Attentional Modulation of Emotional Lateralization Biases with Verbal and Nonverbal Stimuli
Bibliographic record
Abstract
Within hemispheric asymmetry literature, emotional processing appears to be predominately right lateralized; however, this degree of lateralization seems more complex when the stimuli engage with multiple functions lateralized across hemispheres, such as language, face perception, and spatial attention. Using the divided visual field paradigm, our online experiment employs a 2x2 design to examine the scope of emotion laterality by comparing lateralization biases when processing neutral and valence-laden stimuli in both verbal and nonverbal modalities. The study also employs a modified “Posner’s task” to investigate attentional modulation of hemispheric biases. Our preliminary findings pertaining to neutral face and neutral word perception did not show hemispheric bias although both revealed strong cueing effects. Subsequent experiments will investigate how attention cueing impacts hemispheric performances and how these patterns interact with emotion. This research aids to quantify hemispheric interactions when processing emotional stimuli and informs the treatment of mood disorders using non-invasive brain stimulation.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".