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Predicting brain activation patterns associated with individual lexical concepts based on five sensory-motor attributes

机译:基于五种感觉运动属性预测与单个词汇概念相关的大脑激活模式

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摘要

While major advances have been made in uncovering the neural processes underlying perceptual representations, our grasp of how the brain gives rise to conceptual knowledge remains relatively poor. Recent work has provided strong evidence that concepts rely, at least in part, on the same sensory and motor neural systems through which they were acquired, but it is still unclear whether the neural code for concept representation uses information about sensory-motor features to discriminate between concepts. In the present study, we investigate this question by asking whether an encoding model based on five semantic attributes directly related to sensory-motor experience – sound, color, visual motion, shape, and manipulation – can successfully predict patterns of brain activation elicited by individual lexical concepts. We collected ratings on the relevance of these five attributes to the meaning of 820 words, and used these ratings as predictors in a multiple regression model of the fMRI signal associated with the words in a separate group of participants. The five resulting activation maps were then combined by linear summation to predict the distributed activation pattern elicited by a novel set of 80 test words. The encoding model predicted the activation patterns elicited by the test words significantly better than chance. As expected, prediction was successful for concrete but not for abstract concepts. Comparisons between encoding models based on different combinations of attributes indicate that all five attributes contribute to the representation of concrete concepts. Consistent with embodied theories of semantics, these results show, for the first time, that the distributed activation pattern associated with a concept combines information about different sensory-motor attributes according to their respective relevance. Future research should investigate how additional features of phenomenal experience contribute to the neural representation of conceptual knowledge.
机译:尽管在揭示感知表示基础的神经过程方面取得了重大进展,但是我们对大脑如何产生概念知识的理解仍然相对较差。最近的工作提供了有力的证据,证明概念至少部分依赖于获得它们的相同的感觉和运动神经系统,但是仍然不清楚用于概念表示的神经代码是否使用有关感觉运动特征的信息来进行区分概念之间。在本研究中,我们通过询问基于与感觉运动体验直接相关的五个语义属性(声音,颜色,视觉运动,形状和操纵)的编码模型是否可以成功预测个人引起的大脑激活模式,从而对此问题进行了研究。词汇概念。我们收集了关于这五个属性与820个单词的含义相关性的评分,并将这些评分用作与一组单独参与者中的单词相关的fMRI信号的多元回归模型中的预测变量。然后,通过线性求和将五个得到的激活图组合起来,以预测由一组80个测试词组成的新颖集合所激发的分布式激活模式。编码模型预测测试单词引发的激活模式要好于偶然。不出所料,对具体的预测是成功的,但对抽象的概念却没有。基于属性的不同组合的编码模型之间的比较表明,所有五个属性都有助于具体概念的表示。与语义的具体化理论相一致,这些结果首次表明,与一个概念相关的分布式激活模式根据其各自的相关性组合了有关不同感觉运动属性的信息。未来的研究应该调查现象体验的其他特征如何促进概念知识的神经表示。

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