Faces in the cloud: Fourier power spectrum biases ultrarapid face detection

Abstract

Recent results show that humans can respond with a saccadic eye movement toward faces much faster and with less error than toward other objects. What feature information does your visual cortex need to distinguish between different objects so rapidly? In a first step, we replicated the "fast saccadic bias" toward faces. We simultaneously presented one vehicle and one face image with different contrasts and asked our subjects to saccade as fast as possible to the image with higher contrast. This was considerably easier when the target was the face. In a second step, we scrambled both images to the same extent. For one subject group, we scrambled the orientations of wavelet components (local orientations) while preserving their location. This manipulation completely abolished the face bias for the fastest saccades. For a second group, we scrambled the phases (i.e., the location) of Fourier components while preserving their orientation (i.e., the 2-D amplitude spectrum). Even when no face was visible (100% scrambling), the fastest saccades were still strongly biased toward the scrambled face image! These results suggest that the ability to rapidly saccade to faces in natural scenes depends, at least in part, on low-level information contained in the Fourier 2-D amplitude spectrum.

Looking away from faces: Influence of high-level visual processes on saccade programming

“…This result is somewhat puzzling given the specificity of the faces to capture attention as described above. Moreover, a fast bias toward faces has been reported recently (Crouzet, Thorpe, & Kirchner, 2007;Honey et al, 2008). In a saccadic choice paradigm, Crouzet et al (2007) showed that early selective saccades could be directed toward faces but not toward other categories (animals and means of transport).…”

“…When they replaced the original photographs by scrambled images in which the whole facial configuration was disrupted, the pop-out effect disappeared, leading the authors to conclude that the pop-out effect reflects high-level, holistic parallel processing of faces. Yet, while replicating the original finding by Hershler and Hochstein, VanRullen (2006) reported that impairing the holistic processing by inverting faces had only a minor effect on search performance; he argued that the face popout effect may be explained by low-level differences between faces and other categories, such as the Fourier amplitude spectrum (see also Honey, Kirchner, & VanRullen, 2008). Thus there is still a controversy as to whether the face pop-out effect is driven by low-level visual factors or depends on high-level mechanisms.…”

“…At the behavioral level, in a recent study by Honey et al (2008), subjects were asked to saccade to the image containing the greater contrast in image pairs. The authors further manipulated the low-level visual content by scrambling either the local orientation or the position (i.e., the phase) of the spatial frequency components of the images.…”

“…We kept the global low-level features between object categories constant to see whether high-level properties of the stimuli could account for the face bias. We used the same phase scrambling procedure as described in Honey et al (2008) but matched each image to the mean amplitude spectrum of all images before scrambling and keeping the RMS contrast constant. Thus, faces, cars, and noise patterns differed only in phase content at the global level.…”

Reaction Time for Object Categorization Is Predicted by Representational Distance

“…Early in visual processing object manifolds are entangled, albeit to lesser extent than at the level of the retina, reflecting the weakness of the formatting of the information for making categorical decisions. Ostensibly some portions of a manifold are less entangled in EVC, possibly because of categorical biases in low-level stimulus features (Honey, Kirchner, & VanRullen, 2008). Exemplars producing activity localized to partially untangled portions of the manifold in EVC would have a subsequent advantage for categorization in IT.…”