Improving Peripheral Reading With Noninvasive Transcranial Electrical Stimulation of Early Visual Areas
Bibliographic record
Abstract
Purpose: Noninvasive transcranial electrical stimulation (tES) of the primary visual cortex can reduce crowding in peripheral vision. We investigated the effect of two tES protocols, visual cortex transcranial direct current stimulation (tDCS) and transcranial random noise stimulation (tRNS), on reading using peripheral vision, a task that is limited by visual crowding. Methods: A double-blind, placebo-controlled experimental design was used. Forty-one heathy adults with normal visual acuity completed one active and one sham tES session. During each session, English sentences were presented one word at a time 10 degrees below fixation. The proportion of words read correctly was assessed before, during, after and 30 minutes after real or sham tDCS (n = 21) or tRNS (n = 20). Results: The tRNS elicited a small but significant performance benefit (P = 0.035), whereas tDCS had no effect. Strong within-session learning effects were observed for all conditions (P = 0.001). Conclusions: These results add to a growing body of evidence indicating that noninvasive stimulation of the visual cortex can enhance visual processing and may have applications in vision rehabilitation. Translational Relevance: Noninvasive brain stimulation may enhance how the brain processes visual input from a diseased eye, complementing standard eye-based treatments of retinal and macular diseases.
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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".