What's in a line? Verbal, facial, and emotional influences on the line bisection task
Bibliographic record
Abstract
In Bryden and MacRae's [(1988). Dichotic laterality effects obtained with emotional words. Neuropsychiatry, Neuropsychology, and Behavioral Neurology, 1(3), 171-176] dichotic listening task, attending to verbal (left hemisphere) or emotional (right hemisphere) auditory stimuli can result in opposite patterns of behaviour. We examined whether performance on the line bisection task might also be influenced in opposite ways by left- and right-lateralized functions. The line bisection task is a simple and effective measure of visuospatial bias. Pseudoneglect, a leftward bias, is typically found on this task, and appears to result from right hemisphere dominance for spatial processing. We investigated how emotion, verbal, and facial processing impacted line bisection performance, while also examining the influence of hand-use. Line type (face, word, and solid), valence (positive, negative, and neutral), and hand-use (left, right, and both) were manipulated. Results indicated that face and word lines decreased and increased the extent of pseudoneglect, respectively, and that valence accentuated these results. These results were in the opposite direction from our predictions. Hand-use had little influence. We discuss the impact that visual scanning, and local and global processing, may have had on line bisection performance. Until future research clarifies how lateralized functions affect line bisections, we suggest caution in adapting the line bisection task as a general measure of relative hemispheric activation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".