Oculomotor feature discrimination is cortically mediated
Bibliographic record
Abstract
Eye movements are often directed toward stimuli with specific features. Decades of neurophysiological research has determined that this behavior is subserved by a feature-reweighting of the neural activation encoding potential eye movements. Despite the considerable body of research examining feature-based target selection, no comprehensive theoretical account of the feature-reweighting mechanism has yet been proposed. Given that such a theory is fundamental to our understanding of the nature of oculomotor processing, we propose an oculomotor feature-reweighting mechanism here. We first summarize the considerable anatomical and functional evidence suggesting that oculomotor substrates that encode potential eye movements rely on the visual cortices for feature information. Next, we highlight the results from our recent behavioral experiments demonstrating that feature information manifests in the oculomotor system in order of featural complexity, regardless of whether the feature information is task-relevant. Based on the available evidence, we propose an oculomotor feature-reweighting mechanism whereby (1) visual information is projected into the oculomotor system only after a visual representation manifests in the highest stage of the cortical visual processing hierarchy necessary to represent the relevant features and (2) these dynamically recruited cortical module(s) then perform feature discrimination via shifting neural feature representations, while also maintaining parity between the feature representations in cortical and oculomotor substrates by dynamically reweighting oculomotor vectors. Finally, we discuss how our behavioral experiments may extend to other areas in vision science and its possible clinical applications.
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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 | 0.002 |
| 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 | 0.000 | 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".