Bridging the gap of brain and experience – Converging Neurophenomenology with Spatiotemporal Neuroscience
Bibliographic record
Abstract
Neuroscience faces the challenge of connecting brain and mind, with the mind manifesting in first-person experience while the brain's neural activity can only be investigated in third-person perspective. To connect neural and mental states, Neurophenomenology provides a methodological toolkit for systematically linking first-person subjective experience with third-person objective observations of the brain's neural activity. However, beyond providing a systematic methodological strategy ('disciplined circularity'), it leaves open how neural activity and subjective experience are related among themselves, independent of our methodological strategy. The recently introduced Spatiotemporal Neuroscience suggests that neural activity and subjective experience share a commonly underlying feature as their "common currency", notably analogous spatiotemporal dynamics. Can Spatiotemporal Neuroscience inform Neurophenomenology to allow for a deeper and more substantiative connection of first-person experience and third-person neural activity? The goal of our paper is to show how Spatiotemporal Neuroscience and Neurophenomenology can be converged and integrated with each other to gain better understanding of the brain-mind connection. We describe their convergence on theoretical grounds which, subsequently, is illustrated by empirical examples like self, meditation, and depression. In conclusion, we propose that the integration of Neurophenomenology and Spatiotemporal Neuroscience can provide complementary insights, enrich both fields, allows for deeper understanding of brain-mind connection, and opens the door for developing novel methodological approaches in their empirical investigation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".