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.
Goldenberg, E., Garcowski, J. and Beer, R.D. (2004). May we have your attention: Analysis of a selective attention task. In S. Schaal, A. Ijspeert, A. Billard, S. Vijayakumar, J. Hallam and J.-A. Meyer (Eds.), From Animals to Animats 8: Proceedings of the Eighth International Conference on the Simulation of Adaptive Behavior (pp. 49-56). MIT Press.