Computational modeling of insight processes and artificial cognitive ontogeny

Abstract

Many approaches were proposed to model complex socially emotional behavior in virtual actors, such as intelligent tutors, creative assistants, or team partners. They still lack the ‘magic’ of human-level cognition: systems built for one paradigm appear clueless outside of its boundary. Natural cognitive systems, on the other hand, can adapt to unexpected environments and paradigms. To capture the robustness of natural cognitive development, a new approach is proposed here that enables the formation of new higher cognitive abilities in a model system, embedded in an unexpected environment. This is achieved based on the naturally developing grounding of innate abstract constructs (schemas). The mechanism producing this binding is that of creative insight. In this study, principles of insight processes borrowed from psychology are formalized and adapted for computer modeling. To do this, several examples of insight phenomena at different evolutionary levels and in different species are analyzed before the model is formulated based on the dual process theory, the signal model of insight, and the eBICA cognitive architecture framework. Results of its computer simulations prove the concept. One specific finding is that the accumulation of activation during the incubation period increases creative abilities of the system. It is argued that the proposed approach can explain a range of facts and mysteries associated with the human cognitive ontogeny and can provide the basis for a self-sustained evolution of future Artificial Intelligence.