Behavioural and eye-movement outcomes in response to text-based reading treatment for acquired alexia
Bibliographic record
Abstract
Text-based reading treatments, such as Multiple Oral Rereading (MOR) and Oral Reading for Language in Aphasia (ORLA) have been used successfully to remediate reading impairments in individuals with acquired alexia, but the mechanisms underlying such improvements are not well understood. In this study, an individual with acquired alexia who demonstrated reliance on a sub-lexical reading strategy (i.e., presence of spelling regularity effect and phonologically plausible errors) underwent 12 weeks of text-based reading treatment combining MOR and ORLA procedures. Behavioural assessments of single-word and text reading, along with eye-tracking assessments were conducted pre-treatment, post-treatment and at 5 month follow-up. Improved reading fluency (rate, accuracy) was observed for both trained and untrained passages. Evidence from behavioural and eye-tracking assessment suggested text-based reading treatment facilitated use of a lexical-semantic reading strategy. Increased frequency and lexicality effects, as well as a shift in initial landing position towards the centre of the word (the "optimal viewing position") were observed at post-treatment and follow-up assessments. These results demonstrate the potential utility of using eye movements as a parameter of interest in addition to traditional behavioural outcomes when investigating response to reading treatment.
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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) | 0.000 | 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 | 0.000 |
| 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".