Spatial Frequency and Similarity Modulate Crowding in Letter Identification
Bibliographic record
Abstract
Visual crowding, which impairs our ability to accurately identify a target stimulus when surrounded by flankers, is ubiquitous across a wide variety of stimulus classes. Target eccentricity and target-flanker distance constitute fundamental factors in crowding. Target-flanker similarity appears as another key factor based on findings obtained with non-linguistic stimuli. The present study investigated the impact of these factors in conjunction with spatial frequency (SF) content on single letter identification performance. We presented SF filtered letters to neurologically intact nondyslexic readers while manipulating target-flanker distance, target eccentricity and target-flanker similarity (metric based on published letter confusion matrices). SF filtering conditions were broadband, low-pass, high-pass and hybrid (i.e. medium SFs, known as optimal for letter recognition, removed from the stimulus). These conditions were matched on overall contrast energy. Participants were required to identify the target letter as fast and as accurately as possible. The results show that high target-flanker similarity enhances crowding, i.e. the joint effects of distance and eccentricity. This extends past findings on the impact of similarity on crowding to the visual identification of linguistic materials. Most importantly, the magnitude of the crowding effect is greatest with low-pass filtering, followed by hybrids, high-pass, and broadband, with all pairwise contrasts significant. We conclude that: 1- medium SFs provide optimal protection from crowding in letter recognition; 2- when medium SFs are absent from the stimulus, low SFs magnify crowding and high SFs protect against it, most likely through their opposite impact on the availability of distinctive feature information.
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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".