Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, New Jersey 08544
Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, New Jersey 08544.
J Neurosci. 2018 Nov 7;38(45):9689-9699. doi: 10.1523/JNEUROSCI.0251-18.2018. Epub 2018 Sep 24.
Understanding movies and stories requires maintaining a high-level situation model that abstracts away from perceptual details to describe the location, characters, actions, and causal relationships of the currently unfolding event. These models are built not only from information present in the current narrative, but also from prior knowledge about schematic event scripts, which describe typical event sequences encountered throughout a lifetime. We analyzed fMRI data from 44 human subjects (male and female) presented with 16 three-minute stories, consisting of four schematic events drawn from two different scripts (eating at a restaurant or going through the airport). Aside from this shared script structure, the stories varied widely in terms of their characters and storylines, and were presented in two highly dissimilar formats (audiovisual clips or spoken narration). One group was presented with the stories in an intact temporal sequence, while a separate control group was presented with the same events in scrambled order. Regions including the posterior medial cortex, medial prefrontal cortex (mPFC), and superior frontal gyrus exhibited schematic event patterns that generalized across stories, subjects, and modalities. Patterns in mPFC were also sensitive to overall script structure, with temporally scrambled events evoking weaker schematic representations. Using a Hidden Markov Model, patterns in these regions predicted the script (restaurant vs airport) of unlabeled data with high accuracy and were used to temporally align multiple stories with a shared script. These results extend work on the perception of controlled, artificial schemas in human and animal experiments to naturalistic perception of complex narratives. In almost all situations we encounter in our daily lives, we are able to draw on our schematic knowledge about what typically happens in the world to better perceive and mentally represent our ongoing experiences. In contrast to previous studies that investigated schematic cognition using simple, artificial associations, we measured brain activity from subjects watching movies and listening to stories depicting restaurant or airport experiences. Our results reveal a network of brain regions that is sensitive to the shared temporal structure of these naturalistic situations. These regions abstract away from the particular details of each story, activating a representation of the general type of situation being perceived.
理解电影和故事需要维持一个高层次的情境模型,该模型从感知细节中抽象出来,描述当前展开事件的位置、角色、动作和因果关系。这些模型不仅是根据当前叙述中的信息构建的,还根据关于典型事件脚本的先验知识构建,这些知识描述了一生中遇到的典型事件序列。我们分析了 44 名人类受试者的 fMRI 数据,他们观看了 16 个三分钟的故事,这些故事由来自两个不同脚本的四个示意性事件组成(在餐厅用餐或通过机场)。除了这种共享的脚本结构之外,这些故事在角色和故事情节方面差异很大,并且以两种截然不同的格式呈现(视听剪辑或口语叙述)。一组以完整的时间顺序呈现故事,而另一组独立的对照组则以打乱的顺序呈现相同的事件。包括后内侧皮层、内侧前额叶皮层(mPFC)和额上回在内的区域表现出了跨故事、受试者和模态的示意性事件模式。mPFC 中的模式也对整体脚本结构敏感,时间上打乱的事件引起的示意性表示较弱。使用隐马尔可夫模型,这些区域的模式可以高精度地预测未标记数据的脚本(餐厅与机场),并用于以共享脚本对多个故事进行时间对齐。这些结果将人类和动物实验中对受控、人工图式的感知工作扩展到对复杂叙事的自然感知。在我们日常生活中遇到的几乎所有情况下,我们都能够利用我们关于世界上通常发生的事情的示意性知识来更好地感知和心理表示我们正在经历的事情。与以前使用简单的人工联想研究示意性认知的研究不同,我们从观看电影和听描述餐厅或机场体验的故事的受试者那里测量了大脑活动。我们的结果揭示了一个对这些自然情境共享时间结构敏感的大脑区域网络。这些区域从每个故事的特定细节中抽象出来,激活了正在感知的一般类型情境的表示。