Temporal processing deficits in letter-by-letter reading
Bibliographic record
Abstract
Theories of the cognitive impairment underlying letter-by-letter reading vary widely, including prelexical and lexical level deficits. One prominent prelexical account proposes that the disorder results from difficulty in processing multiple letters simultaneously. We investigated whether this deficit extends to letters presented in rapid temporal succession. A letter-by-letter reader, G.M., was administered a rapid serial visual presentation task that has been used widely to study the temporal processing characteristics of the normal visual system. Comparisons were made to a control group of 6 brain-damaged individuals without reading deficits. Two target letters were embedded at varying temporal positions in a stream of rapidly presented single digits. After each stream, the identities of the two letters were reported. G.M. required an extended period of time after he had processed one letter before he was able to reliably identify a second letter, relative to the controls. In addition, G.M.'s report of the second letter was most impaired when it immediately followed the first letter, a pattern not seen in the controls, indicating that G.M. had difficulty processing the two items together. These data suggest that a letter-by-letter reading strategy may be adopted to help compensate for a deficit in the temporal processing of letters.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".