Bibliographic record
Abstract
Cerebral damage to the optic radiations or striate cortex causes ‘homonymous hemianopia’, blindness in the contralateral half of the visual field in each eye. In cultures that read left to right, a right hemianopia has a severe effect on reading efficiency, particularly when the central 5° are lost, because most of the information acquired during reading lies in the right parafoveal field. We explored the importance of right hemifield loss in diagnosing pure alexia (Experiments 1, 2). Later, we assessed the feasibility of an online training program for the rehabilitation of hemianopic dyslexia, and the eye-movement changes that might accompany learning (Experiment 3). In the first two experiments, human subjects performed several visual processing tasks, using a simulated hemianopia gaze-contingent display. We found that hemianopia alone can account for some previously reported impairments in pure alexia. Subsequently, we provide diagnostic criterion for using the word-length effect to discriminate between hemainopic dyslexia and pure alexia for various types of central involvement by right hemifield loss. In the final experiment, a pilot rehabilitation study, two patients with hemianopic dyslexia performed a 10-week on-line perceptual learning task to increase reading span, and improve reading efficiency. Following training, benefits were limited to an increase in the size of forward saccades in one patient (patient JW). We conclude that this training approach is feasible, though further studies are needed to establish efficacy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".