The function of consciousness is to generate experience
Bibliographic record
Abstract
Why would we do anything at all if the doing was not doing something to us? In other words: What is consciousness good for? Here, reversing classical views, according to many of which subjective experience is a mere epiphenomenon that affords no functional advantage, we propose that the core function of consciousness is precisely to enable subject-level experience. “What it feels like” is endowed with intrinsic value, and it is precisely the value agents associate with their experiences that explains why we do certain things and avoid others. Thus, we argue that it is only in virtue of the fact that conscious agents experience things and care about those experiences that they are motivated to act in certain ways and that they prefer some states of affairs vs. others. In this sense, conscious experience functions as a mental currency of sorts, which not only endows mental states with intrinsic value, but also makes it possible for conscious agents to compare vastly different experiences in a common subject-centered space — a feature that readily explains the fact that consciousness is unified. If, as we argue, the function of consciousness is to endow agents with subjective experience, then the hard problem of consciousness seems to dissolve.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.018 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".