Varieties of Clinical Intuition: Explicit, Implicit, and Nonlocal Neurodynamics
Bibliographic record
Abstract
Intuitive response has been a cornerstone of patient–therapist interactions in all schools of therapy. In addition, persistent instances of “uncanny” intuitive knowing, such as “thought transference,” telepathic/precognitive dreams, distant awareness, and synchronicity have been identified since the very beginnings of psychoanalysis. These phenomena have remained on the fringes of scientific exploration, partly because of the lack of a conceptual model that would bring them into the mainstream of clinical work. The authors propose a Nonlocal Neurodynamics model that complements classical local-interactive forms of sensory (verbal and nonverbal) communication with nonlocal-participatory informational channels arising from the fundamental quantum/classical nature of the body/brain/mind system. We suggest the need for a metaphor shift in psychoanalysis in order to incorporate the latest developments in complexity science and quantum neurobiology, which allow for a meta-reductive informational perspective that bridges the Cartesian mind-brain divide and enables a unified picture of psychophysical reality. We use clinical examples illustrating a full spectrum of local and nonlocal clinical intuition to help clinicians utilize these concepts in their daily work.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.026 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".