From biology to physics and the unknown: What would it mean to understand consciousness?
Bibliographic record
Abstract
The three main ideas of the relation between the brain and the mind, Cartesian dualism, epiphenomenalism and brain–mind identity theory are critically reviewed. The point is made that none of these ideas, or their numerous variants, are based on explicit biological, or physical, mechanisms and are therefore not scientific in nature. By contrast, the Penrose–Hameroff orchestrated objective reduction theory does make testable biological predictions. I do not discuss the theory per se, but review two of its recent experimental tests for the purpose of urging caution in the interpretation of their results. A brief review of the neural correlates of consciousness follows. It is concluded that such experiments neither support nor falsify any of the three main ideas on the relation between brain and mind. First and foremost, science is experimental. Consequently, to bring the mind–brain problem in the realm of science requires that we directly measure conscious states the way that we measure electric current, or blood pressure, as examples. The entity of conscious state measurements will be referred to as ‘conscions’, and these must be causally linked to neural activity. If this were ever realized, a deep gap of understanding would persist. This is because of what I will refer to as Tyndall’s point. It can be summarized with a simple example as follows: if love were found to be associated with a right-handed turn of a given molecule and hate associated with its left-handed turn, then the question ‘why we have these feelings’ would remain unanswerable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.030 |
| Scholarly communication | 0.009 | 0.027 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".