  In this paper we present a deeper analysis than has previously been carried
out of a selective attention problem, and the evolution of continuous-time
recurrent neural networks to solve it. We show that the task has a rich
structure, and agents must solve a variety of subproblems to perform well. We
consider the relationship between the complexity of an agent and the ease with
which it can evolve behavior that generalizes well across subproblems, and
demonstrate a shaping protocol that improves generalization.
