Most architectures and models of language processing have been built upon a restricted view of language, which is limited to sentence processing. These approaches fail to capture one primordial characteristic: efficiency. Many facilitation effects are known to be at play in natural situations such as conversation (shallow processing, no real access to the lexicon, etc.) without any impact on the comprehension. In this study, on the basis of a new model integrating into a unique architecture, we present these facilitation effects for accessing the meaning into the classical compositional architecture. This model relies on two mechanisms, prediction and unification, and provides a unique architecture for the description of language processing in its natural environment.

中文翻译：

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基于预测和统一的理解神经认知模型

大多数语言处理的架构和模型都是建立在有限的语言视图之上的，仅限于句子处理。这些方法未能捕捉到一个基本特征：效率。众所周知，许多促进效应在自然情况下发挥作用，例如对话（浅层处理，无法真正访问词典等），而不会对理解产生任何影响。在这项研究中，基于融入独特建筑的新模型，我们提出了这些促进效应，以获取经典组合建筑的意义。该模型依赖于预测和统一两种机制，并为自然环境中的语言处理描述提供了独特的架构。