Computational models of category-selective brain regions enable high-throughput tests of selectivity

More Than the Face: Representations of Bodies in the Inferior Temporal Cortex

Visual representations of bodies, in addition to those of faces, contribute to the recognition of con- and heterospecifics, to action recognition, and to nonverbal communication. Despite its importance, the neural basis of the visual analysis of bodies has been less studied than that of faces. In this article, I review what is known about the neural processing of bodies, focusing on the macaque temporal visual cortex. Early single-unit recording work suggested that the temporal visual cortex contains representations of body parts and bodies, with the dorsal bank of the superior temporal sulcus representing bodily actions. Subsequent functional magnetic resonance imaging studies in both humans and monkeys showed several temporal cortical regions that are strongly activated by bodies. Single-unit recordings in the macaque body patches suggest that these represent mainly body shape features. More anterior patches show a greater viewpoint-tolerant selectivity for body features, which may reflect a processing principle shared with other object categories, including faces. Expected final online publication date for the Annual Review of Vision Science, Volume 8 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Face neurons encode nonsemantic features

The primate inferior temporal cortex contains neurons that respond more strongly to faces than to other objects. Termed “face neurons,” these neurons are thought to be selective for faces as a semantic category. However, face neurons also partly respond to clocks, fruits, and single eyes, raising the question of whether face neurons are better described as selective for visual features related to faces but dissociable from them. We used a recently described algorithm, XDream, to evolve stimuli that strongly activated face neurons. XDream leverages a generative neural network that is not limited to realistic objects. Human participants assessed images evolved for face neurons and for nonface neurons and natural images depicting faces, cars, fruits, etc. Evolved images were consistently judged to be distinct from real faces. Images evolved for face neurons were rated as slightly more similar to faces than images evolved for nonface neurons. There was a correlation among natural images between face neuron activity and subjective “faceness” ratings, but this relationship did not hold for face neuron–evolved images, which triggered high activity but were rated low in faceness. Our results suggest that so-called face neurons are better described as tuned to visual features rather than semantic categories.

NeuroGen: Activation optimized image synthesis for discovery neuroscience

Functional MRI (fMRI) is a powerful technique that has allowed us to characterize visual cortex responses to stimuli, yet such experiments are by nature constructed based on a priori hypotheses, limited to the set of images presented to the individual while they are in the scanner, are subject to noise in the observed brain responses, and may vary widely across individuals. In this work, we propose a novel computational strategy, which we call NeuroGen, to overcome these limitations and develop a powerful tool for human vision neuroscience discovery. NeuroGen combines an fMRI-trained neural encoding model of human vision with a deep generative network to synthesize images predicted to achieve a target pattern of macro-scale brain activation. We demonstrate that the reduction of noise that the encoding model provides, coupled with the generative network's ability to produce images of high fidelity, results in a robust discovery architecture for visual neuroscience. By using only a small number of synthetic images created by NeuroGen, we demonstrate that we can detect and amplify differences in regional and individual human brain response patterns to visual stimuli. We then verify that these discoveries are reflected in the several thousand observed image responses measured with fMRI. We further demonstrate that NeuroGen can create synthetic images predicted to achieve regional response patterns not achievable by the best-matching natural images. The NeuroGen framework extends the utility of brain encoding models and opens up a new avenue for exploring, and possibly precisely controlling, the human visual system.