Effect of training to an area-cue on human saccadic eye movements
Bibliographic record
Abstract
Introduction. Eye movement latency to targets can be shortened by advanced preparation of saccadic programs. Specifically, advanced saccade preparation can be enhanced through training to attend to a specific location in space (Paré & Munoz, 1996). However, people often know the area where a target will appear rather than its precise location. Here, we investigated how saccades were affected by training to attend to a circular area within which a target appeared at random locations. Additionally, we looked at how training to attend to an area of one size influenced saccades to targets presented in a larger circular area. Methods. Each trial began with a central fixation point, followed by simultaneously presenting a circular area-cue (6° or 10° diameter), for 400ms. These disappeared, and following a gap period (170ms or 220ms), the target was flashed for 68ms. Participants were required to quickly and accurately saccade to the target once it appeared. Saccade reaction time and position were recorded. To prevent anticipatory saccades, catch trials were included in pre- and post-training sessions where some targets were presented outside the area-cues. During the training sessions, the target was always presented within a 6° area at random locations. Results. Post-training goal-directed saccades were mostly anticipatory. Participants each developed a preferred region inside the trained area, where post-training anticipatory saccades were directed. This preferred region scaled with cued-area size, i.e. post-training distributions of saccade-end points greatly overlapped once normalized for cued-area position and size. From the preferred region subjects often generated visually driven corrective saccades to the target inside the area cue. Over all, there was no speed-accuracy trade off. Discussion. Present findings show that oculomotor preparations extend to areas, not just to a single target location. Compared to pre-training, the learned strategy assures that targets are acquired more quickly without loss of accuracy.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".