The channel for detecting contrast modulation also responds to density modulation (or vice versa)

Monocular and binocular mechanisms detect modulations of dot density and dot contrast

Strong reciprocity has been demonstrated between (1) spatial modulations of dot density and modulations of dot luminance, and (2) modulations of dot density and modulations of dot contrast, in textures. The latter are much easier to detect when presented in phase with one another than when presented 180° out of phase, although out-of-phase modulations can also be detected given sufficient amplitude. This result supports the existence of two detection mechanisms: one that is excited by both density modulations and contrast modulations (quiescent when those modulations are presented 180° out of phase) and another that is relatively insensitive to either density modulations or contrast modulations (thus remaining stimulated regardless of phase angle). We investigate whether the mechanism responsible for detecting out-of-phase modulations depends on high-level computations (downstream from the confluence of monocular signals) or whether both mechanisms are situated at the monocular level of visual processing. Specifically, density-modulated and/or contrast-modulated stimuli were presented monocularly (i.e., to the same eye) or dichoptically (i.e., to opposite eyes). Out-of-phase modulations of density were much easier to detect when presented dichoptically. A dichoptic advantage was also found for out-of-phase density and contrast modulations. These dichoptic advantages imply conscious access to a mechanism at the monocular level of processing. When density modulations were presented dichoptically, 180° out of phase, detection thresholds were highest. Consequently, a mechanism with binocular input must also contribute to the detection of these modulations. We describe a minimal, image-based model for these results that contains one monocular computation and one binocular computation.

Second-order boundaries segment more easily when they are density-defined rather than feature-defined

Previous studies have demonstrated that density is an important perceptual aspect of textural appearance to which the visual system is highly attuned. Furthermore, it is known that density cues not only influence texture segmentation, but can enable segmentation by themselves, in the absence of other cues. A popular computational model of texture segmentation known as the "Filter-Rectify-Filter" (FRF) model predicts that density should be a second-order cue enabling segmentation. For a compound texture boundary defined by superimposing two single-micropattern density boundaries, a version of the FRF model in which different micropattern-specific channels are analyzed separately by different second-stage filters makes the prediction that segmentation thresholds should be identical in two cases: (1) Compound boundaries with an equal number of micropatterns on each side but different relative proportions of each variety (compound feature boundaries) and (2) Compound boundaries with different numbers of micropatterns on each side, but with each side having an identical number of each variety (compound density boundaries). We directly tested this prediction by comparing segmentation thresholds for second-order compound feature and density boundaries, comprised of two superimposed single-micropattern density boundaries comprised of complementary micropattern pairs differing either in orientation or contrast polarity. In both cases, we observed lower segmentation thresholds for compound density boundaries than compound feature boundaries, with identical results when the compound density boundaries were equated for RMS contrast. In a second experiment, we considered how two varieties of micropatterns summate for compound boundary segmentation. In the case where two single micro-pattern density boundaries are superimposed to form a compound density boundary, we find that the two channels combine via probability summation. By contrast, when they are superimposed to form a compound feature boundary, segmentation performance is worse than for either channel alone. From these findings, we conclude that density segmentation may rely on neural mechanisms different from those which underlie feature segmentation, consistent with recent findings suggesting that density comprises a separate psychophysical 'channel'.