Modeling second-order boundary perception: A machine learning approach
Fig 1
Examples of second-order boundary stimuli, with varying texture density and modulation depth.
(a) Top: A natural occlusion boundary formed by animal fur (foreground, lower right) occluding the forest floor (background, upper left). This boundary has similar average luminance on both sides, but clearly visible differences in texture. Bottom: A contrast-defined boundary (used in Experiment 3) with identical mean luminance in each region, but different contrasts of the texture elements. In this example the contrast modulation envelope has a left-oblique (-45 deg. w.r.t vertical) orientation. (b) Examples of contrast-modulated micropattern stimuli, for three densities of micropatterns and three modulation depths, all having a right-oblique (+45 deg.) boundary orientation. Boundary segmentation is typically easier with increasing modulation depth and micropattern density. (c) Schematic illustration of the two psychophysical tasks used here (orientation identification: Experiments 1 and 3, orientation discrimination: Experiment 2), in both cases two-alternative forced-choice judgements of left- vs. right-oblique boundary orientation. Stimuli shown are representative of those used in Experiments 1 and 2. In the identification task boundaries are oriented at (+/- 45 deg.), and in the discrimination task boundaries are oriented slightly off vertical (+/- 6–7 deg.).