Beyond the Adult Mind: A Developmental Framework for Predictive Processing in Infancy

Predictive Processing has been proposed as the single unifying computation underlying all of cognition, and proponents argue that all psychological phenomena can be explained as consequences of this principle. This theoretical framework has inspired many cognitive scientists and neuroscientists, but it currently has no developmental mechanism that would explain how infants begin to perceive and learn about the world. Rather, it treats human cognition as if it exists in a fully developed adult with a history of observations and world knowledge. In its current formulation, Predictive Processing only allows for perception of incoming stimuli given the existence of expectations based on previous experiences and as such does not allow for an infant to ever make a first observation. In this paper, we propose a possible starting point from which the infant can begin to develop predictive models, as well as a toolkit necessary to allow the infant to perform the range of cognitive operations on predictive models necessary for learning. The starting point we propose is a set of low-precision, low level-of-detail predictions with little or no hierarchical structure, which is very rapidly updated to reflect the infant's early environment. The toolkit contains a range of operations referred to collectively as structure learning, which are applied to models in order to allow for building adult-like hierarchical models. These modifications are necessary for developmental scientists to be able to adopt the Predictive Processing framework and benefit from its advantages, but also for Predictive Processing to be able to explain all human cognition, which inherently must include development.