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Abstract: The reconstruction of images from neural data can provide a unique window into the content of human perceptual representations. While recent efforts have established the viability of this enterprise using functional magnetic resonance imaging (fMRI) patterns, these efforts have relied on a variety of prespecified image features. Here, we take on the twofold task of deriving features directly from empirical data and of using these features for facial image reconstruction. First, we use a version of reverse correlation to derive visual features from fMRI patterns elicited by a large set of homogeneous face exemplars. Then, we combine these features to reconstruct novel face images from the corresponding neural patterns. This novel approach allows us to estimate collections of facial features associated with different cortical areas as well as to achieve significant levels of reconstruction accuracy. Furthermore, we establish the robustness and the utility of this approach by reconstructing face images from patterns of behavioral data. From a theoretical perspective, the current results provide key insights into the nature of high-level visual representations; and, from a practical perspective, these findings make possible a broad range of image-reconstruction applications via a straightforward methodological approach.
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