Bibliographic record
Abstract
Purpose: Torsional eye movements are considered reflexive responses to visual image rotation or to head roll about the visual axis. Recent studies indicate that torsion scales with visual stimulus properties such as rotational direction, speed and size, indicating a voluntary component. However, it is unclear whether these eye movements can be modulated by cognitive factors such as expectation. Method: Head-fixed healthy human adults (n=6) viewed a textured disk, translating horizontally to the right across a computer monitor and rotating about its center. This type of stimulus triggers horizontal smooth pursuit eye movements with a torsional component. Stimulus rotation was either clockwise, in the same direction as a rolling ball ("natural"), or counterclockwise ("unnatural"). In baseline trials, the texture moved horizontally without rotation. These three rotation conditions were presented in separate blocks of 100 trials each, two blocks per condition, to elicit cognitive expectation of rotational direction. Three-dimensional eye position was recorded with a head-mounted Chronos eye tracker. Results: Observers initiated horizontal pursuit 250 ms prior to stimulus onset in anticipation of translational stimulus motion. This effect was stronger for baseline than for rotation conditions, indicating that stimulus rotation is taken into account when computing anticipatory horizontal pursuit velocity. Interestingly, the eyes also started rotating clockwise in response to "natural" and counterclockwise in response to "unnatural" rotation prior to stimulus onset in anticipation of stimulus rotation. Conclusions: Torsional eye movements can be modulated by cognitive factors, indicating a strong voluntary component in the control of these movements. The frontal pursuit pathway, including areas such as the frontal eye field, might carry ocular torsion signals and underlie the effects of cognitive expectation on ocular torsion. Meeting abstract presented at VSS 2017
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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 | 0.004 |
| 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) | 0.002 | 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".