I started my PhD in September 2017 on the project Understanding predictive processing in development: Modelling the generation of generative models (i.e., mental models that represent the causal structure of the environment).
Predictive processing is an influential paradigm in cognitive science that postulates that the brain implements these generative models which make hypotheses about the causes underlying sensory inputs, update probability of the hypotheses based on new sensory inputs and generate prediction errors. Within the broader framework of predictive processing, I am investigating two research lines. Whilst a lot is known about how the probability of the existing hypotheses in these models is updated, less is known about how these models are built up and changed in structure as a consequence of learning.
The first research line is thus investigating different ways in which the structure of generative models can change during learning. And the second line of my research is concerned with how information about the environment is represented in generative models. To investigate these questions, I am using a diverse range of tool ranging from conceptual analysis, empirical methods and formal and computational modelling.