Stem Cell and Brain Research Institute, INSERM U846, 69676 Bron Cedex, France.
Brain Lang. 2010 Mar;112(3):180-8. doi: 10.1016/j.bandl.2009.07.001. Epub 2009 Aug 7.
This article addresses issues in embodied sentence processing from a "cognitive neural systems" approach that combines analysis of the behavior in question, analysis of the known neurophysiological bases of this behavior, and the synthesis of a neuro-computational model of embodied sentence processing that can be applied to and tested in the context of human-robot cooperative interaction. We propose a Hybrid Comprehension Model that links compact propositional representations of sentences and discourse with their temporal unfolding in situated simulations, under the control of grammar. The starting point is a model of grammatical construction processing which specifies the neural mechanisms by which language is a structured inventory of mappings from sentence to meaning. This model is then "embodied" in a perceptual-motor system (robot) which allows it access to sentence-perceptual representation pairs, and interaction with the world providing the basis for language acquisition. We then introduce a "simulation" capability, such that the robot has an internal representation of its interaction with the world. The control of this simulator and the associated representations present a number of interesting "neuro-technical" issues. First, the "simulator" has been liberated from real-time. It can run without being connected to current sensory motor experience. Second, "simulations" appear to be represented at different levels of detail. Our paper provides a framework for beginning to address the questions: how does language and its grammar control these aspects of simulation, what are the neurophysiological bases, and how can this be demonstrated in an artificial yet embodied cognitive system.
本文从“认知神经系统”的角度探讨了体验式句子处理中的问题，该方法结合了对相关行为的分析、对这种行为的已知神经生理学基础的分析，以及对体验式句子处理的神经计算模型的综合，该模型可应用于人机协作交互的背景下并在其中进行测试。我们提出了一种混合理解模型，该模型将句子和语篇的紧凑命题表示与它们在情境模拟中的时间展开联系起来，受语法的控制。该模型的起点是一个语法构建处理模型，该模型指定了语言是从句子到意义的映射的结构化库存的神经机制。然后，将该模型“体现”在感知运动系统（机器人）中，该系统允许其访问句子感知表示对，并与世界交互，为语言习得提供基础。然后，我们引入了“模拟”功能，使得机器人对其与世界的交互具有内部表示。该模拟器的控制和相关表示提出了一些有趣的“神经技术”问题。首先，“模拟器”已经从实时中解放出来。它可以在不连接当前感觉运动体验的情况下运行。其次，“模拟”似乎以不同的细节级别表示。我们的论文提供了一个框架，开始解决以下问题：语言及其语法如何控制这些模拟方面，其神经生理学基础是什么，以及如何在人工但具有体验性的认知系统中证明这一点。
