Rutar’s thesis explored two research topics within the theoretical framework of predictive processing. She examined how information is represented in generative models and how generative models change in structure as a consequence of learning. One of the central tenets of predictive processing is that generative models make predictions based on their internal representation of the world. What exactly do generative models represent though? In Chapter 2 she investigated how the format of represented information changes depending on how the sensory input is structured. In Chapter 3 she further argued that two functional features of representations, coding for structural similarity and operating in a decoupled manner, are gradual and that modulating the degree of decoupling and structural similarity is conducive to behavioural success of learners. In predictive processing, learning has been mostly formalised as Bayesian model updating. Whilst the latter provides important insights into how humans change the degree of belief in existing hypotheses in light of new evidence, it cannot explain how new hypotheses are incorporated in generative models (i.e., structure learning). In Chapter 4 she argued that the only available computational implementation of structure learning in predictive processing fails to capture what is most distinctive about structure learning – the change in structure of generative models. We put forward an improved version of structure learning theory, by proposing a set of operations for changing the structure of generative models. Chapter 5 investigated how Bayesian model updating and structure learning can be computationally differentiated based on the prediction error dynamics. Finally, she discusses what implications the findings presented in the thesis have for understanding adult and child higher cognition.