Papers
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Total Citations
11
H-Index
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About
Nori Jacoby is a cognitive scientist and computational researcher whose work sits at the intersection of music cognition, human-robot interaction, and cross-cultural perception. Best known for pioneering methodologies that combine machine learning with human behavioral experiments, Jacoby has made significant contributions to understanding how humans perceive rhythm, timing, and sound across diverse cultural contexts. His research employs iterated learning paradigms and large-scale international collaboration to uncover universal and culturally specific patterns in auditory cognition, work that has garnered substantial attention within cognitive science and music psychology communities. Among his more recent contributions, Jacoby has ventured into the emerging field of human-robot interaction, co-authoring "Giving Robots a Voice: Human-in-the-Loop Voice Creation and Open-Ended Labeling" (2024, 11 citations), which addresses the nuanced challenge of aligning a robot's vocal qualities with its physical appearance. By developing human-in-the-loop frameworks that capture the rich vocabulary of both voice and robot embodiment, this work opens new pathways for more intuitive and socially resonant human-robot communication. Jacoby's interdisciplinary approach—bridging acoustics, cognitive psychology, and AI—positions him as an innovative voice in shaping how humans and machines interact through sound.
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Top Papers
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