Critical spatial frequencies in the perception of letters, faces, and novel stimuli
Bibliographic record
Abstract
Critical-band masking paradigm is a method that reveals the band of spatial frequencies used by human observers to identify a stimulus. Previous studies of letter recognition have shown that a) the critical band of frequencies is relatively narrow and b) the peak frequency in object frequency units changes with letter size, indicating scale-dependence and suggesting the existence of channels specialized for processing letters of various sizes (Majaj, Pelli, Kurshan, & Palomares, 2002; Oruc & Landy 2006). In this study, we investigated whether similar results are found for other types of visual stimuli. We characterized stimuli along two main dimensions: evolutionary relevance and amount of training. Letters are arbitrary shapes from an evolutionary perspective, but for which most observers are highly trained. Faces are not only well trained but may have long-standing evolutionary relevance in the human visual system. As arbitrary and untrained stimuli we used, first, a set of novel shapes, and second, mirror-image letters. We found, first, that all four types of patterns are recognized using a narrow band of frequencies, despite the fact that these are all broadband stimuli. Second, the critical frequency band shifted with changes in stimulus size in a similar manner for letters, reversed letters, and novel shapes. Faces on the other hand differed, in that there was a greater degree of scale invariance for larger stimuli. These results show that the critical frequencies found for letter processing are not unique to these linguistic symbols; face processing, however, may differ from other stimuli.
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 | 0.000 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".