High-level visual areas act like domain-general filters with strong selectivity and functional specialization

Investigation of the visual system has mainly relied on a-priori hypotheses to restrict experimental stimuli or models used to analyze experimental data. Hypotheses are an essential part of scientific inquiry, but an exclusively hypothesis-driven approach might lead to confirmation bias towards existing theories and away from novel discoveries not predicted by them. This paper uses a hypothesis-neutral computational approach to study four high-level visual regions of interest (ROIs) selective to faces, places, letters, or body parts. We leverage the unprecedented scale and quality of the Natural Scenes Dataset to constrain neural network models of these ROIs with functional Magnetic Resonance Imaging (fMRI) measurements. We show that using only the stimulus images and the associated activity in an ROI, we are able to train from scratch a neural network that can predict the activity in each voxel of that ROI with an accuracy that beats state-of-the-art models. Moreover, once trained, the ROI-specific networks can reveal what kinds of functional properties emerge spontaneously in their training. Strikingly, despite no category-level supervision, the units in the trained networks act strongly as detectors for semantic concepts like ‘faces’ or ‘words’, thereby providing sub-stantial pieces of evidence for categorical selectivity in these visual areas. Importantly, this selectivity is maintained when training the networks with selective deprivations in the training diet, by excluding images that contain their preferred category. The resulting selectivity in the trained networks strongly suggests that the visual areas do not function as exclusive category detectors but are also sensitive to visual patterns that are typical to their preferred categories, even in the absence of these categories. Finally, we show that our response-optimized networks have distinct functional properties. Together, our findings suggest that response-optimized models combined with model interpretability techniques can serve as a powerful and unifying computational framework for probing the nature of representations and computations in the brain.