Yujun Chen¹ (), Andreas Keil¹, Mingzhou Ding¹; ¹UNIVERSITY OF FLORIDA

Recent fMRI studies in human subjects have found affect-specific neural representations of emotional scenes in early visual cortex. The origin of these representations is debated. One group of hypotheses suggests that these representations result from reentrant feedback from anterior emotion-modulating structures (e.g., the amygdala), whereas another group of hypotheses states that sensory cortex, including retinotopic visual cortex, may itself code for the emotional qualities of visual stimuli, without the necessity for feedback processing. We examined this problem by employing a neural encoding model that can generate synthetic fMRI responses to natural images in early visual cortex. The model works by linearly mapping features extracted by convolutional neural networks onto voxel-wise BOLD responses in different visual areas and is trained on the Natural Scenes Dataset. Dividing the images in the International Affective Picture System into three broad categories: pleasant, neutral and unpleasant, we found that in early visual cortex, the neural patterns evoked by the emotional images cannot be decoded from that evoked by the neutral images, in contrast with the findings from recent fMRI studies in human subjects. Because the model-generated responses are free from emotion-modulated reentrant feedback, this finding can be seen as lending support to the reentry hypothesis. Interestingly, when face stimuli from the AffectNet were shown to the model, the neural patterns evoked by emotional faces in early visual cortex can be significantly decoded from that evoked by neutral faces, suggesting that the early visual cortex may contribute differently to the emotional processing of faces versus scenes.