Human awareness viewed from natural language concept formation: Focusing on affective words related to facial expressions

Mental Image Directed Semantic Theory (MIDST) has already shown that each physical event concept (e.g., “carry”, “separate”) in natural language is characterized by a so-called “event pattern”, abstract pattern formed by the constituents of its referents. Therefore, people are assumed significantly aware of the event pattern involved when they cognize or recognize a physical event discerned with the others. Such event patterns are modeled as so-called “loci in attribute spaces” in MIDST. This is also the case for mental event concepts (e.g., “love”, “sympathize”). For example, a 5-dimensional attribute space can be provided for human emotion. This paper describes an approach toward a human mentality, so called Kansei, in order to provide robots with a function to measure peoples' emotions toward external things, focusing on human awareness in concept formation of affective words related to facial expressions of Buddhism statues.