Behavioural asymmetries on the greyscales task: The influence of native reading direction
Bibliographic record
Abstract
Reliable leftward attentional and perceptual biases demonstrated in a variety of visuospatial tasks have been found to deviate from the left in research examining the influence of scanning habits. The aim of the current research was to examine the influence of native script direction on pseudoneglect during the greyscales task in a representative sample of native right-to-left readers. Fifty-four native left-to-right readers and 43 right-to-left readers completed the greyscales task, which required judging the darker of two left-right mirrored brightness gradients. Native left-to-right readers demonstrated a left response bias on the greyscales task, whereas right-to-left readers failed to demonstrate a bias, however, both groups responded more quickly when making leftward choices. The research suggests that the strength of attentional biases are influenced by behavioural biases, such as scanning habits, and neural and anatomical asymmetries in the right parietal and frontal cortices. Thus, to improve the clinical utility of the greyscales task for diagnosing neglect, right-to-left readers should be examined to fully understand the normal range of biases displayed by neurologically healthy individuals.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".