The reinforcement landscape influences sensorimotor learning
Bibliographic record
Abstract
Successful movement, such as hitting a long and straight golf drive, produces a satisfying feeling. Such positive reinforcement feedback has been suggested to influence motor learning. Here, we tested the idea that the reinforcement landscape—the probability of task success given a motor action—can be manipulated to influence learning. In Experiment 1, we tested the prediction that participants experiencing a steep reinforcement landscape would learn faster than those experiencing a shallow landscape. In Experiment 2, we predicted that participants experiencing a complex landscape with multiple gradients would change where they aimed their hand such that they would ascend the steeper portion of the landscape. Participants grasped the handle of a robot arm. They reached from a home position to a displayed target. Vision of the upper limb was occluded. Critically, we shaped the reinforcement landscape by manipulating the probability of reward as a function of their angular displacement from the displayed target. Depending on the assigned landscape, participants were more likely to receive reward if they reached to the left and/or right of the displayed target. We found that participants learned at a faster rate when experiencing a steeper landscape and were more likely to ascend the steeper portion of a complex landscape. Finally, we developed a simple computational model that replicates both experiments. The model naturally reproduces several other hallmarks of human movement, such as random-walk behaviour in task-irrelevant dimensions, increased learning rates with greater movement variability and exponential learning curves.Acknowledgments: NSERC, CIHR
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".