Abstract
How does the brain adapt to slow changes in the body’s kinematic chain? And how can it perform complex operations that need tool use? Here, we consider both processes through the same perspective and propose that the kinematic chain is represented by an Active Inference model encoding, in a hierarchical fashion, intrinsic and extrinsic information separately. However, the several pathways through which prediction errors can be minimized introduce some optimization problems. We show that an agent can rapidly change its kinematic chain online using action-perception cycles, similar to how learning and inference processes are handled in Predictive Coding Networks.
Supported by European Union H2020-EIC-FETPROACT-2019 grant 951910 to IPS and Italian PRIN grant 2017KZNZLN to IPS.
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Priorelli, M., Stoianov, I.P. (2024). Efficient Motor Learning Through Action-Perception Cycles in Deep Kinematic Inference. In: Buckley, C.L., et al. Active Inference. IWAI 2023. Communications in Computer and Information Science, vol 1915. Springer, Cham. https://doi.org/10.1007/978-3-031-47958-8_5
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