Multicomponent Multimethod Assessment of Emotional Change in Psychotherapy Research: Initial Validation of a Neurobehavioral Paradigm
Bibliographic record
Abstract
Abstract Self-contempt and emotional arousal are two key concepts associated with psychological distress but have been little studied in a daily life context. This work explores the use of individualized self-contemptuous stimuli extracted from a self-critical two-chair dialogue into an fMRI scanner. 28 female controls participated in psychological investigations (at three time points) and a self-critical emotion-eliciting two-chair dialogue followed by an fMRI assessment. We observed the neurofunctional activation during this task and compared neural activation during the exposition to self-critical individualized stimuli versus negative non-individualized stimuli. We also investigated emotional arousal change during the psychological session. The fMRI data analysis showed no significant difference in activation between the first and second fMRI assessments. We found no significant activation when comparing the neural activation between the exposition to self-contemptuous individualized stimuli and non-individualized negative stimuli. Controls do show an increased self-reported emotional arousal when expressing self-contempt. Our neurobehavioral design seems promising as proof of concept in combining an analogue psychotherapy session and an fMRI session to investigate expressed self-contempt and emotional arousal in healthy controls. Using this design in clinical populations seems feasible and may be important in clinical populations known for emotional difficulties such as BPD.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".