Experience and Net Worth Affects Optimality in a Motor Decision Task
Bibliographic record
Abstract
Previous research has shown in a motor decision task involving a target (yielding a reward) and overlapping penalty area (yielding a loss), people initially aim closer to the penalty area than optimal. This risky strategy may be adopted to increase target hits, thereby increasing net worth. The purpose of the current study was to determine whether the starting net worth level (either 5,000 or 0 points) affected the influence of task experience on endpoint selection. It was hypothesized the 5,000-point group should adopt a less risky strategy and aim further from the penalty area than those with 0 points. Net worth affected participants' initial endpoint where the 5,000-point group aimed further from the penalty circle, and closer to the optimal endpoint, than the 0-point group. The 0-point group adapted their endpoint over the course session to aim closer to the optimal endpoint whereas no such change was seen in the 5,000-point group. The results show that changing the participants' reference point through initial net worth can affect the optimality of participants' endpoint and how endpoints change with experience.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".