The Synergistic Mind-Body Hypothesis: A Daoist Approach to Consciousness from the Perspective of Traditional Chinese Medicine
Bibliographic record
Abstract
This paper formulates a possible embodied account of consciousness by integrating the vantages of ancient Chinese philosophy and medicine and predictive processing theories in cognitive science. It draws on robust empirical evidence from across the cognitive sciences, including contemporaneous work in philosophy of mind, neuropsychiatry, clinical psychology, and cognitive neuroscience to build on an overarching proposal that consciousness is ultimately the product of a synergistic and exaptive relationship between mind and body. I call this formulation the synergistically affectively interoceptively synchronizing mind-body hypothesis (SAIS). This system is characterized by highly adaptive interoceptive active inference that is realized through the dynamic coordination of allostatic affective control and exteroception. Emerging research underscores parallels between the allostatic paradigm of predictive processing and the system of Qi in traditional Chinese medicine and Daoist conceptions of consciousness, supporting the notion that there lies much to be explored within the realm of the mind-body connection. Leveraging these insights, it is highly plausible that the key to understanding consciousness may lie in a revisit of the fundamental processes that govern cognition – allostasis, homeostasis, and autopoiesis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".