In computational neuroscience, the free-energy principle holds that organisms minimize free energy via perception and learning. It includes predictive coding, where the brain updates an internal model by reducing prediction errors, and active inference, where organisms select actions that lower expected free energy and unify perception and planning. Despite support, human research often separates sensory processing and motor control. We propose a unified sensorimotor model based on the free-energy principle. We will implement the model, run simulations, and compare outputs with empirical data to evaluate it. It aims to advance HCI, psychology, and applications in developmental disorders and robotics.
GRANTS
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BOOST (young researchers), Japan Science and Technology Agency, 2025-2030.