Reading without eye movements: Improving reading comprehension in young adults with attention-deficit/hyperactivity disorder (ADHD)
Bibliographic record
Abstract
OBJECTIVES: In ADHD a common obstacle of academic success is impaired reading comprehension. Impaired comprehension in ADHD is accompanied by altered eye movements during reading as well as more general eye movement deficits associated with non-verbal stimuli. This suggests that the reading deficits do not cause the eye movement impairment. Instead, eye movements might contribute to reading comprehension difficulties. METHODS: We tested whether minimizing the need for eye movements during reading aids comprehension. We measured reading comprehension in a sample of undergraduate students with and without ADHD. Students read short paragraphs using normal text reading with all words fully visible (FULL), PACED reading that preserved text layout with one word at a time appearing at its usual location in the text, and reading with minimal eye movements in which one word at a time appeared in the center of the screen in a rapid serial visual presentation (RSVP). RESULTS: ADHD participants performed better in the RSVP condition relative to the other two reading conditions that required eye movements, and they benefited from the RSVP condition requiring minimal eye movements by almost 13% relative to neurotypical controls, who showed comprehension difficulties using the RSVP mode. CONCLUSIONS: Minimizing eye movement boosted reading comprehension in the ADHD suggesting that eye movements are implicated in reading processes in ADHD, an interference that can be avoided in the RSVP reading condition. Future work should explore the possibility of RSVP as a reading aid in ADHD adults and potentially school-aged children.
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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) | 0.002 | 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".