Abstract
This study investigates the effect of joint activities on the joint Simon effect (JSE) when the collaborator is a human or bot. In human-activity-recognition research, sensing data from a virtual reality (VR) environment are used to classify a pair’s activities as a target tag of cooperation, conformity, and competition. The collaborator performing the JSE task in VR space is replaced with bots during the sessions without the participant’s notice, thereby creating a human or bot experimental condition. Analysis results show that cooperative activity is observed under human conditions, whereas a higher proportion of conformity is observed under bot conditions. The synchrony index, as calculated based on important features for classification, is lower in the bot condition compared with that in the human condition. In conclusion, our classification model successfully classifies interpersonal activities using VR sensor data and can distinguish between humans and bots. (143 words)