Elements of Dynamics IV: Neuronal Metaphors—Probing Neurobiology for Psychodynamic Meaning
Bibliographic record
Abstract
The essentiality of neuronal connections can be seen by the frenetic activity which dissociated neurons in culture exhibit in an attempt to re-establish functional connections. Through these interactions, neurons gain access to trophic factors, such as nerve growth factor, which determine whether they live or die. Connection and successful interaction with others is thus a biological imperative for neurons. The properties of a given neuron are determined both by the inherent genetic program and by changes in its genetic expression induced by cell-to-cell interaction. In other words, plasticity of neuronal function reflects not only cellular properties but is a consequence of its relationships. Since it is reasonable to assume that the emotional experience of organisms derives from the biological properties of their brain cells, it seems fruitful to probe the basic biology of these cells for metaphors that might yield insight into psychological properties. Clinical examples illustrate how using these concepts can enlighten the processes by which psychotherapeutic change can occur. In addition, perhaps understanding the underlying neurobiology can add scientific legitimacy to clinical psychodynamic work, and can reduce the tension between "biological" and "psychological" psychiatry. These ideas might be used to develop a new framework to strengthen the teaching of psychotherapy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.035 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".