he Bayesian framework for probabilis- tic inference provides a general approach to understanding how problems of induc- tion can be solved in principle and per- haps how they might be solved in the hu- man mind. Let us give a few examples. Vision researchers are interested in how the mind infers the intrinsic properties of a ob- ject (e.g., its color or shape) as well as its role in a visual scene (e.g., its spatial re- lation to other objects or its trajectory of motion). These features are severely under- determined by the available image data. For instance, the spectrum of light wavelengths reflected from an object’s surface into the observer’s eye is a product of two unknown spectra: the surface’s color spectrum and the spectrum of the light illuminating the scene. Solving the problem of “color constancy” – inferring the object’s color given only the light reflected from it, under any conditions of illumination – is akin to solving the equa- tion y = a × b for a given y,withoutknow- ing b. No deductive or certain inference is possible. At best, we can make a reasonable guess, based on some expectations about which values of a and b are more likely a priori. This inference can be formalized in a Bayesian framework (Brainard & Free- man, 1997), and it can be solved reasonably well given prior probability distributions for natural surface reflectances and illumination spectra.