The Influence of Visual Feedback and Prior Knowledge About Feedback on Vertical Aiming Strategies
Bibliographic record
Abstract
Two experiments were conducted to examine time and energy optimization strategies for movements made with and against gravity. In Experiment 1, the authors manipulated concurrent visual feedback, and knowledge about feedback. When vision was eliminated upon movement initiation, participants exhibited greater undershooting, both with their primary submovement and their final endpoint, than when vision was available. When aiming downward, participants were more likely to terminate their aiming following the primary submovement or complete a lower amplitude corrective submovement. This strategy reduced the frequency of energy-consuming corrections against gravity. In Experiment 2, the authors eliminated vision of the hand and the target at the end of the movement. This procedure was expected to have its greatest impact under no-vision conditions where no visual feedback was available for subsequent planning. As anticipated, direction and concurrent visual feedback had a profound impact on endpoint bias. Participants exhibited pronounced undershooting when aiming downward and without vision. Differences in undershooting between vision and no vision were greater under blocked feedback conditions. When performers were uncertain about the impending feedback, they planned their movements for the worst-case scenario. Thus movement planning considers the variability in execution, and avoids outcomes that require time and energy to correct.
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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 | 0.056 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".