Affiliation:
1. Stanford University
2. University of Texas at Austin
3. Dartmouth College
4. University of Oregon
5. University of Pittsburgh
Abstract
Abstract
Humans seamlessly transform dynamic social signals into inferences about the internal states of the people around them. To understand the neural processes that sustain this transformation, we collected fMRI data from participants (N = 100) while they rated the emotional intensity of people (targets) describing significant life events. Targets rated themselves on the same scale to indicate the intended “ground truth” emotional intensity of their videos. Next, we developed two multivariate models of observer brain activity– the first predicted the “ground truth” (r = 0.50, p < 0.0001) and the second predicted observer inferences (r = 0.53, p < 0.0001). When individuals make more accurate inferences, there is greater moment-by-moment concordance between these two models, suggesting that an observer's brain activity contains latent representations of other people’s emotional states. Using naturalistic socioemotional stimuli and machine learning, we developed reliable brain signatures that predict what an observer thinks about a target, what the target thinks about themselves, and the correspondence between them. These signatures can be applied in clinical data to better our understanding of socioemotional dysfunction.
Publisher
Research Square Platform LLC
Reference76 articles.
1. Abraham, A., Pedregosa, F., Eickenberg, M., Gervais, P., Mueller, A., Kossaifi, J., Gramfort, A., Thirion, B., & Varoquaux, G. (2014). Machine learning for neuroimaging with scikit-learn. Frontiers in Neuroinformatics, 8. https://doi.org/10.3389/fninf.2014.00014
2. Relationship of subjective and objective social status with psychological and physiological functioning: Preliminary data in healthy, White women;Adler NE;Health Psychology,2000
3. The role of the parahippocampal cortex in cognition;Aminoff EM;Trends in Cognitive Sciences,2013
4. Empathic Care and Distress: Predictive Brain Markers and Dissociable Brain Systems;Ashar YK;Neuron,2017
5. Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain;Avants B;Medical Image Analysis,2008