Saliency Response in Superior Colliculus at the Future Saccade Goal Predicts Fixation Duration during Free Viewing of Dynamic Scenes
Bibliographic record
Abstract
Eye movements in daily life occur in rapid succession and often without a predefined goal. Using a free viewing task, we examined how fixation duration prior to a saccade correlates to visual saliency and neuronal activity in the superior colliculus (SC) at the saccade goal. Rhesus monkeys (three male) watched videos of natural, dynamic, scenes while eye movements were tracked and, simultaneously, neurons were recorded in the superficial and intermediate layers of the superior colliculus (SCs and SCi, respectively), a midbrain structure closely associated with gaze, attention, and saliency coding. Saccades that were directed into the neuron's receptive field (RF) were extrapolated from the data. To interpret the complex visual input, saliency at the RF location was computed during the pre-saccadic fixation period using a computational saliency model. We analyzed if visual saliency and neural activity at the saccade goal predicted pre-saccadic fixation duration. We report three major findings: (1) Saliency at the saccade goal inversely correlated with fixation duration, with motion and edge information being the strongest predictors. (2) SC visual saliency responses in both SCs and SCi were inversely related to fixation duration. (3) SCs neurons, and not SCi neurons, showed higher activation for two consecutive short fixations, suggestive of concurrent saccade processing during free viewing. These results reveal a close correspondence between visual saliency, SC processing, and the timing of saccade initiation during free viewing and are discussed in relation to their implication for understanding saccade initiation during real-world gaze behavior.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".