And now the machines recognize emotion categories embedded in the visual system

A neural network could recognize which #emotions, out of 20 different categories, a human would feel in response to an image, challenging the prevailing view that emotions are independent from the sensory environment. https://t.co/m67bzLgH6k @ScienceAdvances #ScienceMagArchives

— Science Magazine (@ScienceMagazine) August 1, 2020

Abstract: Theorists have suggested that emotions are canonical responses to situations ancestrally linked to survival. If so, then emotions may be afforded by features of the sensory environment. However, few computational models describe how combinations of stimulus features evoke different emotions. Here, we develop a convolutional neural network that accurately decodes images into 11 distinct emotion categories. We validate the model using more than 25,000 images and movies and show that image content is sufficient to predict the category and valence of human emotion ratings. In two functional magnetic resonance imaging studies, we demonstrate that patterns of human visual cortex activity encode emotion category–related model output and can decode multiple categories of emotional experience. These results suggest that rich, category-specific visual features can be reliably mapped to distinct emotions, and they are coded in distributed representations within the human visual system.