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<p>A publicly available, BIDS-compliant multimodal dataset is presented for studying the impact of digital information distraction on attentional processing. Thirty-eight young adults underwent fMRI scanning while performing a global-local attention task under three conditions: social notifications, non-social notifications, and a no-distraction baseline. The dataset includes task-based fMRI, high-resolution structural MRI, trial-level behavioral performance data, and a range of psychological questionnaires assessing digital-media tendencies, affective traits, and attentional control-related individual differences. Raw neuroimaging data, event files, and MRIQC-generated quality-control outputs are provided for all participants. This dataset supports diverse analytic approaches for exploring how different digital cues affect attentional allocation. By offering a well-documented and publicly accessible resource, the Data Descriptor promotes reproducible research and facilitates further investigations into the cognitive and neural mechanisms involved in attention within digitally saturated environments.</p>

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An open fMRI dataset examining the effects of online social and non-social information distraction on attention

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月7日

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  • Congcong Liu,
  • Xiangting Dai,
  • Meng Yuan,
  • Juan Kou

摘要

A publicly available, BIDS-compliant multimodal dataset is presented for studying the impact of digital information distraction on attentional processing. Thirty-eight young adults underwent fMRI scanning while performing a global-local attention task under three conditions: social notifications, non-social notifications, and a no-distraction baseline. The dataset includes task-based fMRI, high-resolution structural MRI, trial-level behavioral performance data, and a range of psychological questionnaires assessing digital-media tendencies, affective traits, and attentional control-related individual differences. Raw neuroimaging data, event files, and MRIQC-generated quality-control outputs are provided for all participants. This dataset supports diverse analytic approaches for exploring how different digital cues affect attentional allocation. By offering a well-documented and publicly accessible resource, the Data Descriptor promotes reproducible research and facilitates further investigations into the cognitive and neural mechanisms involved in attention within digitally saturated environments.