The use of spatial frequencies for visual word recognition in each cerebral hemisphere
Bibliographic record
Abstract
A matter of important debate remains regarding whether the range of spatial frequencies (SFs) preferred by the left cerebral hemisphere is higher than that preferred by the right hemisphere. In visual word recognition, relatively low SFs allow access to coarse visual word form and higher SFs to fine letter discrimination. Here, we determined the SF spectrum optimally used by each cerebral hemisphere for visual word recognition. Twelve right-handed normal readers read 1,800 word stimuli, so far, each having been presented for 200 ms either in the right or the left hemifield. We created each word stimulus by randomly sampling the SFs of a word (see Willenbockel et al., in press). The quantity of SFs was adjusted to maintain correct response rate at 50%. For each participant and for each hemifield, we performed a multiple linear regression between the random filters and response accuracy. Regression coefficients were smoothed (FWHM = 2.35), z-scored, and a pixel test was applied (Chauvin et al, 2005). For now, five of the 12 participants yield significant results in both hemifields. Nonetheless, a t-test on the SF peaks of these five subjects already confirms the use of higher SFs for words presented to the right hemifield (M = 1.64 cycles/letter) than for words presented to the left hemifield (M = 1.37 cycles/letter; mean difference = 0.27 cycles/letter, t(4) = 3.64, p <0.05). Furthermore, the quantity of SF information necessary for accurate word recognition was 45% greater for words processed by the right hemisphere (t(4) = 12.20, p <0.001). Our findings support the hypothesis of a left hemisphere bias for higher SFs, explaining its greater sensitivity for visual word recognition. As the right hemisphere has access to coarser visual word form, it's possible that it remains useful for the recognition of words with lower visual confusability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".