Abstract
Autobiographical memory and episodic future thinking (i.e. the capacity to project oneself into an imaginary future) are typically assessed using the Autobiographical Interview (AI). In the AI, subjects are provided with verbal cues (e.g. “your wedding day”) and are asked to freely recall (or imagine) the cued past (or future) event. Narratives are recorded, transcribed and analyzed using an established manual scoring procedure (Levine et al., 2002). Here we applied automatic text feature extraction methods to a relatively large (n = 86) set of AI data. In a first proof-of-concept approach, we used regression models to predict internal (episodic) and semantic detail sum scores from low-level linguistic features. Across a range of different regression methods, prediction accuracy averaged at about 0.5 standard deviations. Given the known association of episodic future thinking with temporal discounting behavior, i.e. the preference for smaller-sooner over larger-later rewards, we also ran models predicting temporal discounting directly from linguistic features of AI narratives. Here, prediction accuracy was much lower, but involved the same text feature components as prediction of internal (episodic) details. Our findings highlight the potential feasibility of using tools from quantitative text analysis to analyze AI datasets, and we discuss potential future applications of this approach.
Highlights
Autobiographical memory (AM) is central to our personal identity, and changes in this process characterize developmental phases as well as effects of neurological and psychiatric disorders
Internal details ratings (t85 = 5.0057, p < 0.001) and external details ratings (t85 = 2.236, p = 0.028) where higher for AM than EFT, whereas semantic details ratings were higher for EFT than AM (t85 = 2.5189, p = 0.014)
We explore for the first time methods to analyze autobiographical interview (AI) data using automatic extraction of low-level linguistic features
Summary
Autobiographical memory (AM) is central to our personal identity, and changes in this process characterize developmental phases as well as effects of neurological and psychiatric disorders. One way to assess this type of behavior is via temporal discounting In these paradigms, the relative preference for smaller-sooner rewards over larger-but-later rewards is measured[10,11]. Recent years have brought forth increasing empirical support for the idea that EFT may, under certain conditions, modulate temporal discounting in this manner[12,13,14,15,16,17,18] These interactions are relevant for psychiatry, since steep discounting is a reliable behavioral marker www.nature.com/scientificreports/. Typical outcome measures of an AI study include sum scores of the number of episodic details (often termed internal details, as they pertain directly to the central event in question), sum scores for external details (episodic details not pertaining to the event in question) and sum scores for semantic details (non-episodic information). The present study provides a first step towards a more automatic and quantitative analysis of linguistic content in AI data by extracting low-level linguistic features from AI narratives in a largely automatic fashion
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