The Price of Needing to Belong: Neurobiology of Working Through Attachment Trauma
Bibliographic record
Abstract
Belonging is fundamental to health and well-being. Complex relational trauma disrupts attachments, negatively impacting developing neurobiology and has significant implications for attachment behaviors, mental health, and treatment planning. We have developed a dynamic relational (DR) model of psychotherapy that aims to restore a healthy sense of belonging, targeting levels of activation and integration of large scale neural networks in the service of increasing the emotional capacities (attunement, processing, regulation, and expression) required to work through attachment trauma and establish healthy relationships. Our DR model provides an organizing framework through which to understand both the phenomenology observed in complex trauma and the mechanisms of therapeutic change. Our approach informs the weighting and timing of interventions to actively address capacity deficits, ego-syntonic symptoms, and unconscious resistance. The implications of this model also relate to the pathogenesis of mental disorder, and suggest prevention and early intervention efforts focus on modulation of subcortical (autonomic) responses and the encouragement of balanced cortical integration to enhance cognitive flexibility/psychological resilience. Ultimately, interventions based on our systematic model may modulate the genetic diathesis and comorbidities of relational trauma and increase psychological resilience.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".