On the origins and evolution of qualia: An experience-space perspective
Bibliographic record
Abstract
This paper elaborates on a proposal for mapping a configuration space for selector circuits (SCs), defined as the subset of neural correlates of consciousness (NCCs) responsible for evoking particular qualia, to its experiential counterpart, experience-space (E-space), as part of an investigation into the nature of conscious experience as it first emerged in evolution. The dimensionality of E-space, meaning the degrees of freedom required to specify the properties of related sets of qualia, is at least two, but the utility of E-space as a hypothetical construct is much enhanced by assuming it is a large dimensional space, with at least several times as many dimensions as there are categories of qualia to occupy them. Phenomenal consciousness can then be represented as having originated as one or more multidimensional ur-experiences that combined multiple forms of experience together. Taking this as a starting point, questions concerning evolutionary sequence can be addressed, including how the quale best suited to a given sensory modality would have been extracted by evolution from a larger set of possibilities, a process referred to here as dimensional sorting, and how phenomenal consciousness would have been experienced in its earliest manifestations. There is a further question as to whether the E-space formulation is meaningful in analytical terms or simply a descriptive device in graphical form, but in either case it provides a more systematic way of thinking about early stages in the evolution of consciousness than relying on narrative and conjecture alone.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.023 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".