Neural event segmentation of continuous experience in human infants

How infants experience the world is fundamental to understanding their cognition and development. A key principle of adult experience is that, despite receiving continuous sensory input, we perceive this input as discrete events. Here we investigate such event segmentation in infants and how it differs from adults. Research on event cognition in infants often uses simplified tasks in which (adult) experimenters help solve the segmentation problem for infants by defining event boundaries or presenting discrete actions/vignettes. This presupposes which events are experienced by infants and leaves open questions about the principles governing infant segmentation. We take a different, data-driven approach by studying infant event segmentation of continuous input. We collected whole-brain fMRI data from awake infants (and adults, for comparison) watching a cartoon and used a hidden Markov model to identify event states in the brain. We quantified the existence, timescale, and organization of..., Please refer to the manuscript for details. Citation: Yates, T. S., Skalaban, L. J., Ellis, C. T., Bracher, A. J., Baldassano, C., & Turk-Browne, N. B. (2022). Neural event segmentation of continuous experience in human infants. Proceedings of the National Academy of Sciences., Please refer to the README for guidance on how to use this data.,婴儿如何感知周遭世界，是理解其认知与发展的核心议题。成人感知的核心原则之一是：尽管接收的是连续的感官输入，我们仍会将其拆解为离散的事件。本研究聚焦婴儿的事件分割能力，以及其与成人的差异之处。过往针对婴儿事件认知的研究，常采用简化实验范式：由成人实验者通过界定事件边界或呈现离散动作与场景片段，替婴儿完成事件分割任务。这类研究预设了婴儿所感知的事件范畴，却未探讨支配婴儿事件分割的核心原则，遗留了诸多悬而未决的问题。本研究采用截然不同的数据驱动范式，探究婴儿对连续输入的事件分割过程。我们采集了清醒状态下婴儿（及作为对照的成人）观看卡通片时的全脑功能磁共振成像（fMRI）数据，并通过隐马尔可夫模型（hidden Markov model）识别大脑中的事件状态。我们对事件的存在性、时间尺度与组织模式进行了量化分析，详细内容请参阅手稿。 引用格式：Yates, T. S., Skalaban, L. J., Ellis, C. T., Bracher, A. J., Baldassano, C., & Turk-Browne, N. B. (2022). 人类婴儿连续体验的神经事件分割. 美国国家科学院院刊. 关于该数据集的使用方法，请参阅README文件。