Abstract
Visual cortex plays an important role in representing the affective significance of visual input. The origin of these affect-specific visual representations is debated: they are innate to the visual system versus they arise through reentry from frontal emotion processing structures such as the amygdala. We examined this problem by combining a convolutional neural network (CNN) model of the human ventral visual cortex pre-trained on ImageNet with two datasets of affective images. Our results show that (1) in all layers of the CNN model, there were artificial neurons that responded consistently and selectively to neutral, pleasant, or unpleasant images and (2) lesioning these neurons by setting their output to 0 or enhancing these neurons by increasing their gain lead to decreased or increased emotion recognition performance respectively. These results support the idea that the visual system may have the innate ability to represent the affective significance of visual input and suggest that CNNs offer a fruitful platform for testing neuroscientific theories.
Teaser A convolutional neural network (CNN) model of the human ventral visual cortex shows that artificial neurons respond selectively to emotional images, supporting the idea of an innate ability to represent affective significance of visual input.
Competing Interest Statement
This work was supported by the National Institutes of the National Institutes of Health/National Institute of Mental Health grants MH112558 and MH125615, the National Science Foundation grant 1908299, the University of Florida Artificial Intelligence Research Catalyst Fund, the University of Florida Informatics Institute Graduate Student Fellowship, the University of Florida McKnight Brain Institute, the University of Florida Center for Cognitive Aging and Memory, and the McKnight Brain Research Foundation.
Footnotes
we added one co-author
