A Medium spatial frequency trough causes letter-by-letter dyslexia in normal readers
Bibliographic record
Abstract
Letter-by-letter (LBL) dyslexia is characterized by slow and laborious reading where reading latency increases markedly with the number of letters in a word. Interestingly, reading rate is also affected by high-level factors (i.e. imageability and lexical frequency), which suggests an implicit lexical/semantic access prior to conscious word identification. Fiset et al. (2006) recently proposed that the critical spatial frequencies for reading (between 2.5 to 3 cycles per letter) may be unavailable in LBL dyslexia and that implicit lexical/semantic access is mediated by lower spatial frequencies, which would fail however to offer information that is sufficiently accurate for explicit word recognition. To compensate, LBL readers would use high spatial frequencies for the sequential explicit identification of individual letters, which is the diagnostic feature of the disorder. The aim of the current study is to further investigate the special role of the medium frequencies for word recognition in normal readers. The critical medium spatial frequencies were filtered out from the words presented for overt reading, which therefore comprised only high and low ([[lt]]2 and [[gt]]6 cycles per letter) spatial frequencies. The results replicate the main features of LBL dyslexia. Reading latency increased linearly as a function of the number of letters in the word while being affected by imageability and lexical frequency. Error rates were relatively low, as in most LBL dyslexic cases. We thus caused letter-by-letter dyslexia in normal readers by depriving them of medium spatial frequency information. These findings are consistent with the crucial importance of this information and strongly suggest that dyslexics are truly unable to process these medium spatial frequencies for reading.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".