On the axiomatic foundations of the integrated information theory of consciousness
Bibliographic record
Abstract
The integrated information theory (IIT) is one of the most influential scientific theories of consciousness. It functions as a guiding framework for a great deal of research into the neural basis of consciousness and for attempts to develop a consciousness meter. In light of these developments, it is important to examine whether its foundations are secure. This article does just that by examining the axiomatic method that the architects of IIT appeal to. I begin by asking what exactly the axiomatic method involves, arguing that it is open to multiple interpretations. I then examine the five axioms of IIT, asking: what each axiom means, whether it is indeed axiomatic and whether it could constrain a theory of consciousness. I argue that none of the five alleged axioms is able to play the role that is required of it, either because it fails to qualify as axiomatic or because it fails to impose a substantive constraint on a theory of consciousness. The article concludes by briefly sketching an alternative methodology for the science of consciousness: the natural kind approach.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.022 |
| Scholarly communication | 0.006 | 0.015 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".