Visual cortex anodal tDCS does not alter reading performance for Chinese presented character-by-character to normal peripheral vision
Bibliographic record
Abstract
Abstract Visual cortex transcranial direct current stimulation (tDCS) reduces crowding in normal peripheral vision and may improve reading of English words in patients with macular degeneration. Given the different visual requirements of reading English words and Chinese characters, the effect of tDCS on peripheral reading performance in English might be different from Chinese. This study recruited seventeen participants (59 to 73 years of age) with normal vision and tested the hypothesis that anodal tDCS would improve reading of Chinese characters presented at 10° eccentricity compared with sham stimulation. Chinese sentences of different print sizes and exposure durations were presented one character at a time 10° below or to the left of fixation, and the individual critical print size (CPS) - the smallest print size eliciting the maximum reading speed (MRS) was determined. Reading accuracies for characters presented 0.2 logMAR smaller than the CPS were measured before, during, 5 mins, and 30 mins after receiving active or sham anodal visual cortex tDCS. Participants completed both the active and sham sessions in a random order following a double-blind, within-subject design. No effect of active anodal-tDCS on reading accuracy was observed, implying that a single session of tDCS did not improve Chinese character reading in normal peripheral vision. This may suggest that tDCS does not significantly reduce the crowding elicited within a single Chinese character. However, the effect of tDCS on between-character crowding is yet to be determined.
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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".