A new model for border-ownership computation reflecting global configuration and consistency of surface properties N Kogo1, V Froyen2, J Wagemans1 |
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1Laboratory of Experimental Psychology, University of Leuven (KU Leuven), Belgium |
| We developed a model (Kogo et al, 2010, Psychological Review, 117(2), 406-439) that reproduces figure-ground organization by implementing global interactions of border-ownership (BOWN) signals. The algorithm works in favor of convex shapes, corresponding to human perception. However, in certain conditions, this convexity preference is reduced. For example, if a convex region is on top of another surface and has the same color/texture as the background, it is often perceived as a hole. The preference of convex regions in repetitive columnar configurations is also reduced if the concave regions have inconsistent colors (Peterson and Salvagio, 2008, Journal of Vision, 8(16):4, 1-13). These data suggest that consistency of surface properties plays a key role in figure-ground organization. Importantly, Zhou et al. (2000, Journal of Neuroscience, 20(17), 6594-6611) showed that roughly half of BOWN sensitive neurons in V2/V4 were also sensitive to contrast polarity. We developed a new algorithm so that only when interacting BOWN signals are consistent in both owner side and contrast, their signals are enhanced. With this, we are able to reproduce the reversal of the convexity preference. The general implications of this new approach will also be discussed. |
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