On the relation between anticipatory ocular torsion and anticipatory smooth pursuit
Bibliographic record
Abstract
Humans and other animals move their eyes in anticipation to compensate for sensorimotor delays. Such anticipatory eye movements can be driven by the expectation of a future visual object or event. Here we investigate whether such anticipatory responses extend to ocular torsion, the eyes' rotation about the line of sight. We recorded three-dimensional eye position in head-fixed healthy human adults who tracked a rotating dot pattern moving horizontally across a computer screen. This kind of stimulus triggers smooth pursuit with a horizontal and torsional component. In three experiments, we elicited expectation of stimulus rotation by repeatedly showing the same rotation (Experiment 1), or by using different types of higher-level symbolic cues indicating the rotation of the upcoming target (Experiments 2 and 3). Across all experiments, results reveal reliable anticipatory horizontal smooth pursuit. However, anticipatory torsion was only elicited by stimulus repetition, but not by symbolic cues. In summary, torsion can be made in anticipation of an upcoming visual event only when low-level motion signals are accumulated by repetition. Higher-level cognitive mechanisms related to a symbolic cue reliably evoke anticipatory pursuit but did not modulate torsion. These findings indicate that anticipatory torsion and anticipatory pursuit are at least partly decoupled and might be controlled separately.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".