Curvature of Visual Space Under Vertical Eye Rotation: Implications for Spatial Vision and Visuomotor Control
Bibliographic record
Abstract
Most models of spatial vision and visuomotor control reconstruct visual space by adding a vector representing the site of retinal stimulation to another vector representing gaze angle. However, this scheme fails to account for the curvatures in retinal projection produced by rotatory displacements in eye orientation. In particular, our simulations demonstrate that even simple vertical eye rotation changes the curvature of horizontal retinal projections with respect to eye-fixed retinal landmarks. We confirmed the existence of such curvatures by measuring target direction in eye coordinates in which the retinotopic representation of horizontally displaced targets curved obliquely as a function of vertical eye orientation. We then asked subjects to point (open loop) toward briefly flashed targets at various points along these lines of curvature. The vector-addition model predicted errors in pointing trajectory as a function of eye orientation. In contrast, with only minor exceptions, actual subjects showed no such errors, showing a complete neural compensation for the eye position-dependent geometry of retinal curvatures. Rather than bolstering the traditional model with additional corrective mechanisms for these nonlinear effects, we suggest that the complete geometry of retinal projection can be decoded through a single multiplicative comparison with three-dimensional eye orientation. Moreover, because the visuomotor transformation for pointing involves specific parietal and frontal cortical processes, our experiment implicates specific regions of cortex in such nonlinear transformations.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".