Research in face recognition has tended to focus on discriminating between individuals, or “telling people apart.” It has recently become clear that it is also necessary to understand how images of the same person can vary, or “telling people together.” Learning a new face, and tracking its representation as it changes from unfamiliar to familiar, involves an abstraction of the variability in different images of that person's face. Here, we present an application of principal components analysis computed across different photos (...) of the same person. We demonstrate that people vary in systematic ways, and that this variability is idiosyncratic—the dimensions of variability in one face do not generalize well to another. Learning a new face therefore entails learning how that face varies. We present evidence for this proposal and suggest that it provides an explanation for various effects in face recognition. We conclude by making a number of testable predictions derived from this framework. (shrink)

This paper concerns the question of which properties figure in the contents of perceptual experience. According to conservatives, only low-level properties figure in the contents of perceptual experience. Liberals, on the other hand, claim that high-level properties, such as natural kind properties, artifacts, and even moral properties, can figure in the contents of perceptual experience. I defend a novel argument in favor of liberalism, the Epistemic Argument, which hinges on two crucial claims. The first is that many perceptual experiences of (...) even neurotypical human beings can justify beliefs in high-level properties without providing justification for their low-level constituents. The second claim, roughly, is that any experience that alone provides (defeasible) justification for beliefs about some property p, other things being equal, has p as part of its content. In short, certain perceptual experiences represent high-level but not low-level properties, which entails that liberalism is true. (shrink)