Reinforcement Emotion-Cognition System: A Teaching Words Task

Abstract

The goal of this paper is to suggest a system for intelligent learning environments with robots modeling of emotion regulation and cognition based on quantitative motivation. A detailed interactive situation for teaching words is proposed. In this study, we introduce one bottom-up collaboration method for emotion-cognition interplay and behaviour decision-making. Integration with gross emotion regulation theory lets the proposed system adapt to natural interactions between students and the robot in emotional interaction. Four key ideas are advocated, and they jointly set up a reinforcement emotion-cognition system (RECS). First, the quantitative motivation is grounded on external interactive sensory detection, which is affected by memory and preference. Second, the emotion generation triggered by an initial motivation such as external stimulus is also influenced by the state in the previous time. Third, the competitive and cooperative relationship between emotion and motivation intervenes to make the decision of emotional expression and teaching actions. Finally, cognitive reappraisal, the emotion regulation strategy, is introduced for the establishment of emotion transition combined with personalized cognition. We display that this RECS increases the robot emotional interactive performance and makes corresponding teaching decision through behavioural and statistical analysis.