“I’m a fish!” Deepening receptivity to neurodiversity: a neuroscientifically informed integration of psychoanalytic psychotherapy, reciprocal prediction, and mindfulness
Bibliographic record
Abstract
Receptivity to our patients’ experience is a vital aspect of the psychoanalytic endeavor. As we receive incoming transmissions, we resonate with what is active in the patient. We hope to then jointly metabolize the experience. When we meet neurodiversity, realms of experience emerge that may elude us. Precipitously formulated ideas in the therapist, based on a neurotypical frame of reference, can impinge upon the discovery of our patients’ authentic world. How do we open ourselves to receive their true experience? This clinical narrative tracks the psychoanalytic travels of an individual who identifies as neurodivergent, who helped the therapist learn to deepen receptivity by dipping into a less differentiated place to follow the here-and-now experience from the bottom up. Throughout the journey, interweaving neuroscientific and psychoanalytic perspectives offered a powerful matrix from which an enriched understanding of our process could emerge. Psychoanalytic concepts including evenly suspended attention, unconscious-to-unconscious communication, alpha function, reverie, and negative capability are explored alongside neuroscientific insights into the stress response, mirror neurons, and the default mode network. A predictive coding lens introduced a view of the therapeutic exchange as a continuous reciprocal prediction, evoking the hypothesis that deepening receptivity required opening awareness to incoming signals and lessening the hold of prior predictions. To bring greater therapist awareness to the present moment and lessen the influence of self-referential evaluation, neuroscientifically-informed reflections also inspired the practice of mindfulness. Subsequent developments suggest that these approaches helped deepen receptivity to the experiences being communicated, leading to new understandings with transformative potential.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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".