We study an adaptive controller that adjusts its internal parameters by self-organization of its interaction with the environment. We show that the parameter changes that occur in this low-level learning process can themselves provide a source of information to a higher-level context-sensitive learning mechanism. In this way, the context is interpreted in terms of the concurrent low-level learning mechanism. The dual learning architecture is studied in realistic simulations of a foraging robot and of a humanoid hand that manipulated an object. Both systems are driven by the same low-level scheme, but use the second-order information in different ways. While the low-level adaptation continues to follow a set of rigid learning rules, the second-order learning modulates the elementary behaviors and affects the distribution of the sensory inputs via the environment.

DOI: 10.1142/S0219525909002258