When philosophical nuance matters: safeguarding consciousness research from restrictive assumptions
Bibliographic record
Abstract
In this paper, we revisit the debate surrounding the Unfolding Argument (UA) against causal structure theories of consciousness (as well as the hard-criteria research program it prescribes), using it as a platform for discussing theoretical and methodological issues in consciousness research. Causal structure theories assert that consciousness depends on a particular causal structure of the brain. Our claim is that some of the assumptions fueling the UA are not warranted, and therefore we should reject the methodology for consciousness science that the UA prescribes. First, we briefly survey the most popular philosophical positions in consciousness science, namely physicalism and functionalism. We discuss the relations between these positions and the behaviorist methodology that the UA assumptions express, despite the contrary claim of its proponents. Second, we argue that the same reasoning that the UA applies against causal structure theories can be applied to functionalist approaches, thus proving too much and deeming as unscientific a whole range of (non-causal structure) theories. Since this is overly restrictive and fits poorly with common practice in cognitive neuroscience, we suggest that the reasoning of the UA must be flawed. Third, we assess its philosophical assumptions, which express a restrictive methodology, and conclude that there are reasons to reject them. Finally, we propose a more inclusive methodology for consciousness science, that includes neural, behavioral, and phenomenological evidence (provided by the first-person perspective) without which consciousness science could not even start. Then, we extend this discussion to the scope of consciousness science, and conclude that theories of consciousness should be tested and evaluated on humans, and not on systems considerably different from us. Rather than restricting the methodology of consciousness science, we should, at this point, restrict the range of systems upon which it is supposed to be built.
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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 | 0.149 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.008 | 0.099 |
| Scholarly communication | 0.016 | 0.052 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.012 | 0.029 |
| 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".