Mapping the anatomy of perceptual pseudoneglect. A multivariate approach
Bibliographic record
Abstract
Fundamental to the understanding of the functions of spatial cognition and attention is to clarify the underlying neural mechanisms. It is clear that relatively right-dominant activity in ventral and dorsal parieto-frontal cortex is associated with attentional reorienting, certain forms of mental imagery and spatial working memory for higher loads, while lesions mostly to right ventral areas cause spatial neglect with pathological attentional biases to the right side. In contrast, complementary leftward biases in healthy people, called pseudoneglect, have been associated with varying patterns of cortical activity. Notably, this inconsistency may be explained, at least in part, by the fact that pseudoneglect studies have often employed experimental paradigms that do not control sufficiently for cognitive processes unrelated to pseudoneglect. To address this issue, here we administered a carefully designed continuum of pseudoneglect and control tasks in healthy adults while using functional magnetic resonance imaging (fMRI). Data submitted to partial least square (PLS) imaging analysis yielded a significant latent variable that identified a right-dominant network of brain regions along the intra-occipital and -parietal sulci, frontal eye fields and right ventral cortex in association with perceptual pseudoneglect. Our results shed new light on the interplay of attentional and cognitive systems in pseudoneglect.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".