Top-Down and Bottom-Up Interactions between Low-Level Reactive Control and Symbolic Rule Learning in Embodied Agents

Mammals bootstrap their cognitive structures through embodied interaction with the world. This raises the question: how the reactive control of initial behavior is recurrently coupled to the high-level symbolic representations they give rise to? We investigate this question in the framework of the "Distributed Adaptive Control" (DAC) cognitive architecture, where we study top-down and bottomup interactions between low-level reactive control and symbolic rule learning in embodied agents. Reactive behaviors are modeled using a neural allostatic controller, whereas high-level behaviors are modeled using a biologically-grounded memory network reflecting the role of the prefrontal cortex. The interaction of these modules in a closed-loop fashion suggests how symbolic representations might have been shaped from low-level behaviors and recruited for behavior optimization.