Department of Neuroscience and Biomedical Engineering, Aalto University, P.O. Box 12200, FI-00076, Aalto, Finland.
Aalto NeuroImaging, Aalto University, P.O. Box 12200, Aalto, FI-00076, Finland.
Nat Commun. 2019 Feb 25;10(1):927. doi: 10.1038/s41467-019-08848-0.
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.
现代语义理论假设，单词的意义可以分解为语义特征的独特组合（例如，“狗”包括“barks”）。在这里，我们使用功能磁共振成像（fMRI）证明，大脑将信息碎片组合成有意义的对象表示。参与者以口头描述的形式接收单个对象的线索，这些线索由三个孤立的语义特征组成。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。使用不仅实际上作为线索呈现的三个语义特征的组合，而是使用与目标对象通常相关的语义特征的更丰富集合，可以最佳地解码记录的大脑模式。我们得出结论，我们的实验方案使我们能够证明，碎片化的信息被组合成对象的完整语义表示，并确定与对象含义相关的大脑区域。
