Representational Similarity and Pattern Classification of Fifteen Emotional States Induced by Movie Clips and Text...

Theoretical models emphasize that categorical factors, dimensional factors, or their combination may define the semantic space organization of emotion representations. While recent behavioral work has applied innovative multivariate methods for testing these theories, neuroscientific assessments remain limited due to a focus on a small number of emotions, single theoretical perspectives, univariate methods, or small sample sizes. We overcame these limitations in a comprehensive functional magnetic resonance imaging (fMRI) study in which participants (N = 136) viewed 150 movie clips and 150 text scenarios that reliably induced 15 different emotional states spanning positive, negative, and neutral valence. For the movie inductions, representational similarity analysis yielded a correspondence between the categorical behavioral responses and the brain activity patterns, and partial least squares discriminant analysis achieved strong decoding performance for all 15 emotions from whole-brain fMRI data, with importance maps encompassing cortical, limbic, and subcortical regions. Classification error analyses and a Bayesian model comparison supported the categorical nature of the emotion representations relative to a 2-dimensional arousal-valence model. Hierarchical clustering of the representational dissimilarity matrices revealed that the 15 emotions were organized into similarly meaningful clusters at both the subjective and neural levels. Results from the scenario inductions, while demonstrating similar behavioral effects and behavioral hierarchical structures, were more difficult to decode from the fMRI data. Overall, these findings provide novel insights into how emotions are organized and represented in the human brain and evidence a relationship between our subjective experience of and brain responses to emotional inductions.

We found a robust relationship between participants' categorical endorsement of and their neural responses to emotionally evocative movie clips using an unsupervised machine learning approach. Subsequently, we were also able to decode the emotional content of these movie clips using a supervised classification technique. Finally, we found that these emotions are organized in a very similar ways both in terms of participants' self-report and their brain responses to these stimuli. Together, these data-driven and computational modeling based findings from our study significantly advance our understanding of how emotions are organized and represented in our mind and brain.