Machine Cognition and the Integration of Emotional Response in the Monitoring of Mental Disorders

Abstract

Computer science relies heavily on computational modeling and while the value of such an approach is generally recognized the methodological account of computational explanation is not up-to-date. In this paper we explore machine cognition with the creation of effective cognitive modeling and consider the elemental components that combine to create an effective cognitive model. The creation of such a model will enable the processing of information in intelligent context-aware systems while integrating emotion (more accurately stated as emotive response). We present a brief review of related research addressing cognitive science and machine cognition in which we identify the concept of self. Modeling is introduced with an overview of conceptual models and semiotics followed by consideration of implementation using a proposed approach based on fuzzy sets. We introduce depression as a use-case to illustrate the proposed approach and a general discussion where future directions for research and open research questions are considered. The paper closes with concluding observations. We posit that creating an effective cognitive model offers the potential to integrate emotive response and thereby improve context-aware systems in a broad and diverse range of domains and systems along with improvements in the levels of computational intelligence.