Neural Mechanisms of Individuality - EEG Studies in Self and Morality
Bibliographic record
Abstract
The need for individual neural markers has been expressed in both basic and clinical neuroscience. To address this, we here designed a novel behavioural paradigm in which to test several measures as possible neural markers of individuality which distinguish participants from each other in how they perceive, feel and perform cognitive tasks. The individualized paradigm for consequentialist moral dilemmas was validated, showing variability across participants in thresholds and reaction times. Next, task-induced activity changes in EEG activity during the time interval of the Late Positive Potential (LPP) in alpha power, along with phase coherence early in the trial, correlated with reaction times and scores of subjective emotional distress. From these findings in study one, in study two we measured trial-to-trial variability (TTV) and found that the TTV index in the alpha and beta bands correlated with reaction time and prestimulus Lempel-Ziv Complexity. These findings, again in the alpha and beta bands, support alpha power during the LPP, variability quenching in these bands, and early intertrial coherence as markers of neural individuality. Finally, measures of scale-free activity in the resting state, along with others, and self-consciousness scale subscores as indices of the self were investigated. It was found that the power-law exponent, autocorrelation window, modulation index and electromagnetic tomography activity in two Default Mode Network areas correlated significantly with the Private subscore of the Self-Consciousness Scale only. These findings indicate that these resting state measures, along with activity in the DMN, may serve as markers of neural individuality in the brain’s spontaneous activity.
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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 | 0.002 |
| 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".