Semi-automated transcription and scoring of autobiographical memory narratives

Abstract

Autobiographical memory studies conducted with narrative methods are onerous, requiring significant resources in time and labour. We have created a semi-automated process that allows autobiographical transcribing and scoring methods to be streamlined. Our paper focuses on the Autobiographical Interview (AI; Levine et al., 2002) but this method can be adapted for other narrative protocols. Specifically, here we lay out a procedure that guides researchers through the four main phases of the autobiographical narrative pipeline: (1) data collection, (2) transcribing, (3) scoring, and (4) analysis. First, we provide recommendations for incorporating transcription software to augment human transcribing. We then introduce an electronic scoring procedure for tagging narratives for scoring that incorporates the traditional AI scoring method with basic keyboard shortcuts in Microsoft Word. Finally, we provide a Python script that can be used to automate counting scored transcripts. This method accelerates the time it takes to conduct a narrative study and reduces opportunity for error in narrative quantification. Available open access on GitHub (https://github.com/cMadan/scoreAI), our pipeline makes narrative methods more accessible for future research.

Fade In, Fade Out: Do Shifts in Visual Perspective Predict the Consistency of Real-World Memories?

“…Because details unrelated or tangential to the event being recalled, such as information about other events (e.g., "just like the last time we went to the beach") or semantic knowledge (e.g., "I like rocky beaches more than sandy beaches"), are not inherent to the accuracy of the recall, we considered details related to the specific episode only (i.e., internal details). Internal AI details were further divided into detail categories: event (i.e., what happened, who was there), perceptual (i.e., sensations and percepts), emotion/ thoughts (i.e., emotions and thoughts), place (i.e., location), and time (i.e., temporal setting), in accordance with the AI protocol (Levine et al, 2002;Wardell, Esposito et al, 2021).…”

Automated scoring of the autobiographical interview with natural language processing

“…Previous work consists of two approaches: speeding up the processes involved in scoring, and predicting the number of internal and external details. For example, Wardell et al (2021a) automated the process of transcribing spoken narratives to text with Dragon NaturallySpeaking software. The researchers also reduced the time necessary for scoring by setting up keyboard shortcuts in Microsoft Word.…”

Evoking episodic and semantic details with instructional manipulation during autobiographical recall.

“…Scorers were only given the list of memories with a participant number, without indication of the age group. We used keyboard shortcuts to label the types of details and an automated approach to count the labeled details (to facilitate scoring and reduce human error, as suggested by Wardell et al, 2021). A script developed in MATLAB (Mathworks, Inc.) for this purpose is available at https://osf.io/srw2c/.…”