Optimizing visual performance by adapting images to observers Michael A. Webster*

Visual adaptation is widely assumed to optimize visual performance, but demonstrations of functional benefits beyond the case of light adaptation remain elusive. The failure to find marked improvements in visual discriminations with contrast or pattern adaptation may occur because these become manifest only over timescales that are too long to probe by briefly adapting observers. We explored the potential consequences of color contrast adaptation by instead “adapting” images to simulate how they should appear to observers under theoretically complete adaptation to different environments, and then used a visual search task to measure the ability to detect colors within the adapted images. Color salience can be markedly improved for extreme environments to which the observer is not routinely exposed, and may also be enhanced even among naturally occurring outdoor environments. The changes in performance provides a measure of how much in theory the visual system can be optimized for a given task and environment, and can reveal the extent to which differences in the statistics of the environment or the sensitivity of the observer are important in driving the states of adaptation. Adapting the images also provides a potential practical tool for optimizing performance in novel visual contexts, by rendering image information in a format that the visual system is already calibrated for