Evidence for Continuous Processing of Visual Information in a Manual Video-Aiming Task
Bibliographic record
Abstract
Research shows that individuals are able to correct for an experimentally-induced and unexpected aiming error (i.e., a cursor jump), even when they do not detect it consciously. Researchers have interpreted these results to be evidence of continuous processing of visual afferent information for movement control. The authors conducted 2 experiments to determine whether they would gain additional support for this proposition by showing that correction for a cursor jump can be initiated outside the central visual field. In addition, the authors wanted to determine whether the normally occurring modulation of the ongoing movement is affected by detection and correction of the cursor jump. Participants performed video-aiming movements in which a 30-mm cursor jump occurred in a small proportion of the trials. The results indicate that correction for the cursor jump was initiated when the cursor was as far as 15 degrees of visual angle from the target. In addition, the authors observed accurate corrections when vision of the cursor was withdrawn soon after the cursor jump. Last, online control processes reducing initial movement variability were not significantly affected by the detection and correction for the cursor jump. The results suggest near continuous monitoring of visual afferent information but a more discrete movement-correction process.
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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 | 0.008 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".