The influence of self-regulatory focus on encoding of, and memory for, emotional words
Bibliographic record
Abstract
We investigated self-regulatory focus (Higgins, 1997 Higgins, E. T. 1997. Beyond pleasure and pain. American Psychologist, 52(12): 1280–1300. [Crossref], [PubMed], [Web of Science ®] , [Google Scholar], 1998 Higgins, E. T. 1998. Promotion and prevention: Regulatory focus as a motivational principle. Advances in Experimental Social Psychology, 46: 1–46. [Google Scholar]) as one source of variation in encoding of, and memory for, emotional words. Participants wrote about their hopes and aspirations (promotion focus) or duties and obligations (prevention focus). In a subsequent incidental encoding task during functional magnetic resonance imaging (fMRI), participants evaluated emotional (positive and negative) and neutral words as either good or bad. A surprise memory test followed, outside the scanner. We observed a dissociation in posterior cingulate cortex (PCC), where activity during the evaluation task was greater when words were focus-consistent (positive for the promotion focus group, negative for the prevention focus group). Similarly, activity in a parahippocampal region was related to subsequent memory, but only for focus-consistent words. Given the role of the PCC in self-referential processing and episodic retrieval, and the parahippocampus in memory-related processing, these data suggest that regulatory focus influences which items are preferentially associated with self-referential information in memory. Such preferential processing may help explain why events are remembered differently by different individuals, which subsequently may influence interpersonal interactions.
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".