首页> 外文期刊>Philosophical Transactions of the Royal Society of London, Series B. Biological Sciences >Sentential negation of abstract and concrete conceptual categories: a brain decoding multivariate pattern analysis study
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Sentential negation of abstract and concrete conceptual categories: a brain decoding multivariate pattern analysis study

机译:抽象和具体概念类别的句子否定:脑解码多变量模式分析研究

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摘要

We rarely use abstract and concrete concepts in isolation but rather embedded within a linguistic context. To examine the modulatory impact of the linguistic context on conceptual processing, we isolated the case of sentential negation polarity, in which an interaction occurs between the syntactic operator not and conceptual information in the negation's scope. Previous studies suggested that sentential negation of concrete action-related concepts modulates activation in the fronto-parieto-temporal action representation network. In this functional magnetic resonance imaging study, we examined the influence of negation on a wider spectrum of meanings, by factorially manipulating sentence polarity (affirmative, negative) and fine-grained abstract (mental state, emotion, mathematics) and concrete (related to mouth, hand, leg actions) conceptual categories. We adopted a multivariate pattern analysis approach, and tested the accuracy of a machine learning classifier in discriminating brain activation patterns associated to the factorial manipulation. Searchlight analysis was used to localize the discriminating patterns. Overall, the neural processing of affirmative and negative sentences with either an abstract or concrete content could be accurately predicted by means of multivariate classification. We suggest that sentential negation polarity modulates brain activation in distributed representational semantic networks, through the functional mediation of syntactic and cognitive control systems.
机译:我们很少使用摘要和具体的概念,而是在语言背景下嵌入。为了检查语言语境对概念处理的调制影响,我们孤立的句子否定极性的情况,其中在判断运算符之间不会发生交互,并在否定范围内的概念信息。以前的研究表明,对具体动作相关概念的句子否定调制了额外颞动作表示网络的激活。在这种功能磁共振成像研究中,我们通过应对句子极性(肯定,负)和细粒度的摘要(精神状态,情感,数学)和混凝土(与嘴有关)来检查否定对更广泛的含义的影响,手,腿部动作)概念类别。我们采用多变量模式分析方法,并在鉴别与阶乘操作相关的脑激活模式中测试机器学习分类器的准确性。探照灯分析用于本地化辨别模式。总的来说,可以通过多变量分类准确地预测摘要或混凝土内容的肯定和负句的神经处理。我们建议,句子否定极性通过语法和认知控制系统的功能调解来调节分布式代表性语义网络中的大脑激活。

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