Native reading direction influences lateral biases in the perception of shape from shading
Bibliographic record
Abstract
Although neurologically normal individuals often exhibit leftward biases of perception and attention, known as pseudoneglect, factors such as lighting, spatial location and native reading direction have been found to modulate these biases. To investigate lighting and spatial biases in left-to-right and right-to-left readers search times were measured in a target finding task where lighting and target locations were manipulated. Target search times under upper-left lighting were significantly shorter than lower-left, upper-right and lower-right lighting among left-to-right readers. Right-to-left readers did not display the same leftward bias, even displaying significantly shorter search times under upper-right lighting than those of left-to-right readers. Significantly shorter search times for targets located in the upper-left quadrant (compared to other quadrants) were observed for left-to-right readers, while search times for upper-right located targets were significantly shorter for right-to-left readers compared to those of left-to-right readers. Participant scan times of stimuli divided into equal quadrants were monitored by an eye-tracking camera. Both groups displayed greater scan times in upper quadrants. These findings suggest that native reading direction modulates spatial and light perception biases resulting in weaker leftward, or a lack of lateral biases among right-to-left readers.
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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 | 0.002 |
| 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".