Quantitative text feature analysis of autobiographical interview data: prediction of episodic details, semantic details and temporal discounting

TY - JOUR

T1 - Quantitative text feature analysis of autobiographical interview data: prediction of episodic details, semantic details and temporal discounting

AU - Peters, J

AU - Wiehler, A

AU - Bromberg, U

PY - 2017/11/8

Y1 - 2017/11/8

N2 - 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.

AB - 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.

KW - Journal Article

U2 - 10.1038/s41598-017-14433-6

DO - 10.1038/s41598-017-14433-6

M3 - SCORING: Journal article

C2 - 29118403

VL - 7

SP - 14989

JO - SCI REP-UK

JF - SCI REP-UK

SN - 2045-2322

IS - 1

ER -