Independent gaze-centered representations of reach targets viewed with left vs. right eye
Bibliographic record
Abstract
Numerous studies on visuomotor control (reaching and pointing) have implicitly assumed that there is a single eye-centered representation of target location for planning movements. However, visual information about target location enters our brains through two eyes which are horizontally separated and therefore provide disparate location information. In this study, we explored whether information about target location from the two eyes is actively transformed into a common eye-centered representation for reach space or whether the eye which encodes target location matters. We dissociated the location of targets presented to the two eyes by having seven subjects pointing to a distant central target while fixating on various (near) horizontally displaced, peripherally viewed targets,, a) with the right eye only or b) left eye only. We measured the final pointing positions of the fingertip in space. When pointing to peripheral viewed targets, subjects tend to overshoot the position of the target depending on its position relative to gaze. During viewing with each eye, this gaze-dependent pattern of overshoot errors depended on target location relative to each eye independently. These results suggest that the information about target location from the two eyes is not actively transformed into a single common target representation.
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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 | 0.001 |
| 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 | 0.001 |
| 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".