The "Naturalistic Free Recall" dataset: four stories, hundreds of participants, and high-fidelity transcriptions.

Abstract

The "Naturalistic Free Recall" dataset provides transcribed verbal recollections of four spoken narratives collected from 229 participants. Each participant listened to two stories, varying in duration from approximately 8 to 13 minutes, recorded by different speakers. Subsequently, participants were tasked with verbally recalling the narrative content in as much detail as possible and in the correct order. The dataset includes high-fidelity, time-stamped text transcripts of both the original narratives and participants' recollections. To validate the dataset, we apply a previously published automated method to score memory performance for narrative content. Using this approach, we extend effects traditionally observed in classic list-learning paradigms. The analysis of narrative contents and its verbal recollection presents unique challenges compared to controlled list-learning experiments. To facilitate the use of these rich data by the community, we offer an overview of recent computational methods that can be used to annotate and evaluate key properties of narratives and their recollections. Using advancements in machine learning and natural language processing, these methods can help the community understand the role of event structure, discourse properties, prediction error, high-level semantic features (e.g., idioms, humor), and more. All experimental materials, code, and data are publicly available to facilitate new advances in understanding human memory.