Motion distractors perturb saccade programming later in time than static distractors
Bibliographic record
Abstract
The mechanism that reweights oculomotor vectors based on visual features is unclear. However, the latency of oculomotor visual activations gives insight into their antecedent featural processing. We compared the oculomotor processing time course of grayscale, task-irrelevant static and motion distractors during target selection by continuously measuring a battery of human saccadic behavioral metrics as a function of time after distractor onset. The motion direction was towards or away from the target and the motion speed was fast or slow. We compared static and motion distractors and observed that both distractors elicited curved saccades and shifted endpoints at short latencies (∼25 ms). After 50 ms, saccade trajectory biasing elicited by motion distractors lagged static distractor trajectory biasing by 10 ms. There were no such latency differences between distractor motion directions or motion speeds. This pattern suggests that additional processing of motion stimuli occurred prior to the propagation of visual information into the oculomotor system. We examined the interaction of distractor processing time (DPT) with two additional factors: saccadic reaction time (SRT) and saccadic amplitude. Shorter SRTs were associated with shorter DPT latencies of biased saccade trajectories. Both SRT and saccadic amplitude were associated with the magnitude of saccade trajectory biases.
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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 | 0.001 |
| 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 | 0.000 |
| 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".