Neurophenomenology: An Introduction for Neurophilosophers
Bibliographic record
Abstract
Introduction One of the major challenges facing neuroscience today is to provide an explanatory framework that accounts for both the subjectivity and neurobiology of consciousness. Although neuroscientists have supplied neural models of various aspects of consciousness, and have uncovered evidence about the neural correlates of consciousness (or NCCs), there nonetheless remains an ‘explanatory gap’ in our understanding of how to relate neurobiological and phenomenological features of consciousness. This explanatory gap is conceptual, epistemological, and methodological: An adequate conceptual framework is still needed to account for phenomena that (ⅰ) have a first-person, subjective-experiential or phenomenal character; (ⅱ) are (usually) reportable and describable (in humans); and (ⅲ) are neurobiologically realized. The conscious subject plays an unavoidable epistemological role in characterizing the explanandum of consciousness through first-person descriptive reports. The experimentalist is then able to link first-person data and third-person data. Yet the generation of first-person data raises difficult epistemological issues about the relation of second-order awareness or meta-awareness to first-order experience (e.g., whether second-order attention to first-order experience inevitably affects the intentional content and/or phenomenal character of first-order experience). The need for first-person data also raises methodological issues (e.g., whether subjects should be naïve or phenomenologically trained). Neurophenomenology is a neuroscientific research program whose aim is to make progress on these issues associated with the explanatory gap. In this chapter we give an overview of the neurophenomenological approach to the study of consciousness.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.016 | 0.010 |
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".