Accounting for direction and speed of eye motion in planning visually guided manual tracking
Bibliographic record
Abstract
Accurate motor planning in a dynamic environment is a critical skill for humans because we are often required to react quickly and adequately to the visual motion of objects. Moreover, we are often in motion ourselves, and this complicates motor planning. Indeed, the retinal and spatial motions of an object are different because of the retinal motion component induced by self-motion. Many studies have investigated motion perception during smooth pursuit and concluded that eye velocity is partially taken into account by the brain. Here we investigate whether the eye velocity during ongoing smooth pursuit is taken into account for the planning of visually guided manual tracking. We had 10 human participants manually track a target while in steady-state smooth pursuit toward another target such that the difference between the retinal and spatial target motion directions could be large, depending on both the direction and the speed of the eye. We used a measure of initial arm movement direction to quantify whether motor planning occurred in retinal coordinates (not accounting for eye motion) or was spatially correct (incorporating eye velocity). Results showed that the eye velocity was nearly fully taken into account by the neuronal areas involved in the visuomotor velocity transformation (between 75% and 102%). In particular, these neuronal pathways accounted for the nonlinear effects due to the relative velocity between the target and the eye. In conclusion, the brain network transforming visual motion into a motor plan for manual tracking adequately uses extraretinal signals about eye velocity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".