Strategic automation of emotion regulation.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it