ArticlePLoS computational biology2019
The gradient of the reinforcement landscape influences sensorimotor learning.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Successful reinforcement history suppresses explicit and implicit error corrections.PLoS computational biology · 2026Article
- Article
- Hypothesis testing governs strategic motor learning.NPJ science of learning · 2026Article
- Enforcing a high success percentage interferes with reward-based motor learning.Scientific reports · 2026Article
- Article
- Online Movements Reflect Ongoing Deliberation.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- The sign of exploration during reward-based motor learning is not independent from trial to trial.Experimental brain research · 2025Article
- A Circle-Drawing Task for Studying Reward-Based Motor Learning in Children and Adults.Behavioral sciences (Basel, Switzerland) · 2024Article
- Roles and interplay of reinforcement-based and error-based processes during reaching and gait in neurotypical adults and individuals with Parkinson's disease.PLoS computational biology · 2024Article
- Review
- Punishment Leads to Greater Sensorimotor Learning But Less Movement Variability Compared to Reward.Neuroscience · 2024Article
- Reinforcement Learning during Locomotion.eNeuro · 2024Article
- Reinforcement feedback impairs locomotor adaptation and retention.Frontiers in behavioral neuroscience · 2024Article
- Reinforcement-based processes actively regulate motor exploration along redundant solution manifolds.Proceedings. Biological sciences · 2023Article
- Implicit reward-based motor learning.Experimental brain research · 2023Article
- Implicit reward-based motor learning.bioRxiv : the preprint server for biology · 2023Article
- The sensorimotor system modulates muscular co-contraction relative to visuomotor feedback responses to regulate movement variability.Journal of neurophysiology · 2023Article
- Exercising choice over feedback schedules during practice is not advantageous for motor learning.Psychonomic bulletin & review · 2023Article
- Neuromuscular control: from a biomechanist's perspective.Frontiers in sports and active living · 2023Review
- Human Variation in Error-Based and Reinforcement Motor Learning Is Associated With Entorhinal Volume.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Consideration of previous successes and failures is essential to mastering a motor skill. Much of what we know about how humans and animals learn from such reinforcement feedback comes from experiments that involve sampling from a small number of discrete actions. Yet, it is less understood how we learn through reinforcement feedback when sampling from a continuous set of possible actions. Navigating a continuous set of possible actions likely requires using gradient information to maximize success. Here we addressed how humans adapt the aim of their hand when experiencing reinforcement feedback that was associated with a continuous set of possible actions. Specifically, we manipulated the change in the probability of reward given a change in motor action-the reinforcement gradient-to study its influence on learning. We found that participants learned faster when exposed to a steep gradient compared to a shallow gradient. Further, when initially positioned between a steep and a shallow gradient that rose in opposite directions, participants were more likely to ascend the steep gradient. We introduce a model that captures our results and several features of motor learning. Taken together, our work suggests that the sensorimotor system relies on temporally recent and spatially local gradient information to drive learning.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.