ArticleBehavior research methods2024
Automated scoring of the autobiographical interview with natural language processing.
Article in Behavior research methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effects of Healthy and Neuropathological Aging on Autobiographical Memory: A Meta-Analysis of Studies Using the Autobiographical Interview.The journals of gerontology. Series B, Psychological sciences and social sciences · 2023Pooled it
- Impaired autobiographical remembering and future thinking in temporal lobe epilepsy.Epilepsy & behavior : E&B · 2026Article
- Natural language processing captures memory content associated with shared neural patterns at encoding and retrieval.Communications psychology · 2026Article
- Memory justifications provide valid indicators of retrieval accuracy across time.Communications psychology · 2025Article
- Event segmentation applications in large language model enabled automated recall assessments.Communications psychology · 2025Article
- Rich autobiographical memory benefits from both novelty and similarity to other daily experiences.Scientific reports · 2025Article
- Similarity is associated with where repeated-event memories fall on the semantic-episodic continuum.Memory & cognition · 2025Article
- Rich autobiographical memory recall benefits from both novelty and similarity to other daily experiences.Research square · 2025Article
- Modeling memories, predicting prospections: Automated scoring of autobiographical detail narration using large language models.Behavior research methods · 2025Article
- Assessing autobiographical memory consistency: Machine and human approaches.Behavior research methods · 2025Article
- From Llama to language: prompt-engineering allows general-purpose artificial intelligence to rate narratives like expert psychologists.Frontiers in artificial intelligence · 2025Article
- What's in a cue?: Using natural language processing to quantify content characteristics of episodic future thinking in the context of overweight and obesity.Health psychology and behavioral medicine · 2025Article
- The "Naturalistic Free Recall" dataset: four stories, hundreds of participants, and high-fidelity transcriptions.Scientific data · 2024Article
- Natural language processing reveals differences in mental time travel at higher levels of self-efficacy.Scientific reports · 2024Article
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- Collective events and individual affect shape autobiographical memory.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- The role of gestures in autobiographical memory.PloS one · 2023Article
- The influence of shifting perspective on episodic and semantic details during autobiographical memory recall.Memory (Hove, England) · 2022Article
- Language-agnostic, Automated Assessment of Listeners' Speech Recall Using Large Language Models.Trends in hearingArticle
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2 authors.
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Abstract
The autobiographical interview has been used in more than 200 studies to assess the content of autobiographical memories. In a typical experiment, participants recall memories, which are then scored manually for internal details (episodic details from the central event) and external details (largely non-episodic details). Scoring these narratives requires a significant amount of time. As a result, large studies with this procedure are often impractical, and even conducting small studies is time-consuming. To reduce scoring burden and enable larger studies, we developed an approach to automatically score responses with natural language processing. We fine-tuned an existing language model (distilBERT) to identify the amount of internal and external content in each sentence. These predictions were aggregated to obtain internal and external content estimates for each narrative. We evaluated our model by comparing manual scores with automated scores in five datasets. We found that our model performed well across datasets. In four datasets, we found a strong correlation between internal detail counts and the amount of predicted internal content. In these datasets, manual and automated external scores were also strongly correlated, and we found minimal misclassification of content. In a fifth dataset, our model performed well after additional preprocessing. To make automated scoring available to other researchers, we provide a Colab notebook that is intended to be used without additional coding.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.