Evidence for a retinal velocity memory underlying the direction of anticipatory smooth pursuit eye movements
Bibliographic record
Abstract
To compute spatially correct smooth pursuit eye movements, the brain uses both retinal motion and extraretinal signals about the eyes and head in space (Blohm and Lefèvre 2010). However, when smooth eye movements rely solely on memorized target velocity, such as during anticipatory pursuit, it is unknown if this velocity memory also accounts for extraretinal information, such as head roll and ocular torsion. To answer this question, we used a novel behavioral updating paradigm in which participants pursued a repetitive, spatially constant fixation-gap-ramp stimulus in series of five trials. During the first four trials, participants' heads were rolled toward one shoulder, inducing ocular counterroll (OCR). With each repetition, participants increased their anticipatory pursuit gain, indicating a robust encoding of velocity memory. On the fifth trial, they rolled their heads to the opposite shoulder before pursuit, also inducing changes in ocular torsion. Consequently, for spatially accurate anticipatory pursuit, the velocity memory had to be updated across changes in head roll and ocular torsion. We tested how the velocity memory accounted for head roll and OCR by observing the effects of changes to these signals on anticipatory trajectories of the memory decoding (fifth) trials. We found that anticipatory pursuit was updated for changes in head roll; however, we observed no evidence of compensation for OCR, representing the absence of ocular torsion signals within the velocity memory. This indicated that the directional component of the memory must be coded retinally and updated to account for changes in head roll, but not OCR.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".