Ego-centric and allo-centric abstraction in self-organized hierarchical neural networks

The computational systems supporting the cognitive capacity of artificial agents are often structured hierarchically, with sensory-motor details placed in the lower levels, and abstracted conceptual items in the upper levels. Such an architecture mimics the structural properties of the animal and human nervous system. To operate efficiently in varying circumstances, artificial agents are necessary to consider both ego-centric (i.e. self-centered) and allo-centric (i.e. other-centered) information, which are further combined to address given tasks. The present work investigates effective assemblies for simultaneously placing ego-centric and allo-centric processes in the cognitive hierarchy, by evolving self-organized neural network controllers. The systematic study of the internal network mechanisms has showed that effective neural assemblies are developed by placing allo-centric information in the upper levels of the cognitive hierarchy, followed by ego-centric abstracted representations in the middle and finally sensory-motor details in the lower level. We present and discuss the obtained results considering how they are related with known assumptions about human brain functionality.