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Acquiring Rules for Rules: Neuro-Dynamical Systems Account for Meta-Cognition

Laboratory for Behavior and Dynamic Cognition, Brain Science Institute, RIKEN, mmaniada{at}brain.riken.jp

Jun Tani

Laboratory for Behavior and Dynamic Cognition, Brain Science Institute, RIKEN, tani{at}brain.riken.jp

Both animals and humans use meta-rules in their daily life, in order to adapt their behavioral strategies to changing environmental situations. Typically, the term meta-rule encompasses those rules that are applied to rules themselves. In cognitive science, conventional approaches for designing meta-rules follow human hardwired architectures. In contrast to previous approaches, this study employs evolutionary processes to explore neuronal mechanisms accounting for meta-level rule switching. In particular, we performed a series of experiments with a simulated robot that has to learn to switch between different behavioral rules in order to accomplish given tasks. Continuous time recurrent neural networks (CTRNN) controllers with either a fully connected or a bottleneck architecture were examined. The results showed that different rules are represented by separate self-organized attractors, while rule switching is enabled by the transitions among attractors. Furthermore, the results showed that neural network division into a lower sensorimotor level and a higher cognitive level enhances the performance of the robot in the given tasks. Additionally, meta-cognitive rule processing is significantly supported by the embodiment of the controller and the lower level sensorimotor properties of environmental interaction.

Key Words: meta-level cognition • meta-cognitive dynamics • evolutionary self-organization • cognitive robotics • dynamical systems • recurrent neural networks

Adaptive Behavior, Vol. 17, No. 1, 58-80 (2009)
DOI: 10.1177/1059712308101739


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