Changes in Situation Models Modulate Processes of Event Perception in Audiovisual Narratives
Changes in Situation Models Modulate Processes of Event Perception in Audiovisual Narratives
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DOI：
10.1037/a0036780
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发表时间：
2014-09-01
影响因子：
2.6
通讯作者：
Papenmeier, Frank
中科院分区：
文献类型：
--
作者：
Huff, Markus;Meitz, Tino G. K.;Papenmeier, Frank
人类通过在情境模型中在心理上代表文本和电影的内容来理解文本和电影。这些使用时间，地点，主角和动作等维度来描述情况。1个或多个维度的变化（e）。例如，在一个实施例中，一个新的角色进入场景）导致故事线的不连续性，并且通常被认为是两个有意义的单元之间的边界。事件知觉的最新理论进展导致了情境模型在工作记忆中以事件模型的形式表示的假设。这些事件模型在事件边界处更新。事件模型更新的时间点很重要：与正在进行的事件期间的情况相比，事件边界处的情况被更精确地记住，并且对接下来发生的事情的预测变得不那么可靠。我们假设这些影响取决于情境模型中变化的数量。在2个实验中，我们让参与者观看情景喜剧，并测量识别记忆和预测性能的事件边界，其中包含1，2，3或4个维度的变化。实验结果表明，维度变化越多，识别率越高，这是一个线性关系.与此同时，随着维度变化数量的增加，参与者的预测变得不那么可靠。这些结果表明，事件边界的事件模型的更新发生递增。
Humans understand text and film by mentally representing their contents in situation models. These describe situations using dimensions like time, location, protagonist, and action. Changes in 1 or more dimensions (e. g., a new character enters the scene) cause discontinuities in the story line and are often perceived as boundaries between 2 meaningful units. Recent theoretical advances in event perception led to the assumption that situation models are represented in the form of event models in working memory. These event models are updated at event boundaries. Points in time at which event models are updated are important: Compared with situations during an ongoing event, situations at event boundaries are remembered more precisely and predictions about what happens next become less reliable. We hypothesized that these effects depend on the number of changes in the situation model. In 2 experiments, we had participants watch sitcom episodes and measured recognition memory and prediction performance for event boundaries that contained a change in 1, 2, 3, or 4 dimensions. Results showed a linear relationship: the more dimensions changed, the higher recognition performance was. At the same time, participants' predictions became less reliable with an increasing number of dimension changes. These results suggest that updating of event models at event boundaries occurs incrementally.