Predicting the brain activation pattern associated with the propositional content of a sentence: Modeling neural representations of events and states
Predicting the brain activation pattern associated with the propositional content of a sentence: Modeling neural representations of events and states
复制标题
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征进行建模
作者：
Jing Wang;V. Cherkassky;M. Just
尽管最近关于个体概念和类别的神经表征已经了解了很多，但神经成像研究才刚刚开始揭示更复杂的想法，如事件和状态描述，是如何被神经表征的。我们提出了单个事件和状态的神经表示的预测计算理论，因为它们被描述在240个句子中。对回归模型进行训练，以确定42个神经元似然语义特征(NPSF)与命题概念的主题角色之间的映射，以及处理不同类型信息的不同皮质区域的fMRI激活模式。给定对该模型来说是新的句子内容的语义表征，该模型可以可靠地预测所产生的神经特征，或者，给定一个新句子的观察到的神经特征，该模型可以预测其语义内容。这些模型也可以可靠地在所有参与者中推广。这个计算模型提供了一个复杂但基本的思维单位的大脑表征，即命题的概念性内容。除了在其成分概念的语义和主题特征的水平上表征句子表征外，还使用因素分析来开发句子的更高水平表征，指定句子所引起的事件表征的一般类型(例如，社会交互与物理状态的改变)以及与每个因素最相关的体素位置。Hum Brain Mapp 38：4865-4881,2017。©2017威利期刊公司。
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. Hum Brain Mapp 38:4865–4881, 2017. © 2017 Wiley Periodicals, Inc.
影响因子：
3.7
作者：
Fernandino, Leonardo;Binder, Jeffrey R.;Seidenberg, Mark S.
通讯作者：
Seidenberg, Mark S.
影响因子：
3.7
作者：
Coutanche, Marc N.;Thompson-Schill, Sharon L.
通讯作者：
Thompson-Schill, Sharon L.