A computational account of transsaccadic attentional allocation based on visual gain fields
Bibliographic record
Abstract
Coordination of goal-directed behavior depends on the brain's ability to recover the locations of relevant objects in the world. In humans, the visual system encodes the spatial organization of sensory inputs, but neurons in early visual areas map objects according to their retinal positions, rather than where they are in the world. How the brain computes world-referenced spatial information across eye movements has been widely researched and debated. Here, we tested whether shifts of covert attention are sufficiently precise in space and time to track an object's real-world location across eye movements. We found that observers' attentional selectivity is remarkably precise and is barely perturbed by the execution of saccades. Inspired by recent neurophysiological discoveries, we developed an observer model that rapidly estimates the real-world locations of objects and allocates attention within this reference frame. The model recapitulates the human data and provides a parsimonious explanation for previously reported phenomena in which observers allocate attention to task-irrelevant locations across eye movements. Our findings reveal that visual attention operates in real-world coordinates, which can be computed rapidly at the earliest stages of cortical processing.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".