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About

Laura Cohen’s research sits at the compelling intersection of clinical psychiatry, human-robot interaction, and affective computing. Her most influential work, "Unravelling socio-motor biomarkers in schizophrenia" (42 citations), introduces a low-cost, non-invasive diagnostic tool using the ‘mirror-game’—a coordination task where partners mimic hand movements—to identify potential biomarkers from a patient’s solo movements. This breakthrough offers a novel pathway for early detection. Cohen further explores how humanoid robots can improve social competences in schizophrenia, demonstrating in a 2017 study (30 citations) that robotic social feedback enhances patient engagement. Her work on mind perception in schizophrenia (6 citations) probes how patients attribute mental states to robots, while her recent 2024 paper on human-robot mutual learning (2 citations) pioneers affective-linguistic interaction, challenging AI’s over-reliance on text. Cohen’s 2022 architecture for affect-grounded language learning (2 citations) reimagines language acquisition as an active, motivation-driven process. By blending robotics, psychology, and neuroscience, Cohen is forging tools that both diagnose social deficits and rehabilitate them, positioning her as a leader in socially assistive robotics.