magnitude during motor learning and that affect more closely tracked points earned. Our findings further implicate reward as another factor, other than error, that contributes to motor learning, suggesting the importance of incorporating affective states into models of motor learning.

Motor Learning with Augmented Feedback: Modality-Dependent Behavioral and Neural Consequences

Cerebral Cortex, 2011

Sensory information is critical to correct performance errors online during the execution of complex tasks and can be complemented by augmented feedback (FB). Here, 2 groups of participants acquired a new bimanual coordination pattern under different augmented FB conditions: 1) visual input reflecting coordination between the 2 hands and 2) auditory pacing integrating the timing of both hands into a single temporal structure. Behavioral findings revealed that the visual group became dependent on this augmented FB for performance, whereas the auditory group performed equally well with or without augmented FB by the end of practice. Functional magnetic resonance imaging (fMRI) results corroborated these behavioral findings: the visual group showed neural activity increases in sensory-specific areas during practice, supporting increased reliance on augmented FB. Conversely, the auditory group showed a neural activity decrease, specifically in areas associated with cognitive/sensory monitoring of motor task performance, supporting the development of a control mode that was less reliant on augmented FB sources. Finally, some remnants of brain activity in sensory-specific areas in the absence of augmented FB were found for the visual group only, illustrating ongoing reliance on these areas. These findings provide the first neural account for the ''guidance hypothesis of information FB,'' extensively supported by behavioral research.