Aversive Learning Changes Face-Viewing Strategies, as Revealed by Model-Based Fixation-Pattern Similarity Analysis

Abstract

Animals can effortlessly adapt their behavior by generalizing from past experiences, and avoid harm in novel aversive situations. In our current understanding, the perceptual similarity between learning and generalization samples is viewed as one major factor driving aversive generalization. Alternatively, the threat-prediction account proposes that perceptual similarity should lead to generalization to the extent it predicts harmful outcomes. We tested these views using a two-dimensional perceptual continuum of faces. During learning, one face is conditioned to predict a harmful event, whereas the most dissimilar face stays neutral; introducing an adversity gradient defined only along one dimension. Learning changed the way how humans sampled information during viewing of faces. These occurred specifically along the adversity gradient leading to an increased dissimilarity of eye-movement patterns along the threat-related dimension. This provides evidence for the threat-prediction account of generalization, which conceives perceptual factors to be relevant to the extent they predict harmful outcomes.