This interdisciplinary workshop aims to break boundaries between the researchers who develop human models (e.g., from the fields of human factors, cognitive psychology, and computational neuroscience) and roboticists who use human models in different human-robot interaction (HRI) contexts. The keynote talks, contributed submissions, and interactive discussions will focus on the questions such as: How can modeling humans help us understand and design human-robot interactions? What kinds of models are useful for which HRI contexts (physical/cognitive interactions) and purposes (behavior prediction/personalization/theory-of-mind/etc.)? What common lessons can be learned from human behavior modeling in HRI across different application domains? How can modeling humans in HRI tasks help us to better understand human cognition/behavior? By stimulating an interdisciplinary conversation around these questions, we aim to raise awareness of the benefits of modeling and expose the wider HRI community to a variety of different modeling approaches, and facilitate the HRI researchers who already engage in modeling to exchange views on methodology of modeling and best practices from diverse fields.

Human perception and behavior are affected by the situational context, in particular during social interactions. A recent study demonstrated that humans perceive visual stimuli differently depending on whether they do the task by themselves or together with a robot. Specifically, it was found that the central tendency effect is stronger in social than in non-social task settings. The particular nature of such behavioral changes induced by social interaction, and their underlying cognitive processes in the human brain are, however, still not well understood. In this paper, we address this question by training an artificial neural network inspired by the predictive coding theory on the above behavioral data set. Using this computational model, we investigate whether the change in behavior that was caused by the situational context in the human experiment could be explained by continuous modifications of a parameter expressing how strongly sensory and prior information affect perception. We demonstrate that it is possible to replicate human behavioral data in both individual and social task settings by modifying the precision of prior and sensory signals, indicating that social and non-social task settings might in fact exist on a continuum. At the same time, an analysis of the neural activation traces of the trained networks provides evidence that information is coded in fundamentally different ways in the network in the individual and in the social conditions. Our results emphasize the importance of computational replications of behavioral data for generating hypotheses on the underlying cognitive mechanisms of shared perception and may provide inspiration for follow-up studies in the field of neuroscience.