Emotion Analysis in the Human Brain Evoked by Language Stimulation

Advances in neural recording and deep learning have enabled a more precise analysis of how the brain processes language. However, neural representations of diverse emotions during natural narrative language comprehension remains unclear. In this study, we investigated how emotional content in naturalistic spoken language is reflected in brain activity using two independent narrative functional magnetic resonance imaging datasets: Alice and Le Petit Prince. Participants listened to narrative readings and their blood-oxygenation-level-dependent responses were recorded. An emotion recognition model was developed by fine-tuning BERT using the GoEmotions dataset together with sentence-level annotations for 80 emotions. Emotional and linguistic features were extracted and used to train voxel-wise encoding models to predict brain activity. To examine emotion-related neural representations, we subtracted predictions based on linguistic features from those based on combined emotional and linguistic features. The resulting emotion-related activity patterns were distributed across cortical regions associated with affective and semantic processing and were consistent across subjects and datasets. These findings indicate that the emotional meaning conveyed through natural language is represented in a graded and distributed manner in the human brain, beyond linguistic content alone.