Configural and Featural Discriminations Use the Same Spatial Frequencies: A Model Observer versus Human Observer Analysis

Abstract

Previous work has shown mixed results regarding the role of different spatial frequency (SF) ranges in featural and configural processing of faces. Some studies suggest no special role of any given band for either type of processing, while others suggest that low SFs principally support configural analysis. Here we attempt to put this issue on a more rigorous footing by comparing human performance when making featural and configural discriminations with that of a model observer algorithm carrying out the same task. The model uses a simple algorithm that calculates the dot product of a stimulus image with each available potential match image to find the maximally likely match. It thus provides a principled way of analyzing available image information. We find human accuracy peaks at around 10 cycles per face (cpf) regardless of whether featural or configural manipulations are being detected. We also find accuracy peaks in the same part of the spectrum regardless of which feature is manipulated (ie eyes, nose, or mouth). Conversely, model observer performance, measured in terms of white noise tolerance, peaks at approximately 5 cpf, and this value again remains roughly constant regardless of the type of manipulation and feature manipulated. The ratio of the model's noise tolerance to a derived equivalent noise tolerance value for humans peaks at around 10 cpf, similar to the accuracy data. These results provide evidence that the human performance maxima at 10 cpf are not due simply to the physical characteristics of face stimuli, but rather arise due to an interaction between the available information in face images and human perceptual processing.