Neural model of lightness scaling in the staircase Gelb illusion.
Journal of vision(2022)
摘要
In the staircase Gelb illusion, the range of perceived reflectances of grayscale papers arranged from darkest to lightest in a spotlight is highly compressed relative to the range of actual paper reflectances: close to the cube-root compression predicted from applying Stevens' brightness law to the reflected light. Reordering the papers reveals additional effects of spatial paper arrangement on lightness. Here, I model both the perceptual scaling and spatial arrangement effects with a computational neural model based on the principle of edge integration (Rudd, J Vision, 2013; J Percept Imaging, 2020). Edge contrasts are encoded by ON and OFF cells described by Naka-Rushton input-output functions having different parameters for ON and OFF cells. These neural responses are subsequently log-transformed, then integrated across space, to compute lightness. Edges are thus neurally weighted on the basis of two independent factors: 1) distance of the edge from the paper whose lightness is computed, and 2) the edge contrast polarity (whether the edge is a luminance increment or decrement in the target direction). Polarity-dependent weightings of 0.27 for increments and 1.0 for decrements were derived from physiological ON and OFF cell data from macaque LGN. Distance-dependence was modeled as an exponential decay with space constant 1.78 deg. The model accounts to within <5% error for lightness matches made to staircase Gelb and reordered grayscale surfaces in an actual 3D environment.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要