This research delves into Human-Human (H-H) and Human-NonHuman (H-NH) social interactions, exploring the impact of virtual environments and Human-Robot Interaction (HRI). Through EEG, audio recordings, and questionnaires, the study investigates brain activity, emotional states, and behavioral responses. Novel findings include distinct brain states in virtual environments and the human brain's ability to differentiate between robots and humans, with heightened concentration and motivation during HRI. Multidisciplinary analyses of H-H and H-NH interactions provide insights into diverse parameters. The study highlights the importance of designing nonhuman agents with human-like behavioral traits rather than focusing solely on appearance. A model achieving 82% accuracy in recognizing human voices based on interlocutor nature is developed. This research bridges gaps in existing literature, emphasizing the need for understanding human reactions to enhance nonhuman agent design and improve cognitive and emotional responses in social interactions. Further extensive research is recommended for a comprehensive understanding of H-NH interactions.