Spatial variability as a limiting factor in texture-discrimination tasks: implications for performance asymmetries

Abstract

Texture-discrimination tasks reveal a pronounced performance asymmetry depending on which texture represents the foreground region (small area) and which represents the ground (large area). This asymmetry implies that some global processes are involved in the segmentation process. We examined this problem within the context of the texture-segmentation algorithm, assuming two filtering stages. The first stage uses spatial frequency and orientation-selective (Gabor) filters, whereas the second stage is formed by low-resolution edge-detection filters. The presence and location of texture borders are indicated by significant responses in the second stage. Spurious texture borders may occur owing to textural local variabilities (such as orientation randomization), which are enhanced by the first stage. We suggest that these spurious borders act as background noise and thus limit performance in texture-discrimination tasks. The noise level depends on which texture occupies the ground in the display. We tested this model on numerous pairs of textures and found remarkably good correlation with human performance. A prediction of the model, namely, that discrimination asymmetry will be reduced when textural elements have identical orientation, was tested psychophysically and confirmed.