A Mathematical Description of Emotional Processes and Its Potential Applications to Affective Computing

In affective computing, machines should detect and recognize human emotions, and adapt their behavior to them. Nowadays these objectives are mainly achieved through the exploitation of machine learning techniques. These techniques are employed to process different emotional-related features and to produce classification labels or coordinates in a valence-arousal space. This approach, however, ignores the neurophysiological processes governing implicit emotional states and, consequently, suffers from substantial limitations. Moreover, machine learning methods employed today do not benefit from the knowledge of emotional dynamics for properly adapting themselves. In this manuscript, starting from recent neuroscience and computational theories, we show how a simple mathematical description of processes governing implicit emotional dynamics can be developed. Moreover, we discuss how our mathematical models can be exploited to improve tracking, estimation and active modulation of human emotions.