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Title: 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.
Trauma narratives are a critical, exposure-based component of trauma-focused cognitive-behavioral therapy, yet community therapists rarely use them. Given evidence that intentions to deliver elements of cognitive behavioral therapy vary by component, and that intentions to deliver exposure are the weakest, this study focused specifically on trauma narratives. We drew on a social psychology causal theory (Theory of Planned Behavior (TPB)) and an implementation science framework (the Consolidated Framework for Implementation Research (CFIR)) to glean insight into multilevel influences on trauma narrative use. While the CFIR offers a broad list of factors potentially affecting implementation, the TPB offers causal pathways between individual-level constructs that predict behavior, including the uptake of an evidence-based intervention. The integration of these approaches may provide a more complete understanding of factors affecting therapists’ use of TNs.
Methods
Therapists (n=65) trained in trauma-focused cognitive behavioral therapy completed a survey about their use of and beliefs about trauma narratives. Content analysis was used to identify common beliefs about trauma narratives. A subset of participants (n=17) completed follow-up qualitative interviews, which were analyzed using an integrated approach informed by the CFIR.
Results
While most participants reported high intentions to use TNs, nearly half reported that they did not use TNs in the last 6 months. Survey data indicate a number of TPB-related determinants related to using trauma narratives. Qualitative interviews identified CFIR-relevant contextual factors that may influence constructs central to TPB.
Conclusions
These results highlight the importance of integrating approaches that address multiple theoretical determinants of therapist behavior, including therapist, organizational, and client factors with causal explanations to explain implementation behavior.
Song, Hayoung; Finn, Emily S.; Rosenberg, Monica D.(
, Proceedings of the National Academy of Sciences)
As we comprehend narratives, our attentional engagement fluctuates over time. Despite theoretical conceptions of narrative engagement as emotion-laden attention, little empirical work has characterized the cognitive and neural processes that comprise subjective engagement in naturalistic contexts or its consequences for memory. Here, we relate fluctuations in narrative engagement to patterns of brain coactivation and test whether neural signatures of engagement predict subsequent memory. In behavioral studies, participants continuously rated how engaged they were as they watched a television episode or listened to a story. Self-reported engagement was synchronized across individuals and driven by the emotional content of the narratives. In functional MRI datasets collected as different individuals watched the same show or listened to the same story, engagement drove neural synchrony, such that default mode network activity was more synchronized across individuals during more engaging moments of the narratives. Furthermore, models based on time-varying functional brain connectivity predicted evolving states of engagement across participants and independent datasets. The functional connections that predicted engagement overlapped with a validated neuromarker of sustained attention and predicted recall of narrative events. Together, our findings characterize the neural signatures of attentional engagement in naturalistic contexts and elucidate relationships among narrative engagement, sustained attention, and event memory.
Sajadi, Susan; Kellam, Nadia N.; Brunhaver, Samantha R.(
, American Society for Engineering Education)
In this methods paper, the development and utility of composite narratives will be explored. Composite narratives, which involve combining aspects of multiple interviews into a single narrative, are a relatively modern methodology used in the qualitative research literature for several purposes: to do justice to complex accounts while maintaining participant anonymity, summarize data in a more engaging personal form and retain the human face of the data, illustrate specific aspects of the research findings, enhance the transferability of research findings by invoking empathy, illuminate collective experiences, and enhance research impact by providing findings in a manner more accessible to those outside of academia. Composite narratives leverage the power of storytelling, which has shown to be effective in studies of neurology and psychology; i.e., since humans often think and process information in narrative structures, the information conveyed in story form can be imprinted more easily on readers’ minds or existing schema. Engineering education researchers have increasingly begun using narrative research methods. Recently, researchers have begun exploring composite narratives as an approach to enable more complex and nuanced understandings of engineering education while mitigating potential issues around the confidentiality of participants. Because this is a relatively new methodology in higher education more broadly and in engineering education specifically, more examples of how to construct and utilize composite narratives in both research and practice are needed. This paper will share how we created a composite narrative from interviews we collected for our work so that others can adapt this methodology for their research projects. The paper will also discuss ways we modified and enhanced these narratives to connect research to practice and impact engineering students. This approach involved developing probing questions to stimulate thinking, learning, and discussion in academic and industrial educational settings. We developed the composite narratives featured in this paper from fifteen semi-structured critical incident interviews with engineering managers about their perceptions of adaptability. The critical incidents shared were combined to develop seven composite narratives reflecting real-life situations to which engineers must adapt in the workplace. These scenarios, grounded in the data, were taken directly to the engineering classroom for discussion with students on how they would respond and adapt to the presented story. In this paper, we detail our process of creating one composite narrative from the broader study and its associated probing questions for research dissemination in educational settings. We present this detailed account of how one composite narrative was constructed to demonstrate the quality and trustworthiness of the composite narrative methodology and assist in its replication by other scholars. Further, we discuss the benefits and limitations of this methodology, highlighting the parts of the data brought into focus using this method and how that contrasts with an inductive-deductive approach to qualitative coding also taken in this research project.
Bhattacharya, Indrani; Foley, Michael; Ku, Christine; Zhang, Ni; Zhang, Tongtao; Mine, Cameron; Li, Manling; Ji, Heng; Riedl, Christoph; Welles, Brooke Foucault; et al(
, Proceedings of the 10th ACM Multimedia Systems Conference)
Studying group dynamics requires fine-grained spatial and temporal understanding of human behavior. Social psychologists studying human interaction patterns in face-to-face group meetings often find themselves struggling with huge volumes of data that require many hours of tedious manual coding. There are only a few publicly available multi-modal datasets of face-to-face group meetings that enable the development of automated methods to study verbal and non-verbal human behavior. In this paper, we present a new, publicly available multi-modal dataset for group dynamics study that differs from previous datasets in its use of ceiling-mounted, unobtrusive depth sensors. These can be used for fine-grained analysis of head and body pose and gestures, without any concerns about participants' privacy or inhibited behavior. The dataset is complemented by synchronized and time-stamped meeting transcripts that allow analysis of spoken content. The dataset comprises 22 group meetings in which participants perform a standard collaborative group task designed to measure leadership and productivity. Participants' post-task questionnaires, including demographic information, are also provided as part of the dataset. We show the utility of the dataset in analyzing perceived leadership, contribution, and performance, by presenting results of multi-modal analysis using our sensor-fusion algorithms designed to automatically understand audio-visual interactions.
Siu, Alexa; S-H Kim, Gene; O'Modhrain, Sile; Follmer, Sean(
, CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems)
Online data visualizations play an important role in informing public opinion but are often inaccessible to screen reader users. To address the need for accessible data representations on the web that provide direct, multimodal, and up-to-date access to the data, we investigate audio data narratives –which combine textual descriptions and sonification (the mapping of data to non-speech sounds). We conduct two co-design workshops with screen reader users to define design principles that guide the structure, content, and duration of a data narrative. Based on these principles and relevant auditory processing characteristics, we propose a dynamic programming approach to automatically generate an audio data narrative from a given dataset. We evaluate our approach with 16 screen reader users. Findings show with audio narratives, users gain significantly more insights from the data. Users describe data narratives help them better extract and comprehend the information in both the sonification and description.
Raccah, Omri, Chen, Phoebe, Gureckis, Todd_M, Poeppel, David, and Vo, Vy_A. The “Naturalistic Free Recall” dataset: four stories, hundreds of participants, and high-fidelity transcriptions. Scientific Data 11.1 Web. doi:10.1038/s41597-024-04082-6.
Raccah, Omri, Chen, Phoebe, Gureckis, Todd_M, Poeppel, David, and Vo, Vy_A.
"The “Naturalistic Free Recall” dataset: four stories, hundreds of participants, and high-fidelity transcriptions". Scientific Data 11 (1). Country unknown/Code not available: Nature Publishing Group. https://doi.org/10.1038/s41597-024-04082-6.https://par.nsf.gov/biblio/10558353.
@article{osti_10558353,
place = {Country unknown/Code not available},
title = {The “Naturalistic Free Recall” dataset: four stories, hundreds of participants, and high-fidelity transcriptions},
url = {https://par.nsf.gov/biblio/10558353},
DOI = {10.1038/s41597-024-04082-6},
abstractNote = {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.},
journal = {Scientific Data},
volume = {11},
number = {1},
publisher = {Nature Publishing Group},
author = {Raccah, Omri and Chen, Phoebe and Gureckis, Todd_M and Poeppel, David and Vo, Vy_A},
}
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