Harnessing psychoanalytical methods for a phenomenological neuroscience
Bibliographic record
Abstract
Psychoanalysis proffers a wealth of phenomenological tools to advance the study of consciousness. Techniques for elucidating the structures of subjective life are sorely lacking in the cognitive sciences; as such, experiential reporting techniques must rise to meet both complex theories of brain function and increasingly sophisticated neuroimaging technologies. Analysis may offer valuable methods for bridging the gap between first-person and third-person accounts of the mind. Using both systematic observational approaches alongside unstructured narrative interactions, psychoanalysts help patients articulate their experience and bring unconscious mental contents into awareness. Similar to seasoned meditators or phenomenologists, individuals who have undergone analysis are experts in discerning and describing their subjective experience, thus making them ideal candidates for neurophenomenology. Moreover, analytic techniques may provide a means of guiding untrained experimental participants to greater awareness of their mental continuum, as well as gathering subjective reports about fundamental yet elusive aspects of experience including selfhood, temporality, and inter-subjectivity. Mining psychoanalysis for its methodological innovations provides a fresh turn for the neuropsychoanalysis movement and cognitive science as a whole - showcasing the integrity of analysis alongside the irreducibility of human experience.
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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 | 0.055 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.005 | 0.051 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.008 |
| 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".