Reliving emotional personal memories: Affective biases linked to personality and sex-related differences.
Bibliographic record
Abstract
Although available evidence suggests that the emotional valence and recollective properties of autobiographical memories (AMs) may be influenced by personality- and sex-related differences, overall these relationships remain poorly understood. The present study investigated these issues by comparing the effect of general personality traits (extraversion and neuroticism) and specific traits linked to emotion regulation (ER) strategies (reappraisal and suppression) on the retrieval of emotional AMs and on the associated postretrieval emotional states, in men and women. First, extraversion predicted recollection of positive AMs in both men and women, whereas neuroticism predicted the proportion of negative AMs in men and the frequency of rehearsing negative AMs in women. Second, reappraisal predicted positive AMs in men, and suppression predicted negative AMs in women. Third, while reliving of positive memories had an overall indirect effect on postretrieval positive mood through extraversion, reliving of negative AMs had a direct effect on postretrieval negative mood, which was linked to inefficient engagement of suppression in women. Our findings suggest that personality traits associated with positive affect predict recollection of positive AMs and maintenance of a positive mood, whereas personality traits associated with negative affect, along with differential engagement of habitual ER strategies in men and women, predict sex-related differences in the recollection and experiencing of negative AMs. These findings provide insight into the factors that influence affective biases in reliving AMs, and into their possible link to sex-related differences in the susceptibility to affective disorders.
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.
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 | 0.000 | 0.003 |
| 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 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".