Hemispheric asymmetry for visual information processing in 3D space
Bibliographic record
Abstract
There is an asymmetry in the ability to process visual information in the left and right visual fields. Extensive literature has consistently shown that young, healthy human adults exhibit a visuospatial bias towards the left hemifield (pseudoneglect). This leftward bias has traditionally been demonstrated through horizontal line bisection tasks in 2D experimental settings. However, as research progressed into 3D space, where lines are presented far from the observer, the dissipation of the classical leftward bias tended to reverse into a rightward bias. The precise distances at which the leftward bias, a neutral point, and rightward biases occur remain unclear. Here, we present a meta-analysis to model how bisection performance changes across 3D space quantitatively. We identified the boundary conditions where leftward biases reverse into rightward biases and at what distances this change can be predicted using line bisection. A total of 30 samples (25 studies, 142 bisection-error effects, n = 720) were included. Overall, the analysis revealed a significant leftward bias within near space, followed by a rightward bias in far space. Three critical ranges for visuospatial asymmetries across depth were revealed in young, healthy adults: (1) significant leftward biases up to 48 cm, (2) no reliable leftward/rightward biases from 49 to 87 cm, and (3) significant rightward biases beyond 88 cm. In addition, we revealed significant moderating effects of participant age (50 + years old), the use of tools to perform bisection, and the control of retinal size across depth. The findings establish important benchmarks when investigating visuospatial asymmetries and could inform clinical assessment.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".