Bibliographic record
Abstract
Abstract The human brain engages the sense of self through both semantic and somatic self-referential processing (SRP). Alpha and theta oscillations have been found to underlie SRP but have not been compared with respect to semantic and somatic SRP. We recorded electroencephalography (EEG) from 50 participants during focused internal attention on life roles (e.g. “friend”) and outer body (e.g. “arms”) compared to resting state and an external attention memory task and localized the sources of on-scalp alpha (8–12 Hz) and theta (4–8 Hz) EEG signals with exact low-resolution tomography. Logarithm of F-ratios was calculated to compare differences in alpha and theta power between SRP conditions, resting state, and external attention. Results indicated that compared to resting state, semantic SRP induced lower theta in the frontal cortex and higher theta in the parietal cortex, whereas somatic SRP induced lower alpha in the frontal and insula cortex and higher alpha in the parietal cortex. Furthermore, results indicated that compared to external attention, both semantic and somatic SRP induced higher alpha in the dorsolateral prefrontal cortex with lateralized patterns based on task condition. Finally, an analysis directly comparing semantic and somatic SRP indicated frontal–parietal and left–right lateralization of SRP in the brain. Our results suggest the alpha and theta oscillations in the frontal, parietal, and the insula cortex may play crucial roles in semantic and somatic SRP.
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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) | 0.002 | 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".