Emotional Expressivity in Older and Younger Adults' Descriptions of Personal Memories
Bibliographic record
Abstract
UNLABELLED: BACKGROUND/STUDY CONTEXT: According to the socioemotional selectivity theory (SST; Mather & Carstensen, 2003, Psychological Sciences, 14, 409-415), aging is associated with greater motivation to regulate emotions. The authors propose that the language people use to describe personal memories provides an index of age differences in emotional self-regulation. METHODS: In the present article, the authors reanalyzed three previously published studies in which older (aged 60-88) and younger (aged 17-33) participants described emotional and neutral memories from their recent and distant pasts. The authors analyzed the language of the memories using Pennebaker, Booth, and Francis's (2007) Linguistic Inquiry Word Count program (Austin, TX: LIWC Inc.), which calculates the percentage of positive and negative emotion words. RESULTS: In Studies 1 and 2, older adults used more positive emotion words than did younger adults to describe their autobiographical memories from the recent past, particularly when these were of a neutral valence. In Study 3, older adults used more positive emotion words when describing more recent memories (from the past 5 years) but not when describing distant childhood or adolescent memories. CONCLUSION: The authors suggest that these age differences in emotional expressivity support SST, and represent an as-yet unreported age difference that may stem from differences in motivation to regulate emotion.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".