Matteo Priorelli

National Research Council, Institute of Cognitive Sciences and Technologies

Papers

Total Citations

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About

Matteo Priorelli is a rising researcher at the forefront of computational neuroscience, specializing in motor control, active inference, and hierarchical planning. His work bridges the gap between cognitive science and artificial intelligence by exploring how the brain selects and executes actions through continuous-time active inference—a framework where organisms minimize prediction errors to adapt. Priorelli’s most cited paper, “Modeling Motor Control in Continuous Time Active Inference: A Survey” (2023, 14 citations), provides a comprehensive overview of how ideomotor theory and cybernetics challenge traditional optimal control approaches, offering a fresh perspective on action selection. His more recent work, “Dynamic Planning in Hierarchical Active Inference” (2025, 8 citations), introduces a novel concept of dynamic planning, where the brain infers and imposes motor trajectories linked to cognitive decisions. This hierarchical approach has significant implications for understanding biological adaptation and developing more human-like AI systems. With a growing citation record and a focus on integrating theoretical insights with practical models, Priorelli is making notable contributions to the fields of embodied cognition and computational psychiatry. His research is particularly valuable for students and researchers interested in the neural underpinnings of movement and decision-making.