Analysis of Information Processing Mechanisms in the Human Brain on Emotion during Tanka Reading

Abstract

Recent advances in non-invasive brain function measurement technologies, such as functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG), and the development of machine learning techniques, including deep learning, have led to increased research on the elucidation and quantitative understanding of information processing processes in the human brain. Since the emergence of word2vec, which represents the meaning of natural language words as vectors, features of language stimuli given to the human brain have been represented using general-purpose language models in natural language processing and used to estimate brain states. In this study, we used GPT-2, which is known to perform well as a feature for representing brain states, to investigate the information processing processes in the human brain when reading Japanese short poems. In particular, we report the results of an investigation into the hierarchical changes in information processing when humans perceive whether a poem is "poetic" or "unpoetic" by estimating brain activity from each layer of deep learning models.