用连续脑电捕捉电影转场时的神经反应,揭示叙事理解的可测量信号。
Transition-Related Potentials as Markers of Narrative Comprehension in Continuous EEG

- 在观看连续电影时,提取转场时刻的脑电响应(TRP)
- TRP响应受叙事上下文影响,与人工标注结果一致
- 仅用深度网络即可从连续记录中自动检测转场信号
利用脑电图(EEG)研究大脑活动受限于固有噪声及信号在头皮上的弥散投影。传统事件相关电位(ERP)依赖重复独立试验,但偏离自然情境。本文通过让受试者观看短片并提取画面剪辑(转场)时刻的连续脑电信号,发现转场相关电位(TRPs)具有类似ERP的时间结构,反映显著的信息处理。对比连贯影片与场景打乱版本(感官输入相同),发现响应受叙事上下文系统性调控。进一步使用小型深度神经网络(DNN)从群体平均的连续记录中直接恢复剪辑相关脑电特征,该检测方法跨影片和被试组泛化良好,恢复的TRPs重现了人工标注剪辑的主要上下文依赖效应。结果表明,叙事理解会在脑电中留下可测量信号,且能直接从连续记录中探测,为分析观众如何理解影视叙事提供了半自动化框架。该方法可推广至其他连续刺激场景,成为更贴近真实人类体验的实验工具。
原文摘要 · Abstract (English)
Harnessing the potential of electroencephalography (EEG) for brain research is fundamentally limited by intrinsic noise and the diffuse projection of brain-generated activity over the scalp. The standard event-related potential (ERP) paradigm addresses this limitation by relying on repeated independent trials, albeit at the cost of moving away from naturalistic experimental conditions. As a more naturalistic alternative, we collected continuous EEG while participants watched short films and extracted potentials aligned to sharp cinematic transitions (cuts). We demonstrate that such transition-related potentials (TRPs) exhibit canonical ERP-like temporal structure associated with significant information processing. By comparing coherent films with scene-scrambled versions containing matched post-cut sensory input, we find that these responses are systematically shaped by narrative context. We then show that the cut-related EEG signature can be recovered directly from group-averaged continuous recordings with a compact deep neural network (DNN). The detector generalized across films and subject groups, and the resulting TRPs reproduced the main context-dependent effects observed for manually annotated cuts. These results indicate that narrative context leaves a measurable signature in EEG responses, that this signature can be detected directly in continuous recordings, and that such detections provide a semi-automated framework for analyzing how viewers process and understand film narratives. We propose that the method outlined here can be adapted to parse EEG responses to other forms of continuous stimulation, providing a general tool for probing experimental conditions that are closer to natural human experience.
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