bioRxiv preprint doi: https://doi.org/10.1101/470237; this version posted November 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. 1 Emotion Schemas are Embedded in the Human Visual System 2 Philip A. Kragel1,2*, Marianne Reddan1, Kevin S. LaBar3, and Tor D. Wager1* 3 1. Department of Psychology and Neuroscience and the Institute of Cognitive Science, University of 4 Colorado, Boulder, CO, USA 2. Institute for Behavioral Genetics, the University of Colorado, Boulder, CO, USA 3. Department of Psychology and Neuroscience and the Center for Cognitive Neuroscience, Duke University, Durham, NC, USA 5 6 *corresponding authors:
[email protected],
[email protected] bioRxiv preprint doi: https://doi.org/10.1101/470237; this version posted November 14, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Kragel, Reddan, LaBar, and Wager Visual Features of Emotions 7 Abstract 8 Theorists have suggested that emotions are canonical responses to situations ancestrally linked to survival. If so, 9 then emotions may be afforded by features of the sensory environment. However, few computationally explicit 10 models describe how combinations of stimulus features evoke different emotions. Here we develop a 11 convolutional neural network that accurately decodes images into 11 distinct emotion categories.