Evidence for Continuous Processing of Visual Information in a Manual Video-Aiming Task
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 In addition, the authors observed accurate corrections when vision of the cursor was withdrawn soon after the cursor jump.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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it