In recent years, many studies using deep learning have been conducted to elucidate the mechanism of information representation in the brain under stimuli evoked by various modalities. On the other hand, it has not yet been clarified how we humans link information of different modalities in the brain. In this study, to elucidate the relationship between visual and language information in the brain, we constructed encoding models that predict brain activity based on features extracted from the hidden layers of VGG16 for visual information and BERT for language information. We investigated the hierarchical characteristics of cortical localization and representational content of visual and semantic information in the cortex based on the brain activity predicted by the encoding model. The results showed that the cortical localization modeled by VGG16 is getting close to that of BERT as VGG16 moves to higher layers, while the representational contents differ significantly between the two modalities.
Generating Natural Language Descriptions for Semantic Representations of Human Brain Activity
Eri Matsuo | Ichiro Kobayashi | Shinji Nishimoto | Satoshi Nishida | Hideki Asoh
Proceedings of the ACL 2016 Student Research Workshop