How clients “change emotion with emotion”: A programme of research on emotional processing
Bibliographic record
Abstract
This paper reviews a body of research that has examined Pascual-Leone and Greenberg's sequential model of emotional processing or used its accompanying measure (the Classification of Affective Meaning States). Research from 24 studies using a plurality of methods examined process-outcome relationships from micro to macro levels of observation and builds support for emotional transformation as a possible causal mechanism of change in psychotherapy. A pooled sample of 310 clinical and 130 sub-clinical cases have been studied, reflecting the process of 7 different treatment approaches in addressing over 5 different presenting clinical problems (including depression, anxiety, relational trauma, and personality disorders). The initial findings on this model support the hypothesis that emotional transformation occurs in specific canonical sequences and these show large effects in the prediction of positive treatment outcomes. This model is the first in the field of psychotherapy to show how non-linear temporal patterns of moment-by-moment process relate to the unfolding of increasingly larger changes to create good psychotherapy treatment outcomes. Finally, clinical application of the model is also considered as a template for case formulations focused on emotion. Clinical or methodological significance of this article: This review article examines research on a specific model of emotional processing. (i) Experiencing certain key emotions during psychotherapy seems to predict good treatment outcomes, at both the session and treatment levels. (ii) There is also evidence to suggest that these productive emotional experiences unfold in an ordered pattern. Moreover, (iii) support for this way of understanding emotional processing comes from a number of very different treatment approaches and for several kinds of major disorders.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".