Strategic automation of emotion regulation.
Bibliographic record
Abstract
As implementation intentions are a powerful self-regulation tool for thought and action (meta-analysis by P. M. Gollwitzer & P. Sheeran, 2006), the present studies were conducted to address their effectiveness in regulating emotional reactivity. Disgust- (Study 1) and fear- (Study 2) eliciting stimuli were viewed under 3 different self-regulation instructions: the goal intention to not get disgusted or frightened, respectively, this goal intention furnished with an implementation intention (i.e., an if-then plan), and a no-self-regulation control group. Only implementation-intention participants succeeded in reducing their disgust and fear reactions as compared to goal-intention and control participants. In Study 3, electrocortical correlates (using dense-array electroencephalography) revealed differential early visual activity in response to spider slides in ignore implementation-intention participants, as reflected in a smaller P1. Theoretical and applied implications of the present findings for emotion regulation via implementation intentions are discussed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".