Abstract
Real-world memories often include our internal thoughts and feelings, yet memory research has largely centered on experimentally controlled external stimuli. As a result, it remains underexplored how such internal experiences are preserved or transformed in naturalistic autobiographical recall, and whether they serve functional roles. To address this gap, we analyzed a large-scale dataset of autobiographical narratives in which 210 crowdsourced participants recalled a specific memorable life event twice, several weeks apart, and rated their memories along multiple dimensions. We combined manual annotation with natural language processing to identify and analyze individual memory details, categorized as either observable external experiences or subjective internal experiences. We found that internal experiences were more prone to omission and semantic distortion over time compared to external ones. However, those with higher emotional intensity and stronger semantic connections to external event features were more likely to be retained. Memories richer in internal experiences were also judged as more important, and semantically precise reinstatement of internal experiences across recalls predicted increased importance over time. Together, these findings show that although memories of internal thoughts and feelings are relatively fragile, they nonetheless play a meaningful role in shaping the subjective significance of autobiographical memory.
Data availability
The original Hippocorpus dataset45 is publicly available on the Microsoft website (https://www.microsoft.com/en-us/download/details.aspx?id=105291). A preprocessed version of the subset used in the current study is publicly available on the Open Science Framework (https://osf.io/a65bg).
Code availability
The analyses in this study were conducted using publicly available Python and R packages. For text similarity analysis, we used a pretrained model (all-mpnet-base-v2) implemented in Sentence Transformers (v2.2.0). For sentiment analysis, we used the VADER sentiment analysis tool implemented in the NLTK library (v3.8.1). We used the lme4 (v1.1-35.5) package to perform mixed-effects logistic regression, psych (v2.5.6) to compute Williams tests, cocor (v1.1.4) to obtain confidence intervals and Cohen’s q for correlation comparisons, irr (v0.84.1) to estimate Fleiss’s κ, and MBESS (v4.9.42) to compute confidence intervals for ANOVA effect sizes in R. For other statistical tests, we used Pingouin (v0.5.1), Scipy (v1.12.0), and Scikit-learn (v1.2.2) in Python. Analysis scripts are publicly available on the Open Science Framework (https://osf.io/a65bg).
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Acknowledgements
We thank Yoolim Hong, Alyssa McMahon, and Taylor Korn for their assistance with preprocessing the recall narrative data.
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H.L. conceived and designed the research. C.K. preprocessed the data. H.S., M. Zhang, and H.L. analyzed the data. H.S., M. Zhang, C.K., and H.L. wrote the original paper. H.S., M. Zhang, M. Zhao, and H.L. reviewed and edited the paper.
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Su, H., Zhang, M., Knight, C. et al. Retention and transformation of internal experiences in autobiographical memory narratives. Commun Psychol (2026). https://doi.org/10.1038/s44271-026-00425-8
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DOI: https://doi.org/10.1038/s44271-026-00425-8