The Trajectory of Pseudoneglect in Adults: A Systematic Review
Bibliographic record
Abstract
Neurologically healthy adults tend to display a reliable leftward perceptual bias during visuospatial tasks, a phenomenon known as pseudoneglect. However, the phenomenon in older adults is not well understood, and a synthesis of research that examines pseudoneglect using the line bisection task, as well as other tasks, in the context of aging is lacking. The aim of the current systematic review is to integrate the available research on pseudoneglect in late adulthood, and to discuss the association between age and a bias to the left hemispace. The systematic search revealed that five different tasks have been used to examine pseudoneglect in younger and older adults, and that participants over 60 years of age have demonstrated inconsistent perceptual biases (e.g., enhanced leftward bias, suppressed leftward bias, and rightward bias). Based on current evidence, although some age-related trends in the perceptual bias can be identified within each task, no firm conclusions about the effects of age on pseudoneglect can be drawn. A number of recommendations for future research are outlined throughout the review, including use of smaller age ranges within age groups, differentiating between neurologically healthy participants and those with clinical diagnoses, continued examination of gender, and consistent use of stimuli and methods of analyses within each task to improve internal comparability.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".