Microsaccades and temporal attention at different locations of the visual field
Bibliographic record
Abstract
Temporal attention, the prioritization of information at specific points in time, improves performance in behavioral tasks but cannot ameliorate the perceptual asymmetries that exist across the visual field. That is, even after attentional deployment, performance is better along the horizontal than vertical meridian and worse at the upper than lower vertical meridian. Here we asked whether and how microsaccades-tiny fixational eye-movements-could mirror or alternatively attempt to compensate for these performance asymmetries by assessing temporal profiles and direction of microsaccades as a function of visual field location. Observers were asked to report the orientation of one of two targets presented at different time points, in one of three blocked locations (fovea, right horizontal meridian, upper vertical meridian). We found the following: (1) Microsaccade occurrence did not affect either task performance or the magnitude of the temporal attention effect. (2) Temporal attention modulated the microsaccade temporal profiles, and this modulation varied with polar angle location. At all locations, microsaccade rates were significantly more suppressed in anticipation of the target when temporally cued than in the neutral condition. Moreover, microsaccade rates were more suppressed during target presentation in the fovea than in the right horizontal meridian. (3) Across locations and attention conditions, there was a pronounced bias toward the upper hemifield. Overall, these results reveal that temporal attention benefits performance similarly around the visual field, microsaccade suppression is more pronounced for attention than expectation (neutral trials) across locations, and the directional bias toward the upper hemifield could reflect an attempt to compensate for typical poor performance at the upper vertical meridian.
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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) | 0.001 | 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".