EMOTION-II Model: A Theoretical Framework for Happy Emotion as a Self-Assessment Measure Indicating the Degree-of-Fit (Congruency) between the Expectancy in Subjective and Objective Realities in Autonomous Control Systems

The second phase of the “EMOTION-II” model (“Emotional Model Of the Theoretical Interpretations Of Neuroprocessing”) introduces the theoretical framework for the evolution of emotion as an internal measure of modeling errors (discrepancy signals) for assessing the degree-of-fit (congruency) between internal model and external world in autonomous control systems. It is derived based on the inevitable real-world consequence that modeling errors often occur in the internal model that represents the external world. When the contextual abstraction of the external world is compared with the internal world model, the discrepancy between the two models (objective reality and subjective reality) serves as a feedback for self-corrective actions. The assessment and recognition of these internally generated signals representing modeling errors of expectancy (and conversely, congruency between the two realities) form the basis for emotion formation in animals and other self-correcting autonomous control systems.