We advance a novel computational model of the acquisition of a hierarchical action repertoire and its use for observation, understanding and motor control. The model is grounded in a principled framework to understand brain and cognition: active inference. We exemplify the functioning of the model by presenting four simulations of a tennis learner who observes a teacher performing tennis shots and forms hierarchical representations of the observed actions - including both actions that are already in her repertoire and novel actions - and finally imitates them. Our simulations that show that the agent’s oculomotor activity implements an active information sampling strategy that permits inferring the kinematics aspects of the observed movement, which lie at the lowest level of the action hierarchy. In turn, this low-level kinematic inference supports higher-level inferences about deeper aspects of the observed actions, such as their proximal goals and intentions. Finally, the inferred action representations can steer imitative motor responses, but interfere with the execution of different actions. Taken together, our simulations show that the same hierarchical active inference model provides a unified account of action observation, understanding, learning and imitation. Finally, our model provides a computational rationale to explain the neurobiological underpinnings of visuomotor cognition, including the multiple routes for action understanding in the dorsal and ventral streams and mirror mechanisms.
Simulating Emotions: An Active Inference Model of Emotional State Inference and Emotion Concept Learning
