A Neurosemantic Theory of Concrete Noun Representation Based on the Underlying Brain Codes
A Neurosemantic Theory of Concrete Noun Representation Based on the Underlying Brain Codes
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DOI：
10.1371/journal.pone.0008622
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发表时间：
2010-01-13
期刊：
影响因子：
3.7
通讯作者：
Mitchell, Tom M.
中科院分区：
文献类型：
--
作者：
Just, Marcel Adam;Cherkassky, Vladimir L.;Mitchell, Tom M.
本文描述了一组生物驱动的语义维度的神经表征的具体名词的基础上发现，然后演示了如何产生的名词表征理论可以用来识别简单的想法，通过他们的功能磁共振成像模式。我们使用因子分析的功能磁共振成像脑成像数据，以揭示生物表征的个别具体名词，如苹果，在没有任何图片刺激。从这个分析中，我们发现了三个主要的语义因素，它们支撑着命名物理对象的名词的神经表征，我们将其标记为操纵、庇护和进食。每个因素在神经上表示在3-4个不同的大脑位置，对应于在非语言任务中共同激活的皮层网络，例如工具使用哑剧的操作因素。几个收敛的方法，如使用的行为评级的词义和文本语料库的特点，提供独立的证据，这些因素的表征的中心。然后，这些因素与机器学习分类器技术一起使用，以表明fMRI测量的单个具体名词（如苹果）的大脑表征可以从60个候选词中以良好的准确性识别出来，仅使用与这些因素相关的16个位置的fMRI活动。为了进一步证明所提出的帐户的生成性，开发了一个基于理论的模型来预测大脑激活模式的算法还没有被暴露的话。这些方法、发现和理论构成了一种利用大脑活动来理解物体概念在头脑中是如何表现的新方法。
This article describes the discovery of a set of biologically-driven semantic dimensions underlying the neural representation of concrete nouns, and then demonstrates how a resulting theory of noun representation can be used to identify simple thoughts through their fMRI patterns. We use factor analysis of fMRI brain imaging data to reveal the biological representation of individual concrete nouns like apple, in the absence of any pictorial stimuli. From this analysis emerge three main semantic factors underpinning the neural representation of nouns naming physical objects, which we label manipulation, shelter, and eating. Each factor is neurally represented in 3-4 different brain locations that correspond to a cortical network that co-activates in non-linguistic tasks, such as tool use pantomime for the manipulation factor. Several converging methods, such as the use of behavioral ratings of word meaning and text corpus characteristics, provide independent evidence of the centrality of these factors to the representations. The factors are then used with machine learning classifier techniques to show that the fMRI-measured brain representation of an individual concrete noun like apple can be identified with good accuracy from among 60 candidate words, using only the fMRI activity in the 16 locations associated with these factors. To further demonstrate the generativity of the proposed account, a theory-based model is developed to predict the brain activation patterns for words to which the algorithm has not been previously exposed. The methods, findings, and theory constitute a new approach of using brain activity for understanding how object concepts are represented in the mind.