Sensors represent a crucial link between the evolutionary forces shaping a
species' relationship with its environment, and the individual's cognitive
abilities to behave and learn.  We report on experiments using a new class
of "latent energy environments" (LEE) to define  environments of carefully
controlled complexity which allow us to state bounds for random and optimal
behaviors that are independent of strategies for achieving the behaviors.
Using LEE's analytic basis for defining environments, we then use  neural
networks (NNets) to model individuals and the GA to model an evolutionary
process shaping the NNets, in particular their sensors.  Our experiments
consider two types of  "contact"  and "ambient" sensors, and  variants
where the NNets are not allowed to learn, learn via error correction from
internal prediction, and via reinforcement learning.  We find that
predictive learning,  even when using a larger repoitoire of the more
sophisticated ambient sensors, provides no advantage over NNets unable to
learn.  However, reinforcement learning using a small number of  crude
contact sensors, does provide a significant advantage.  Our analysis of
these results points to a trade-off between the genetic "robustness" of
sensors and their informativeness to a learning system.