Emotion generation integration into cognitive architecture

Abstract

Emotions play an important role in human intelligence and human behavior. It has become important to model emotions, especially in the context of cognitive architecture. Current models of emotion are greatly underdetermined by experimental data from psychology, cognitive science, and neuroscience literature. I raise the hypothesis that deeper integration between emotion and cognition will produce models with much greater explanatory power. The thesis is that the use of a semantic associative network as a memory model will serve to both deepen and broaden integration between emotion and cognition. To test this, an affective cognitive architecture will be built with a semantic associative network at its heart, and will be compared to existing models as well as tested against existing experimental data. © 2011 Springer-Verlag.