Keep your eyes on the prize: Terminal endpoint feedback is required for participants to learn to aim to an optimal endpoint
Bibliographic record
Abstract
Previous research has shown that participants need feedback and experience to aim to an optimal endpoint when aiming to a target that is overlapped with a penalty region. Participants received money for hitting the target but lost money for hitting the penalty region. It is not clear what type of feedback participants need in order to learn to aim to the optimal endpoint. The purpose of the present study was to determine whether participants need to only know whether they have succeeded or failed at the task or if vision of their hand in relation to the target and penalty regions is important. Participants were divided into three groups. In the No Feedback group, the target/penalty configuration would disappear when participants initiated the movement. After screen contact all participants would be informed of their points (i.e., what region they hit) but the No Feedback group could not see their endpoint in relation to the configuration. In the Terminal Feedback group, the configuration would disappear then reappear upon screen contact. Finally a Full Feedback group could see the configuration for the entire duration of the movement. Participants in the Full Feedback and Terminal Feedback group learned to aim closer to the optimal endpoint but participants in the No Feedback group did not. Trajectories were also measured to assess online corrections and these results will be discussed. Overall the results demonstrate that participants need visual feedback of their limb in relation to the target to aim for an optimal endpoint.
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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 | 0.022 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".