Keywords

lightness, increment, decrement, computational model, neural model

Abstract

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 the model parameters. Specifically, I test the model assumptions that: 1) lightness is computed by a process that spatially sums steps in log luminance across space, giving 1/3 the weight to incremental steps in log luminance that it gives to decremental steps in log luminance (defined in terms of luminance steps from the background to the target); 2) only luminance steps that are interpreted by the observer as steps in surface reflectance (as opposed to steps in illumination) contribute to the lightness computation. The quantitative analysis confirms these assumptions in the context of these simple displays, but it also necessitates a re-evaluation of the previous cortical interpretation of the data.

Start Date

13-5-2016 9:25 AM

End Date

13-5-2016 9:50 AM

 
May 13th, 9:25 AM May 13th, 9:50 AM

Parametrically Constrained Lightness Model Incorporating Edge Classification and increment-Decrement Neural Response Asymmetries

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 the model parameters. Specifically, I test the model assumptions that: 1) lightness is computed by a process that spatially sums steps in log luminance across space, giving 1/3 the weight to incremental steps in log luminance that it gives to decremental steps in log luminance (defined in terms of luminance steps from the background to the target); 2) only luminance steps that are interpreted by the observer as steps in surface reflectance (as opposed to steps in illumination) contribute to the lightness computation. The quantitative analysis confirms these assumptions in the context of these simple displays, but it also necessitates a re-evaluation of the previous cortical interpretation of the data.