Cases of Unconventional Information Flow Across the Mind-Body Interface
Bibliographic record
Abstract
Neuroscience, and behavioral science more broadly, seek to characterize the relationship between functional cognition and the underlying processes operating in living tissue. The current paradigm focuses heavily on the brain, and specific mechanisms thought to underlie mental content and capabilities. One of the most interesting approaches to any field, which often leads to progress, is to highlight data which do not comfortably fit a specific dominant framework. Here, we review clinical and laboratory data in several unconventional systems which are not predicted by the current models in the field. Reduced brain mass or absent brain tissue without the expected loss of function (e.g. hydrocephalus, hemihydranencephaly), discrepancies between cognitive state and brain function (e.g. accidental awareness during anesthesia, terminal lucidity), and cases of cognitive abilities exceeding the apparent skill of the individual, all highlight interesting features of the immense plasticity of the mapping between cognition and its living substrate. These cases suggest new avenues for research that at the very least stretch existing frameworks, and parallels to discoveries being made in the emergent form and behavior of synthetic constructs. We speculate on a roadmap for the study of interesting and still poorly-understood features of embodied minds that could be impactful for biomedicine and engineering, as well as foundational philosophical issues.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".