Transferring active inference to newly encountered prism-shifted environments

Active inference has been proposed as a unified explanation of perception and action. Previous work has shown that canonical neural networks that minimize shared Helmholtz energy are cast as performing active inference of the external environment. However, how animals flexibly adapt to newly encountered environments remains to be fully addressed. To investigate the brains generalizability and adaptability to new environments, this work develops canonical neural networks that employ multiple policy matrices in parallel. We demonstrate that the proposed model can recapitulate the prism adaptation--a form of visuomotor adaptation--under an arm-reaching task. Using policy matrices pretrained under various target positions, these networks could transfer previous experiences and exhibit faster adaptation to the prism-shifted environment than the naive networks. Furthermore, after-effects were observed following the removal of simulated prism glasses. These results suggest the biological plausibility and utility of the proposed model, providing insights into the adaptive capabilities of the brain.