Mo' money mo' problems: The effect of practice on optimal movement end point during rapid aiming under risk
Bibliographic record
Abstract
The expected value of an uncertain decision is the product of the probability of the chosen outcome occurring and the value of the outcome. Trommerhauser et al. (2003) showed that people aimed to an 'optimal movement endpoint' that was modeled based on the participants' endpoint variability (probability) and the cost associated with a penalty region (value) that partially overlapped the target. Although it is predicted that the endpoint should change as a function of endpoint variability, optimal endpoint has only been examined after extensive training. Shifts in endpoint during training, while theoretically relevant, were not examined. The present study was designed to explore the change in endpoint as variability changes with practice. Participants made 300 aiming movements to a target worth +100pts with an overlapping penalty region worth -500pts. The optimal model suggests that 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, the endpoint should shift closer to the optimal location. In contrast to predictions, participants' mean movement endpoint started closer to the penalty region and shifted away with increasing practice, even as endpoint variability decreased. These results indicate that people may have to receive feedback in the form of hitting the penalty region to change to a more optimal movement planning strategy. Acknowledgments: This research was funded by NSERC
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".