Development of a tailor-made information providing method based on the elucidation of information-processing mechanisms in the brain with consideration of gender and individual differences


Project Summary

Principal Investigator

Kobayashi Ichiro (Faculty of Core Research Natural Science Division) 

NISHIDA Satoshi (National Institute of Information and Communications Technology ,Center for Information and Neural Networks)
Ying Luo (Graduate School of Humanities and Sciences, Advanced Sciences)
MOGI Hina (Science, Information Sciences) 

Research period FY 2022 -
SDGs Goals 5(Gender Equality)
3(Good Health and Well-being)

Outline of Research

The presence of gender and individual differences in emotions and other emotional characteristics has been well known. Among various natural stimuli given to humans, scholars have particularly pointed out that music provides tuning to interconnections among multiple areas in the brain and facilitates focus on consciousness while controlling emotions. In addition, music is used as a therapy for neurological disorders with the implicit understanding that music manages emotion. However, studies that elucidated information-processing mechanisms in the human brain in response to musical stimuli have been lacking.
Therefore, the study aims to elucidate information-processing mechanisms in the human brain concerning gender and individual differences using musical stimuli. In addition, the research aims to promote the possibility of providing tailor-made information based on the gender and personality of subjects to increase opportunities for neuromarketing, such as developing products favored by women and creating advertisements that leave a long-lasting impression.

Research Reports

In this study, we analyzed the gender  differences in brain information processing for brain activity under musical stimuli. We extracted the musical features using a convolutional deep learning model to construct an encoding model for predicting the brain activity state from the musical features in each of the deep layer. Subsequently, we used this model to analyze the hierarchical brain activity for each gender in response to musical stimuli. Applying the PageRank algorithm to particular functional brain regions of interest, we investigated the hierarchical transition of brain region hubs, and examined whether differences between men and women existed in terms of brain information processing under musical stimuli. The superior frontal gyrus ranked in the top two positions in males and females, but males ranked first in the right brain and females ranked first in the left brain. Conversely, when applying the PageRank algorithm directly to the measured brain activity without using deep learning, the left brain superior frontal gyrus was ranked first for both males and females. Therefore,  we did not observe any gender differences.

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