An important goal of vision science is to understand the coding strategies underlying the representation of visual information. I will describe experiments and analyses where we have explored these coding strategies using two different approaches. In the first approach, we factor-analyzed individual differences in observers’ color judgments to reveal the representational structure of color appearance. In contrast to conventional opponent-color models, these results point to a population code in which different color categories vary independently and for which there are no opponent axes. We also show that the factors underlying two-dimensional color space are fundamentally different from those of other two-dimensional visual attributes, such as planar motion. In a second study, we developed criteria for using patterns of adaptation aftereffects to discriminate between alternative coding models. We focus on two contrasting models of face aftereffects that have been proposed based on exemplar vs. norm-based encoding strategies. We show that the critical difference between these models, in terms of the aftereffect patterns they predict, depends more on how the channel activations are combined (decoding) rather than the properties of the channels themselves (encoding). Together, these results challenge assumptions of traditional coding theories and add to the value of individual differences and adaptation as tools for probing the coding strategies of visual perception.