Dynamic Inference by Model Reduction.

How can agents infer the intentions of others by simply observing their behavior? And how can they generate fast and accurate actions such as grasping a moving object on the fly? Recent advances in Bayesian model reduction have led to innovative, biologically plausible approaches to actively infer the state of affairs of the world and perform planning with continuous signals. However, reducing the surrounding environment into a small set of simpler hypotheses remains a challenge in highly dynamic contexts. In this study, we propose an approach, based on active inference, that employs dynamic priors sampled from reduced versions of a generative model. Each dynamic prior corresponds to an alternative evolution of the world, which the agent can evaluate by accumulating continuous data. We test our approach on two everyday tasks: inferring a trajectory and grasping a moving object. Our findings reveal how agents can smoothly infer and enact dynamic intentions, and emphasize the key role of intentional gain or precision in motor learning.