面向神经启发的叙事计算模型(特邀演讲)

Jeffrey M. Zacks
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引用次数: 0

摘要

本着今年会议神经科学主题的精神,我将描述一组认知和神经生理现象,这些现象对话语层面的叙事文本处理很重要。文本处理依赖于语言的顺序结构以及语言所描述的事件。事件的语义表征捕获了所描述情景的感知和运动特性,利用了以前的生活经验。尽管有大量的神经系统参与叙事文本的处理,但有限制的系统子集有选择地参与话语层面的处理。为了将这些现象结合在一起,我将提出一个简单的视觉事件处理的神经启发计算模型,该模型可能为语言处理的某些特征提供有益的类比。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Toward Neurally-Inspired Computational Models of Narrative (Invited Talk)
In the spirit of the neuroscience theme of this year's meeting, I will describe a set of cognitive and neurophysiological phenomena that are important for the processing of narrative text at the discourse level. Text processing depends on sequential structure in language and also in the events that language describes. Semantic representations of events capture perceptual and motor properties of described situations, leveraging previous lived experience. Although a large number of neural systems are involved in processing narrative text, a constrained subset of systems are selectively engaged by discourse-level processing. To bring these phenomena together, I will present a simple neurally-inspired computational model of visual event processing that may provide a helpful analogy for some features of language processing.
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