A microslip is a type of action hesitation we experience in everyday life, which highlights the gap between human action and machine action patterns. By proposing a simple computational model for microslips, we examine the microslip as an implicit parallel dynamics underneath human cognition. Here, an agent, given as a dynamical system of a simple neural architecture, takes one of two choices, whose neural net is evolved using a genetic algorithm. An evolved agent often shows a hierarchy of action primitives and intentionality, and the agent is sensitive to the subtle differences of the object's layout, which results in a complex basin structures in the action-selection landscape.
We investigate whether academic emotions are affected by the color of a robot's eyes in lecture behaviors. In conventional human-robot interaction research on robot lecturers, the emphasis has been on robots assisting or replacing human lecturers. We expanded these ideas and examined whether robots could lecture using one's behaviors that are impossible for humans. Psychological research has shown that color affects emotions. Because emotion is strongly related to learning, and a framework of emotion control is required. Thus, we considered whether emotions related to the learner's academic work, called "academic emotions," can be controlled by the color of a robot's illuminated eye light. In this paper, we found that the robot's eye light color affects academic emotions and that the effect can be manipulated and adapted to individuals. Furthermore, the manipulability of academic emotions by color was confirmed in a situation mimicking a real lecture.
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