Perceptual learning of parametric face categories leads to the integration of high-level class-based information but not to high-level pop-out

Abstract

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.

Fixation-pattern similarity analysis reveals adaptive changes in face-viewing strategies following aversive learning

“…In an alternative view, aversive learning consists of recovering the vector of features that defines the adversity gradient (Figure 6B, black arrow ), leading to an increased saliency for discriminative features that separate best the harm- and safety-predicting prototypes. This feature vector could overlap with categorical knowledge that is either naturally present (such as gender, ethnicity or emotional expression), or learned with experience (Dunsmoor and Murphy, 2015; Kietzmann and König, 2010; Qu et al, 2016). While both scenarios lead to an increased separation between the CS+ and CS– poles, as observed in the present study, they have divergent predictions when tested with stimuli organized in three concentric circles (Figure 6B).…”

Towards a new understanding of fear generalization and its neural origin

“…gender and age). Across interleaved conditioning and test phases, participants can be given the possibility to extract the underlying category structure 53 . Importantly, it is crucial to pit the category membership of faces against perceptual similarities to investigate their independent contributions over the course of the learning.…”