Marco Jacono

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About

Marco Jacono investigates the intersection of human-robot interaction and cognitive neuroscience, focusing on how biological and artificial agents achieve seamless coordination. His work demonstrates that mutual synchronization—a hallmark of effective human collaboration—can be modeled and ported onto humanoid robots to improve joint task performance. In his most-cited study, "Adaptation to a humanoid robot in a collaborative joint task" (2017, 7 citations), Jacono shows that robots equipped with human-like synchronization models can foster emergent coordination, mirroring the adaptive dynamics seen in human dyads. This research bridges social cognition and robotics, offering practical pathways for more intuitive human-robot teamwork. Jacono also explores the mechanisms of proactive gaze behavior, revealing that when humans observe object manipulation tasks, their eyes anticipate events rather than reactively track motion—a pattern that breaks down when the same movement is not caused by a human actor (2011, 2 citations). This finding deepens our understanding of action perception and has implications for designing robots that elicit naturalistic gaze responses. Through these contributions, Jacono advances both theoretical models of joint action and applied frameworks for collaborative robotics.