Traits Inference on Virtual Agents Driven by Cognitive Curiosity Models

In the field of Human-Agent Interaction (HAI), many researchers have so far studied trait inference on artificial agents. However, most of them have used appearance and behavior as manipulation factors and have not directly manipulated the internal factors underlying the generation of agent behavior. To solve such issues, we argue the use of cognitive models as a tool to examine the process of participants’ trait inference on virtual agents. By using cognitive models that detail internal states, we can experimentally assume the link between behavior and cognitive characteristics. In this study, we use the Adaptive Control of Thought–Rational (ACT-R), which is a framework for constructing cognitive models, to construct a model and examine whether the properties of the model can be evaluated by humans. We focused on “curiosity,” a property of agents, and visualized the behavior of multiple cognitive models of curiosity as material for subjective ratings on agents’ traits. As a result, we confirmed that participants can estimate the properties of the models of curiosity. This suggests that it is possible to connect psychological findings with issues in the domain of HAI by using cognitive models.