Perceptual learning of parametric face categories leads to the integration of high-level class-based information but...

To date, the relative contribution of the different levels of the visual hierarchy during perceptual decisions remains unclear. Typical models of visual processing, with the reverse hierarchy theory (RHT) as a prominent example, strongly emphasize the role of higher levels and interpret lower levels as sequence of simple feature detectors. Here, we investigate this issue based on two analyses. Using a novel combination of perceptual learning based on two classes of parametric faces and a subsequent odd-one-out paradigm, we first test a vital prediction of RHT: high-level pop-out. With this experimental approach, we overcome the low-level confounds of previous studies while still introducing distinct high-level representations. Contrary to previous findings, our analyses show that there is no high-level pop-out, despite very early, near-perfect classification accuracy and extensive training of our subjects. Second, we explore the underlying form of category representation during subsequent stages of perceptual training. This is accomplished by including class-external and class-internal target-distractor combinations. Whereas the subjects' responses during the first sessions are best explained instance-based and dependent on low-level metric differences, later patterns exhibit the inclusion of high-level, class-based information that is independent of target-stimulus similarity. Finally, we show that the utilized level of information is highly task-dependent.