The reconstruction of images from neural data can provide a unique window into the content of human perceptual representations. Although recent efforts have established the viability of this enterprise using functional magnetic resonance imaging (MRI) 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 method akin to reverse correlation to derive visual features from functional MRI 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 approach allows us to estimate collections of features associated with different cortical areas as well as to successfully match image reconstructions to corresponding face exemplars. Furthermore, we establish the robustness and the utility of this approach by reconstructing 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.image reconstruction | face space | reverse correlation F ace recognition relies on visual representations sufficiently complex to distinguish even among highly similar individuals despite considerable variation due to expression, lighting, viewpoint, and so forth. A longstanding conceptual framework, termed "face space" (1-6), suggests that individual faces are represented in terms of their multidimensional deviation from an "average" face, but the precise nature of the dimensions or features that capture these deviations, and the degree to which they preserve visual detail, remain unclear. Thus, the featural basis of face space along with the neural system that instantiate it remain to be fully elucidated. The present investigation aims not only to uncover fundamental aspects of neural representations but also to establish their plausibility and utility through image reconstruction. Concretely, the current study addresses the issues above in the context of two distinct challenges, first, by determining the visual features used in face identification and, second, by validating these features through their use in facial image reconstruction.With respect to the first challenge, recent studies have demonstrated distinct sensitivity to local features (e.g., the size of the mouth) compared with configural features (e.g., the distance between the eyes and the mouth) in human face-selective cortex (7-10). Also, neurophysiological investigations (1, 11) of monkey cortex have found sensitivity to several facial features, particularly in the eye region of the face. However, most investigations consider only a few handpicked features. Thus, a comprehensive, unbiased assessment of face space stil...