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Parametrically Constrained Lightness Model Incorporating Edge Classification And Increment-Decrement Neural Response Asymmetries, Michael E. Rudd May 2016

Parametrically Constrained Lightness Model Incorporating Edge Classification And Increment-Decrement Neural Response Asymmetries, Michael E. Rudd

MODVIS Workshop

Lightness matching data from disk-annulus experiments has the form of a parabolic (2nd-order polynomial) function when matches are plotted against annulus luminance on log-log axes. Rudd (2010) has proposed a computational cortical model to account for this fact and has subsequently (Rudd, 2013, 2014, 2015) extended the model to explain data from other lightness paradigms, including staircase-Gelb and luminance gradient illusions (Galmonte, Soranzo, Rudd, & Agostini, 2015). Here, I re-analyze parametric lightness matching data from disk-annulus experiments by Rudd and Zemach (2007) and Rudd (2010) for the purpose of further testing the model and to try to constrain …