Brown Induction and Red/Green Hue Shifts

Abstract

This body of work details a number of experiments relating to brown induction (the change of appearance of a stimulus from yellow to brown) and shifts in red/green hue balance (the balance point of stimuli across the yellow-blue spectrum where they appear neither reddish nor greenish). These experiment involve variations of targets and their surrounding stimuli in the dimensions of luminance, saturation, and hue, as well as variations in the manner of their optical presentation (monocular [stimulus only in one eye], binocular [same stimulus both eyes], or dichoptic [different stimuli in each eye] presentations). It was found that brown induction is strongest for stimuli that are sufficiently darker than their surround and slightly desaturated. Although brown stimuli show a systematic shift in their red/green balance compared to yellow stimuli this appears to be controlled by a separate mechanism that is present for all targets that vary along the yellow-blue spectrum (including achromatic gray targets) when they are darker than their surrounds. It was also found that brown induction can occur both prior to and after the cortical combination of signals from the two eyes (in both monocular and binocular pathways, respectively), showing an enhancement from perceptually contiguous bright surrounds presented in either the same or opposite eye as a target. No similar effect was observed for red/green balances, which appear to be influenced only in monocular pathways and have a different relationship with surround contiguity. There are also differences in the effects of target and surround size on brown induction compared to red/green balance shifts. Brown represents a categorical hue change from yellow that is usually accompanied by a red/green balance shift, but such red/green balance shifts can also occur independently of a categorical hue changes as seen with achromatic gray stimuli. These many differences between brown induction and red/green balance shifts suggest that they are two separate phenomena that can co-occur but are not directly related, suggesting different neural mechanisms for each. In addition to academic significance, understanding and accurately modeling these effects is increasingly important for issues related to both human and machine vision.