Reconstructing meaning from bits of information
Reconstructing meaning from bits of information
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DOI：
10.1038/s41467-019-08848-0
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发表时间：
2019-02-25
影响因子：
16.6
通讯作者：
Salmelin, Riitta
中科院分区：
文献类型：
--
作者：
Kivisaari, Sasa L.;van Vliet, Marijn;Salmelin, Riitta
现代语义学理论认为，词语的意义可以分解为语义特征的独特组合（例如，“狗”将包括“吠叫”）。在这里，我们使用功能性磁共振成像（fMRI）证明大脑将信息位组合成有意义的对象表示。受试者以三个孤立的语义特征的形式获得个体对象的线索，这些语义特征以言语描述的形式给出。我们使用基于机器学习的神经解码来学习个体语义特征和BOLD激活模式之间的映射。记录下来的大脑模式最好是结合使用三个语义特征来解码，这些语义特征实际上是作为线索呈现的，但通常与目标对象相关的语义特征要丰富得多。我们的结论是，我们的实验协议使我们能够证明，碎片化的信息被组合成一个完整的语义表示的对象，并确定与对象的意义相关的大脑区域。
Modern theories of semantics posit that the meaning of words can be decomposed into a unique combination of semantic features (e.g., "dog" would include "barks"). Here, we demonstrate using functional MRI (fMRI) that the brain combines bits of information into meaningful object representations. Participants receive clues of individual objects in form of three isolated semantic features, given as verbal descriptions. We use machine-learning-based neural decoding to learn a mapping between individual semantic features and BOLD activation patterns. The recorded brain patterns are best decoded using a combination of not only the three semantic features that were in fact presented as clues, but a far richer set of semantic features typically linked to the target object. We conclude that our experimental protocol allowed us to demonstrate that fragmented information is combined into a complete semantic representation of an object and to identify brain regions associated with object meaning.