Representation of real-world event schemas during narrative perception

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
presented with sixteen 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 can predict the script (restaurant vs. airport) of unlabeled data with high
accuracy, and can be 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 narrative stimuli.

理解电影与故事，需要构建高层次情境模型（situation model），该模型会剥离感知细节，用以描述当前正展开事件的场景、角色、行动及因果关系。此类模型的构建不仅依托当前叙事中的信息，还源自个体对图式化事件脚本的先验知识——此类脚本描述了个体一生中常接触的典型事件序列。本研究分析了44名人类受试者在观看16段时长三分钟的故事时采集的功能磁共振成像（fMRI）数据，这些故事包含源自两类不同脚本（餐厅用餐或机场通行）的4个图式化事件。除共享的脚本结构外，这些故事在角色与剧情上差异显著，且以两种差异极大的呈现模态展示：视听片段或口头旁白。一组受试者按完整的时间顺序观看这些故事，另一组对照组则以打乱的时间顺序呈现相同事件。包括后内侧皮层、内侧前额叶皮层（medial prefrontal cortex, mPFC）以及额上回在内的多个脑区，展现出可跨故事、受试者与呈现模态泛化的图式化事件模式。内侧前额叶皮层的活动模式还对整体脚本结构具有敏感性，时间顺序被打乱的事件会唤起更弱的图式化表征。借助隐马尔可夫模型（Hidden Markov Model），上述脑区的活动模式可高精度预测未标注数据所属的脚本类型（餐厅场景vs机场场景），还能用于对共享同一脚本的多段故事进行时间对齐。本研究结果将针对人类与动物实验中可控人工图式的感知研究，拓展至复杂叙事刺激的自然感知领域。