The role of visual error and reward feedback in learning to aim to an optimal movement endpoint.
Bibliographic record
Abstract
When presented with a target circle horizontally overlapped by a penalty circle, participants initially aim closer to the penalty circle than optimal and with experience and feedback shift their endpoint horizontally to the optimal endpoint. Our purpose was to determine whether solely reward feedback or reward feedback in combination with visual error feedback of the movement and final movement endpoint is necessary for participants to learn to aim to the optimal endpoint. Participants received money for hitting the target but lost money for hitting the penalty region. In a critical No Feedback group, the target/penalty configuration would disappear on movement initiation, but participants were told the number of points received or lost after each trial. Similar to groups receiving various amounts of visual error feedback (target never disappeared, or reappeared upon screen contact), participants in the No Feedback group shifted the endpoint horizontally with experience, but they vertically undershot the horizontal meridian that contained the optimal endpoint. The region of the vertical undershoot, although suboptimal, was associated with less variance in the value of the expected outcomes. We suggest that reward feedback encourages participants to aim toward this less risky region, whereas receiving full visual error feedback leads participants to aim to the more advantageous horizontal meridian. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".