Participants are better at maximizing expected gain in a manual aiming task with rapidly changing probabilities rather than rapidly changing payoffs
Bibliographic record
Abstract
Previous research has shown that humans are able to select movements that achieve their goal while avoiding negative outcomes by selecting an 'optimal movement endpoint' which is modeled based on the participants' endpoint variability and the payoffs associated with the environment. Although in daily interactions the values associated with our goals vary on a moment-to-moment basis, our ability to adapt endpoint selection to changing payoffs in lab-based tasks has only been examined across blocks of trials. The purpose of the present study was to determine whether participants could adjust their endpoint when parameters of the model changed trial-to-trial. Participants aimed to a target circle that was 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 or the distance between the centers of the circles was changed randomly in separate blocks of trials. We found that participants shifted their endpoint when the distance between the circles was varied, but not when the value of the penalty circle was varied. We suggest that participants are more optimal with changing distance parameters because the distance between the two circles is an intrinsic property of the visual stimuli. The results of the Penalty block suggest participants in previous studies needed to receive performance feedback from earlier trials in order to aim to an optimal mean endpoint.Acknowledgments: This research was supported through grants from the Natural Sciences and Engineering Research Council of Canada and the Ontario Ministry of Research and Innovation.
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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 | 0.004 | 0.029 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".