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 a cohort of 229 participants. Each participant listened to two stories, varying in duration from approximately 8 to 13 minutes, recorded by distinct 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 method to score memory performance for narrative content. Using this approach, we extend effects traditionally observed in classic list-learning paradigms. Finally, to facilitate the use of these rich data by the community, we offer a brief overview of recent computational methods used to automatically annotate and evaluate memory for narratives. All experimental materials, code and data are publicly available to facilitate new advances in understanding human memory.
Keywords
Bibtex entry:
@article{raccah2024freerecalldataset,
abstract = {The ``Naturalistic Free Recall Dataset" provides transcribed verbal recollections of four spoken narratives, collected from a cohort of 229 participants. Each participant listened to two stories, varying in duration from approximately 8 to 13 minutes, recorded by distinct 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 method to score memory performance for narrative content. Using this approach, we extend effects traditionally observed in classic list-learning paradigms. Finally, to facilitate the use of these rich data by the community, we offer a brief overview of recent computational methods used to automatically annotate and evaluate memory for narratives. All experimental materials, code and data are publicly available to facilitate new advances in understanding human memory.},
author = {Raccah, O. and Chen, P. and Gureckis, T.M. and Poeppel, D. and Vo, V.A.},
journal = {Scientific Data},
number = {1317},
title = {The "Naturalistic Free Recall" dataset: four stories, hundreds of participants, and high-fidelity transcriptions},
volume = {11},
year = {2024}}QR Code:
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