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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".