  Although different learning systems are coordinated to afford complex
behavior, little is known about how this occurs. This article describes a
theoretical framework that specifies how complex behaviors that might be
thought to require error-driven learning might instead be acquired through
simple reinforcement. This framework includes specific assumptions about the
mechanisms that contribute to the evolution of (artificial) neural networks to
generate topologies that allow the networks to learn large-scale complex
problems using only information about the quality of their performance. The
practical and theoretical implications of the framework are discussed, as are
possible biological analogs of the approach.
