Flexibility of emerging face categorization at different levels of abstraction

Categorization of visual stimuli at different levels of abstraction relies on the encoding of relevant diagnostic features present at different spatial scales. We used the Eidolon Factory, an image-manipulation algorithm that introduces random disarray fields across spatial scales, to study how such a process flexibly combines perceptual information to perform successful categorization depending on task demands. Images of animal faces, human faces, and everyday objects were disarrayed coherently (random fields correlated) or incoherently (random fields randomized) to create a family of 50 eidolons per stimulus image with increasing disarray. Participants (N = 243) viewed each family of eidolons in a smooth sequence from maximum disarray to no disarray and performed a category verification task either at the superordinate (any face type) or basic (human face only) levels at two levels of uncertainty: participants in one group used their gut feeling to respond, whereas another group had to be sure of their decision. When participants used their gut feeling to respond, we observed a superordinate-level advantage. When they were sure of their response, we observed a basic-level advantage. Coherently disarrayed sequences impaired target detection compared to incoherently disarrayed sequences for both levels of response certainty. Furthermore, participants' sensitivity in the Any Face condition increased when they observed coherently disarrayed sequences and had to be sure of their response. These results suggest that the visual system does not strictly adhere to feedforward processing but flexibly adjusts to the relevant perceptual information depending on task context.