Reduced fixation stability induced by peripheral viewing does not contribute to crowding
Bibliographic record
Abstract
Attending to peripheral visual targets while maintaining central fixation, a process that involves covert attention, reduces fixation stability. Here, we tested the hypothesis that changes in fixation stability induced by peripheral viewing contribute to crowding in peripheral vision by increasing positional uncertainty. We first assessed whether fixation was less stable during peripheral versus central (foveal) viewing for both crowded and uncrowded stimuli. We then tested whether fixation stability during peripheral viewing was associated with the extent of crowding. Fourteen participants performed a tumbling E orientation discrimination task at three different eccentricities (0°, 5°, 10°). The target was presented with or without flankers. Fixational eye movements were measured using an infrared video-based eyetracker. A central fixation cross was provided for the two peripheral viewing conditions, and optotype size was scaled for each eccentricity. Discrimination of appropriately scaled uncrowded stimuli was unaffected by eccentricity, whereas discrimination of crowded stimuli deteriorated dramatically with eccentricity, despite scaling. Both crowded and uncrowded peripheral stimuli were associated with reduced fixation stability, increased microsaccadic amplitude, and a greater proportion of horizontal microsaccades relative to centrally presented stimuli. However, these effects were not associated with the magnitude of crowding. This suggests that reduced fixation stability due to peripheral viewing does not contribute to crowding in peripheral vision.
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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 | 0.000 |
| 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".