Visual Emotion Perception in a Deep Neural Network Model with Both Bottom-Up and Top-Down Connections

Emotion reshapes perception by modulating sensory processing through top-down feedback--a process referred to as emotional perception. The computational mechanisms by which distinct affective signals influence visual representations however remain poorly understood. Here, we use a deep neural network to simulate this process and test mechanistic hypotheses about how top-down feedback guides emotional peception. Most existing models treat the perception of emotional content as a static, feedforward task, overlooking the dynamic interplay between internal states, external goals, and sensory input that characterizes affective perception in the brain. We introduce EmoFB, a biologically inspired model that integrates an affective system with a visual processing hierarchy through two functionally distinct feedback signals: intrinsic feedback, arising from the models own affective appraisal of perceptual input, and external steering, conveying contextual priors such as task expectations or target categories. We evaluated EmoFB on three tasks varying in perceptual ambiguity (Single Image, Side-by-Side, and Overlay). External steering exerted the strongest influence, not only improving recognition under challenging conditions but also restructuring internal representations by sharpening category-specific clustering in feature space. Crucially, top-down feedback increased brain-model representational similarity, strengthening alignment with human fMRI responses across early visual cortex, ventral visual areas, and the amygdala. EmoFB provides a computational framework for testing neurocognitive theories of emotion appraisal and top-down feedback modulation. It bridges affective neuroscience and artificial intelligence, offering mechanistic insight into how emotional signals shape perception in both brains and machines.