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Our modeling behaviors were based on the learning flow diagram introduced by Rosch et al.19 They used a hierarchical free-energy state model of a hidden Markov model. Seven activity areas exist in the hierarchy. The first three areas (upper division, middle division, and lower division) interact with one another. Each activity area holds a free-energy value that serves as an estimation of the model’s current prediction error. Prediction errors are derived from sensory observations via expectations and readjustments in precision. The activities in each area are assumed to be represented by crystallized or well-developed neural circuits. What makes this model intrinsically hierarchical is its structure. The activities in an area are controlled by another area that has direct connections to areas that belong to the same level and indirect connections to adjacent areas only. As a result, the model effectively develops a hierarchical free-energy state model. By developing free-energy models for biologically plausible activity areas, we could confirm that our model successfully developed sensory attenuation even when the free-energy models were not curated beforehand (Fig. 1e and Supplementary Fig. S4a). In these simulations, we used only sensory a posteriori prediction error, which is a biologically plausible context. Moreover, because the activity areas are reset after each time step, even though the models are top-down controlled, sensory attenuation emerges gradually, enabling the subject to learn the free-energy state model in a dynamical manner. The simulation study on our learning system is essential for the neurosciences. A model is a mathematical description of a biological system under a particular interpretation. To clarify this point, we used a model architecture with physiologically plausible properties, which is a key issue when developing biologically plausible models. However, the temporal dynamics of a biological system is not simply the result of neural connections and dynamics in an appropriate learning system. Our model’s computational performance using only biologically plausible circuits, such as those based on the Hodgkin-Huxley model, supports that our model is not simply a combination of the HMM and the MTS framework (Fig. 1e). 7211a4ac4a