Causal learning in the Crib: A predictive processing formalization and babybot simulation

Predictive Processing (PP) [1], [2], [3] is becoming an influential account in cognitive neuroscience, including developmental neuroscience [4]. According to PP, human brains interpret their sensory inputs by predicting them, based on a hierarchy of generative models. These predictions are then compared to the actual, observed inputs, and the difference between predictions and observations (so-called prediction error) is used to update the agent's generative model about the world, to minimize future prediction errors. A key question for PP is how situated agents can learn these generative models. This question is especially important from a developmental perspective on PP. That is, the theory needs to specify how such generative models can be ‘developable’ at all, given that infants must somehow build these generative models from embodied, situated interaction with their environments.