Emotional gradients in autobiographical memories across the lifespan
Bibliographic record
Abstract
Autobiographical memory is the mental re-experiencing of complex events from one’s life (Grilli and Sheldon, 2019). Memories are not perfect reproductions – rather, they are reconstructed to serve one’s goals, identity, and social communication. Emotions are known to modulate memory. In this study, we investigated changes in the emotional phenomenology of autobiographical memories across different life periods. 65 participants (ages 22-35) completed the Autobiographical Interview (Levine et al., 2002), in which they verbally recalled memories from childhood, adolescence, adulthood, and the past year; they also recalled a negative memory (from any period) and imagined a future memory. Recall occurred in random order across two sessions to control for state effects. Memory emotionality was scored in multiple ways. Participants self-reported the vividness and emotion (valence, arousal, and dominance) within their memory. Raters evaluated the emotion of each memory on a 1-9 Likert scale (intraclass correlation coefficient = 0.90). Finally, transcripts were analyzed with LIWC-22 to determine the average emotion of words within each memory. Across scoring methods, we found participants consistently reported greater vividness in their memory from adulthood compared to childhood. We observed gradients in emotion across the lifetime: childhood memories were more negative and less dominant, while adulthood memories were more positive and more dominant. Childhood memories contained more words associated with negative emotions, especially anger. Imagined future memories were more positive compared to childhood memories. Our results extend previous findings of a negative-to-positive temporal gradient in self-selected memories (Williamson et al., 2019) by showing this gradient to apply not only when emotional memories are prompted, but also when memories are freely retrieved without special instruction. We also show the pattern to extend to future, imagined memories. We speculate that this gradient may explain why self-appraisal is typically more positive and negative for future and past selves, respectively.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".