Facilitation of ocular pursuit during transient occlusion of externally-generated target motion by concurrent upper limb movement
Bibliographic record
Abstract
Smooth pursuit during prolonged occlusion is improved in the presence of sensorimotor signals when tracking self-generated target motion. The current study investigated whether concurrent arm tracking of externally-generated target motion conveys a similar facilitation to ocular pursuit of transiently occluded constant velocity (Experiment 1) or accelerating (Experiment 2) targets. Velocity characteristics and occlusion duration were arranged in random or blocked order, thus permitting a novel examination of the contribution from sensorimotor signals and predictive processes acting within the ocular system during transient occlusion. Consistent with previous investigations, smooth pursuit decayed during transient occlusion; but eye velocity was higher when trials were presented in blocked compared to random order, particularly for positively accelerating targets. For fast, constant velocity targets, concurrent arm movement facilitated smooth pursuit during transient occlusion. Nevertheless, even with increased predictability regarding the upcoming target motion in blocked-order trials and the presence of sensorimotor signals from concurrent arm movement, eye velocity always remained less than target velocity during occlusion. This contrasted with the manual response, which attained velocity close to target velocity, whether in blocked or random conditions. These findings are discussed with reference to recent models of ocular pursuit that incorporate short-term and/or long-term prediction to account for target extrapolation during occlusion.
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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) | 0.001 | 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".