Optimal weighting of costs and probabilities in a risky motor decision-making task requires experience.
Bibliographic record
Abstract
Previous research has revealed that people choose to aim toward an "optimal" endpoint when faced with a movement task with externally imposed payoffs. This optimal endpoint is modeled based on the magnitude of the payoffs and the probability of hitting the different payoff regions (endpoint variability). Endpoint selection, however, has only been studied after people had experience with the aiming task. The present study examined initial endpoint selection and how it changed as a function of experience with performing the task. Participants completed 300 movements to a target that was overlapped by a penalty region. Mean endpoint was analyzed in intervals of 50 trials. Predictions based on the optimal model would indicate that the mean endpoint should be farther from the penalty region early in practice when endpoint variability is higher-increasing target misses but decreasing costly penalty hits. As variability decreases, however, it is predicted that the endpoint should shift closer to the optimal location. In contrast to these predictions, participants' mean movement endpoints started closer to the penalty region and shifted away with increasing practice, even as endpoint variability decreased. This pattern of endpoint selection leads to suboptimal gains early in experience but more optimal gains by the end of the trials. These findings suggest that, similar to results found in cognitive decision-making tasks, people need to receive performance-based feedback to weight their estimations of probability and payoffs in motor tasks.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".