An important cognitive feature –shared only by humans and a few other species– is self-consciousness. It has been defined as “the possession of the concept of the self and the ability to use this concept in thinking about oneself”. Self-consciousness undoubtedly depends on some kind of self-representation, although the nature of this self-representation in intelligent beings is still unknown. In recent years, several cognitive scientists have proposed self-representation models. Nevertheless, usually these models only represent the current state of consciousness. In this paper, we introduce the time dimension to extend self-representation models in order to represent the development of individual self-representation over time.
Another important cognitive feature of both humans and animals is that they have a sense of belonging. It has been defined as “the process by which an individual understands that other beings are like himself (herself)”. We focus on the social side of self-consciousness and self-representation by defining self-consciousness as a specialization of the sense of belonging.
In this paper, we use modular artificial neural networks for implementation. To test models, we implemented a simulator with modular neural networks composed of self-organized maps (SOM) and time delayed neural networks (TDNN). In this multi-agent system, agents were equipped with a simplified model of sensory perception, personality, sense of belonging and self-consciousness. Agent interaction is tested in different hypothetical social scenarios. The simulator structure and its MANN components are described in detail. The relation between a sense of belonging and self-consciousness is also discussed. Quantitative results are analyzed and conclusions stated.