The effect of ocular dominance and interocular rivalry on monocular reading speed under near-normal, ganzfeld, and complete occlusion conditions
Bibliographic record
Abstract
Purpose: Normal reading typically involves binocular processes. However, in the case of monocular reading, the non-reading eye may interfere with the processes of the reading eye depending on the nature of the input to that eye. Furthermore, this interference may differ depending on whether the eye reading is the dominant or non-dominant eye. Method: The monocular reading speed of seventeen participants with normal vision was tested under six conditions. Three conditions tested the reading speed of the dominant eye while the non-dominant eye received patterned input, light input, or no input, and the other three conditions were similar for the non-dominant eye. Results: No difference in monocular reading speed was found between the dominant and non-dominant eye. A significant difference was found between patterned input and light input (p p Conclusions: While dominance does not seem to play a role in monocular reading, the level of input into the non-reading eye heavily affected monocular reading speed. Specifically, patterned input in the non-reading eye negatively affected monocular reading speed while light input and no light input did not. These results support the hypothesis that patterned input in the non-reading eye would most negatively affect reading speed as reading involves the interpretation of patterned information.
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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 | 0.004 |
| 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".