Ok, this is actually an area on which I'm not well-informed, which is why I'm asking you instead of "looking it up" -- I'd like to better understand exactly what I want to look up.

Let's say we want to build a machine that can form accurate predictions and models/categories from observational data of the sort we encounter in the real world -- somewhat noisy, and mostly "uninteresting" in the sense that you have to compress or ignore some of the data in order to make sense of it. Let's say the approach is very general -- we're not trying to solve a specific problem and hard-coding in a lot of details about that problem, we're trying to make something more like an infant.

Would learning happen more effectively if the machine had some kind of positive/negative reinforcement? For example, if the goal is "find the red ball and fetch it" (which requires learning how to recognize objects and also how to associate movements in space with certain kinds of variation in the 2d visual field) would it help if there was something called "pain" which assigned a cost to bumping into walls, or something called "pleasure" which assigned a benefit to successfully fetching the ball?

Is the fact that animals want food and positive social attention necessary to their ability to learn efficiently about the world? We're evolved to narrow our attention to what's most important for survival -- we notice motion more than we notice still figures, we're better at recognizing faces than arbitrary objects. Is it possible that any process needs to have "desires" or "priorities" of this sort in order to narrow its attention enough to learn efficiently?

To some extent, most learning algorithms have cost functions associated with failure or error, even the one-line formulas. It would be a bit silly to say the Mumford-Shaw functional feels pleasure and pain. So I guess there's also the issue of clarifying exactly what desires/values are.