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Predicting the brain activation pattern associated with the propositional content of a sentence: Modeling neural representations of events and states

机译：预测与句子的命题内容相关的大脑激活模式：对事件和状态的神经表示进行建模

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Even though much has recently been learned about the neural representation of individual concepts and categories, neuroimaging research is only beginning to reveal how more complex thoughts, such as event and state descriptions, are neurally represented. We present a predictive computational theory of the neural representations of individual events and states as they are described in 240 sentences. Regression models were trained to determine the mapping between 42 neurally plausible semantic features (NPSFs) and thematic roles of the concepts of a proposition and the fMRI activation patterns of various cortical regions that process different types of information. Given a semantic characterization of the content of a sentence that is new to the model, the model can reliably predict the resulting neural signature, or, given an observed neural signature of a new sentence, the model can predict its semantic content. The models were also reliably generalizable across participants. This computational model provides an account of the brain representation of a complex yet fundamental unit of thought, namely, the conceptual content of a proposition. In addition to characterizing a sentence representation at the level of the semantic and thematic features of its component concepts, factor analysis was used to develop a higher level characterization of a sentence, specifying the general type of event representation that the sentence evokes (e.g., a social interaction versus a change of physical state) and the voxel locations most strongly associated with each of the factors. . ©
机译：尽管最近已对单个概念和类别的神经表示学到了很多知识，但神经影像研究才刚刚开始揭示如何用神经表示更复杂的思想（例如事件和状态描述）。我们介绍了单个事件和状态的神经表示的预测计算理论，如240句中所述。训练了回归模型，以确定42种神经上合理的语义特征（NPSF）与命题概念的主题角色以及处理不同类型信息的各种皮质区域的fMRI激活模式之间的映射。给定模型新的句子内容的语义特征，模型可以可靠地预测所得的神经签名，或者，给定观察到的新句子的神经签名，模型可以预测其语义内容。这些模型还可以可靠地推广到所有参与者。该计算模型提供了一个复杂但基本的思想单元的大脑表示形式，即命题的概念内容。除了在其组成概念的语义和主题特征级别上表征句子表示之外，因子分析还用于开发句子的更高级别的表征，从而指定了该句子引发的事件表示的一般类型（例如，社会互动与身体状态的变化之间的关系）以及与每个因素最相关的体素位置。 。 ©

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