Normalization of signals coming from linear sensors is an ubiquitous mechanism of neural adaptation.1 Local
interaction between sensors tuned to a particular feature at certain spatial position and neighbor sensors explains
a wide range of psychophysical facts including (1) masking of spatial patterns, (2) non-linearities of motion
sensors, (3) adaptation of color perception, (4) brightness and chromatic induction, and (5) image quality
Although the above models have formal and qualitative similarities, it does not necessarily mean that the
mechanisms involved are pursuing the same statistical goal. For instance, in the case of chromatic mechanisms
(disregarding spatial information), different parameters in the normalization give rise to optimal discrimination
or adaptation, and different non-linearities may give rise to error minimization or component independence.
In the case of spatial sensors (disregarding color information), a number of studies have pointed out the benefits
of masking in statistical independence terms. However, such statistical analysis has not been performed for
spatio-chromatic induction models where chromatic perception depends on spatial configuration.
In this work we investigate whether successful spatio-chromatic induction models,6 increase component independence
similarly as previously reported for masking models. Mutual information analysis suggests that
seeking an efficient chromatic representation may explain the prevalence of induction effects in spatially simple