The role of visual feedback on reach kinematics in a rapid decision making task
Bibliographic record
Abstract
When participants are presented with a target and overlapping penalty region, participants initially aim closer to the penalty region than optimal before shifting their endpoint to a more optimal location. Previously we divided participants into three groups, to explore the effect of different types of visual feedback. In the No Feedback group, the target/penalty configuration would disappear when participants initiated the movement. In the Terminal Feedback group, the configuration would disappear and then reappear upon screen contact. Finally, a Full Feedback group saw the configuration for the entire duration of the movement. We showed no difference in endpoint adaptation away from the penalty region over the course of exposure between the groups, but the Terminal/Full Feedback groups showed greater undershooting of the target, even once full feedback was given in a transfer task. Our results may be related to the finding that individuals have two distinct phases of movement: an initial increase in velocity to reach peak, followed by a decrease presumably to fine-tune the movement using visual feedback. The purpose of this study was to compare the kinematics of the reaches made to the target/penalty configurations under different feedback conditions to determine how visual feedback may have impacted endpoint selection. Results indicate that movement kinematics, including time after peak velocity, changed across exposure to the task in all groups. Other feedback mechanisms, aside from visual feedback, may have helped participants in all conditions select a more optimal endpoint over the course of task exposure.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".