Spatial variability in localization biases predicts crowding performance
Bibliographic record
Abstract
Visual processing varies substantially across individuals, and prior work has shown significant individual differences in fundamental processes such as spatial localization. For example, when asked to report the location of a briefly flashed target in the periphery, different observers systematically misperceive its location in an idiosyncratic manner, showing different patterns of reproduction error across visual field locations. In this study, we tested whether these individual differences may propagate to other stages of visual processing, affecting the strength of visual crowding, which depends on the spacing between objects in the periphery. We, therefore, investigated the relationship between observers' idiosyncratic biases in localization and the strength of crowding to determine whether these spatial biases limit peripheral object recognition. To examine this relationship, we measured the strength of crowding at 12 locations at 8° eccentricity, in addition to the perceived spacing between pairs of Gaussian patches at these same locations. These measurements show an association between variability in crowding strength and perceived spacing at the same visual field locations: at locations where a participant experienced stronger crowding, their perceived spacing was smaller, and vice versa. We demonstrate that spatial heterogeneity in perceived spacing affects observers' ability to recognize objects in the periphery. Our results support the idea that variability in both spatial sensitivity and bias contribute to variability in the strength of crowding and bolster the account that variability in spatial coding may propagate across multiple stages of visual processing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".