Engagement and Disengagement: From the Basic Science of Emotion Regulation to an Anxiety Spectrum
Bibliographic record
Abstract
Emotion regulation strategies vary in depth of processing. For instance, reappraisal requires greater engagement than distraction. This affects short-term and long-term response to stimuli. In this review, I describe how the "engagement-disengagement dimension" improves understanding of emotion regulation in normative contexts and in internalizing psychopathology. Part 1 reviews work from my laboratory and others, suggesting that relatively disengaged emotion regulation strategies (e.g., distraction), may have short-term benefits (e.g., faster implementation), but may come with long-term costs (e.g., increased processing of stimuli at subsequent encounter). Therefore, depending on the desired outcome, the adaptive selection of an emotion regulation strategy will be determined by extent of emotional engagement-disengagement. In Part 2, I describe how individuals with more comorbid internalizing psychopathology (e.g., multiple anxiety and depressive diagnoses) are characterized by disengagement from negative stimuli as measured by the late positive potential (LPP). In addition, I introduce a brain profile I have termed, HARM-A (heightened "alarm" and reduced motivated attention), which is characterized by a combination of heightened "alarm" (i.e., increased amygdala) and emotional disengagement (i.e., blunted LPPs) in response to negative stimuli. HARM-A prospectively predicts worse outcomes over 2 years in a mixed internalizing sample. As such, chronic disengagement from negative stimuli appears to contribute to more comorbid and more severe internalizing psychopathology. Overall, emotional disengagement can be beneficial in the short term but may be poorly suited to emotional coping in the longer term.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".