Affective Change in Psychodynamic Psychotherapy: Theoretical Models and Clinical Approaches to Changing Emotions
Bibliographic record
Abstract
OBJECTIVES: Affective change has been considered the hallmark of therapeutic change in psychoanalysis. Psychoanalytic writers have begun to incorporate theoretically the advanced understanding of emotional processing and transformation of the affective neurosciences. We ask if this theoretical advancement is reflected in treatment techniques addressing the processing of emotion. METHODS: We review psychoanalytic models and treatment recommendations of maladaptive affect processing in the light of a neuroscientifically informed model of achieving psychotherapeutic change by activation and reconsolidation of emotional memory. RESULTS: Emotions tend to be treated as other mental contents, resulting in a lack of specific psychodynamic techniques to work with emotions. Manualized technical modifications addressing affect regulation have been successfully tested in patients with personality pathology, but not for psychodynamic treatments of axis I disorders. CONCLUSIONS: Emotional memories need to be activated in order to be modified, therefore, we propose to include techniques into psychodynamic therapy that stimulate emotional 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".