Emotional Expressivity in Older and Younger Adults' Descriptions of Personal Memories
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
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.
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) | 0.002 | 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 it