The Focal Attention Window Size Explains Letter Substitution Errors in Reading
Bibliographic record
Abstract
Acquired Neglect Dyslexia is often associated with right-hemisphere brain damage and is mainly characterized by omissions and substitutions in reading single words. Martelli et al. proposed in 2011 that these two types of error are due to different mechanisms. Omissions should depend on neglect plus an oculomotor deficit, whilst substitutions on the difficulty with which the letters are perceptually segregated from each other (i.e., crowding phenomenon). In this study, we hypothesized that a deficit of focal attention could determine a pathological crowding effect, leading to imprecise letter identification and consequently substitution errors. In Experiment 1, three brain-damaged patients, suffering from peripheral dyslexia, mainly characterized by substitutions, underwent an assessment of error distribution in reading pseudowords and a T detection task as a function of cue size and timing, in order to measure focal attention. Each patient, when compared to a control group, showed a deficit in adjusting the attentional focus. In Experiment 2, a group of 17 right-brain-damaged patients were asked to perform the focal attention task and to read single words and pseudowords as a function of inter-letter spacing. The results allowed us to confirm a more general association between substitution-type reading errors and the performance in the focal attention task.
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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 | 0.000 |
| 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".