Handedness modulates emotion cognition interactions as revealed by ERPs and alpha oscillations
Bibliographic record
Abstract
Emotion and cognition are interrelated lateralized brain functions, yet their interaction may vary with handedness due to differences in hemispheric specialization. We investigate how handedness modulates emotion-cognition interactions using electroencephalography/event-related potentials (EEG/ERPs) in healthy young adults. Participants completed a hemi-field emotional oddball task, where visually identical circles appeared in either the left or right visual field, and fearful or neutral distractors were shown centrally. Circles on the block-specific "frequent" side served as standard stimuli, while those on the opposite side were spatially defined oddball targets. Neural activity was recorded using a 256-channel EEG system. Fearful distractors elicited greater ERP amplitudes (P100, P300, LPP) and increased alpha oscillations, reflecting heightened emotional processing and attentional engagement. Right-handers showed more adaptive engagement, with post-fear alpha suppression and enhanced P300 and LPP amplitudes to targets in right parietal sites, indicating efficient attentional reallocation via right hemisphere mechanisms. In contrast, left-handers exhibited bilateral ERP responses to fear and sustained bilateral alpha activity during subsequent target processing, suggesting greater reliance on inhibitory control to manage emotional carryover, but with reduced attentional reengagement. These findings reveal distinct lateralized patterns of emotion-attention interactions based on handedness, offering new insights into brain asymmetry and informing personalized educational and clinical approaches.
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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) | 0.001 | 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 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".