The current paper studies possible neuronal mechanisms for meta-level cognition of rule switching. In contrast to the conventional approach of hand-designing the cognitive functions, our study employs evolutional processes to search for neuronal mechanisms accounting for meta-level cognitive functions required in the investigated robotic tasks. Our repeated simulation experiments showed that the different rules are embedded in separate self-organized attractors, while rule switching is enabled by the transitions among attractors. Furthermore, the results showed that although certain segregation between the lower sensory-motor level and the higher cognitive level enhance the task performance, meta-level cognition is significantly supported by the embodiment and the lower level sensory-motor properties. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Maniadakis, M., & Tani, J. (2008). Dynamical systems account for meta-level cognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5040 LNAI, pp. 311–320). https://doi.org/10.1007/978-3-540-69134-1_31
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