Rapid segmentation of one-dimensional noise textures across borders

We investigated the segmentation of texture pairs that were samples of one-dimensional binary visual noise. The stimulus consisted of an array of 5 x 8 squares separated by two-dimensional noise borders of varying width. The squares were filled with vertical black or white stripes of random width. The task was to detect the presence of a target square which differed from the squares above and below in one of three possible ways: the target pattern was either a contrast inverted copy or a horizontal translation of the pattern in the vertically adjacent squares, or else an independent realization of the noise. The binary noise in the textures was sequentially high-pass filtered to preclude the use of coarse-scale receptive fields and minimize the presence of sparse, extended "features". The target could be detected reliably within 100 msec even when the border width was larger than the maximal stripe width. The border width at threshold saturated for longer presentation times. Our results show that the microstructure of the patterns, i.e. information on the scale of the linewidth in the patterns, is not used directly, even though it contains most of the signal energy and is objectively the most reliable cue to the segmentation.