Representation of Real-World Event Schemas during Narrative Perception
Representation of Real-World Event Schemas during Narrative Perception
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DOI：
10.1523/jneurosci.0251-18.2018
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发表时间：
2018-11-07
影响因子：
5.3
通讯作者：
Norman, Kenneth A.
中科院分区：
文献类型：
--
作者：
Baldassano, Christopher;Hasson, Uri;Norman, Kenneth A.
理解电影和故事需要保持一个高层次的情境模型，从感知细节中抽象出来，描述当前正在展开的事件的位置、角色、动作和因果关系。这些模型不仅是建立在当前的叙述中的信息，但也从先验知识的示意性事件脚本，它描述了典型的事件序列在整个生命周期中遇到的。我们分析了来自44名人类受试者（男性和女性）的fMRI数据，这些受试者提供了16个三分钟的故事，包括从两种不同的脚本（在餐馆吃饭或通过机场）中提取的四个示意性事件。除了这种共享的剧本结构之外，这些故事在人物和故事情节方面也有很大差异，并且以两种高度不同的格式（视听剪辑或口语旁白）呈现。一组被试以完整的时间顺序呈现故事，而另一组被试则以混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）和上级额回在内的区域表现出了跨故事、主题和模式的示意性事件模式。mPFC的模式也敏感的整体脚本结构，与时间混乱的事件唤起较弱的示意性表示。使用隐马尔可夫模型，这些区域中的模式以高精度预测了未标记数据的脚本（餐厅与机场），并用于将多个故事与共享脚本进行时间对齐。这些结果扩展了在人类和动物实验中对受控的人工图式的感知的工作，以自然主义的复杂叙事感知。
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.