People are better at maximizing expected gain in a manual aiming task with rapidly changing probabilities than with rapidly changing payoffs
Bibliographic record
Abstract
Previous research has shown that humans can select movements that achieve their goals, while avoiding negative outcomes, by selecting an "optimal movement endpoint." This optimal endpoint is modeled based on the participants' endpoint variability and the payoffs associated with the target and penalty regions within the environment. Although the values associated with our goals vary on a moment-to-moment basis in our daily interactions, the adaptation of endpoint selection to changing payoffs in laboratory-based tasks has been examined by varying contexts between blocks of trials. The present study was designed to determine whether participants adjust endpoints and aim to optimal endpoints and whether performance differs when probability or payoff parameters change from trial to trial. Participants aimed to a target circle that was partially overlapped by a penalty circle. They received 100 points for hitting the target and lost points for hitting the penalty area. The magnitude of the penalty value or the distance between the centers of the circles (related to the probability of target and penalty contact) was changed randomly from trial to trial in separate blocks. Results revealed that participants shifted their endpoint and generally aimed optimally when the distance between the circles was varied but did not optimally shift their endpoints when the penalty value was varied. The results suggest that participants rapidly adapted endpoints when the probabilities associated with the task change, because the spatial parameters are an intrinsic property of the visual stimuli that are tightly linked with the motor system, whereas consistent feedback may be necessary to adjust to value parameters effectively.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".