Active inference for action-unaware agents

Active inference is a formal approach to study cognition that views adaptive agents as engaged in a constant process of approximate Bayesian inference, via the minimisation of variational and expected free energies. In this way, agents are able to maximise the likelihood of preferred observations or states, given a generative model of the environment. While most implementations in the literature rely on expected free energy as an appropriate way to evaluate the desirability of policies, i.e., sequences of actions, there are different ways to consider the contribution of past motor experience to the agent’s future behaviour. In some approaches, agents are assumed to have knowledge of their own executed actions, and use it for planning future ones. In others, agents are unaware of their actions, and must infer their motor behaviour to plan for the future. This difference reflects a standard point of departure in two leading frameworks in motor control based on the presence, or not, of an efference copy signal conveying information about an agent’s own actions. In this work we compare the performances of action-aware and action-unaware agents in two navigations tasks, showing how action-unaware agents can achieve performances comparable to action-aware ones while at a severe disadvantage.