The Human Self Has Two Serial Aspects and Is Dynamic: A Concept Based on Neurophysiological Evidence Supporting a Multiple Aspects Self Theory (MAST)
Bibliographic record
Abstract
The self is an increasingly central topic in current neuroscience. Understanding the neural processes that are involved in self-referential processing and functioning may also be crucial to understanding consciousness. The current short communication goes beyond the typical concept that the self is singular, as has been assumed from neuroanatomical descriptions of the self by fMRI and PET studies. Long ago, theoretically, the idea of multiple aspects of the human self-arose, highlighting a dynamic organizational structure, but an increasing number of electrophysiological brain imaging studies, searching for the temporal dynamics of self-referential brain processes, now has empirical evidence supporting their existence. This short communication focuses on the theoretical idea of a dynamic self and provides first preliminary empirical evidence, including results from own studies of the authors, in support of, and highlights the serial dynamics of the human self, suggesting a primitive Me1 and an elaborate Me2 (a non-personal and a personal self). By focusing on the temporal dimension of the self, we propose that multiple aspects of the self can be distinguished based on their temporal sequence. A multiple aspects Self Theory (MAST) is proposed. This model is meant as a theoretical framework for future studies providing further support.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".