Effects of Regulating Positive Emotions through Reappraisal and Suppression on Verbal and Non-Verbal Recognition Memory
Bibliographic record
Abstract
Previous research has suggested that regulating emotions through reappraisal does not incur cognitive costs. However, in those experiments, cognitive costs were often assessed by recognition memory for information that was contextually related to the emotionally evocative stimuli and may have been incorporated into the reappraisal script, facilitating memory. Furthermore, there is little research on the cognitive correlates of regulating positive emotions. In the current experiment, we tested memory for information that was contextually unrelated to the emotional stimuli and could not easily be related to the reappraisal. Participants viewed neutral and mildly positive slides and either reappraised, suppressed their emotions, or viewed the images with no emotion regulation instruction. At the same time, they heard abstract words that were unrelated to the picture stimuli. Subsequent verbal recognition memory was lower after reappraising than viewing, whereas non-verbal recognition memory (of the slides) was higher after reappraising, but only for positive pictures and when participants viewed the positive pictures first. Suppression had no significant effect on either verbal or non-verbal recognition scores, although there was a trend towards poorer recognition of verbal information. The findings support the notion that reappraisal is effortful and draws on limited cognitive resources, causing decrements in performance in a concurrent memory task.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".