Spatial Heterogeneity in Localization Biases Predicts Crowding Performance
Bibliographic record
Abstract
Crowding is a fundamental constraint on peripheral object recognition linked to variability in spatial precision at different visual field locations. Previous work has also demonstrated significant individual biases in perceived location across visual field locations. We investigated the relationship between observers’ inherent localization biases and the strength of visual crowding across different visual field locations, to determine whether these biases might be related to variability in the strength of crowding. We tested whether peripheral locations with larger apparent spacing between pairs of objects (in the absence of stimulus manipulation) are also associated with reduced crowding. In Experiment 1, crowding was measured at 12 locations at 8º eccentricity. Participants identified the orientation of a central clock stimulus (pointing up, down, left, or right) with two tangential flankers whose orientation varied randomly. We compared these results to participants’ perceived spacing (Experiment 2) at the same locations. Participants were shown pairs of Gaussian blobs separated by one of 6 randomly selected spacings and identified whether the spacing between them was larger or smaller relative to their average of all previously seen spacings. Perceived spacing was estimated for each location from the spacing producing 50% ‘larger than the average’ responses. We show large individual variability in critical spacing and perceived spacing at different visual field locations and a positive correlation between them. In locations in which participants have stronger crowding, perceived spacing was smaller, and vice-versa (r= 0.44, p < .001). These findings demonstrate that spatial heterogeneity in perceived spacing affects observers’ ability to recognize objects in the periphery. Our results support the idea that multiple mechanisms may contribute to individual spatial variability in the strength of crowding and add further evidence supporting the idea that early variation in spatial coding propagates across multiple stages of visual 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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".