Intercepting moving targets in the upper and lower visual fields
Bibliographic record
Abstract
Using a task with a well defined speed-accuracy trade-off function we recently demonstrated that visually guided movements made in the lower visual field (loVF) showed a stronger relationship to target size than the same movements made in the upper visual field (upVF). That is, peak velocity increased as target size increased only for movements made in the loVF. In addition, movements made in the loVF were more accurate than their counterparts in the upVF. Monkey neurophysiology has demonstrated an over-representation of the loVF in area MT, an area known to be critical in the processing of motion. This led us to investigate whether or not healthy individuals would demonstrate differences in the ability to intercept moving targets in the loVF and upVF. Targets were small filled squares moving from right to left or vice versa at a constant speed but starting from random positions along the x-axis. Movements were measured at 100 Hz with conventional OPTOTRAK recording. Peak velocity was higher for targets moving from right to left. In addition, subjects spent more time decelerating when targets appeared in the upVF, suggesting that subjects required more time to intercept targets accurately in the upVF. The majority of subjects also reported that subjectively, targets moving from left to right within the loVF were the easiest to intercept. There was a general tendency to overshoot the target in the y-direction. For the x-direction, subjects intercepted targets earlier when they were moving from left to right in both the upper and the lower visual fields. Overall, the data suggest that the interception of targets is more efficient in the lower rather than the upper visual field.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".