Predicting the future: How our brain predicts future events for successful navigation through our dynamic environment.

To successfully navigate our dynamic environment, our brain needs to continuously update its representation of external information. This poses a fundamental problem: how does the brain cope with a stream of dynamic input? It takes time to transmit and process information along the hierarchy of the visual system.

Our capacity to interact with dynamic stimuli in a timely manner (e.g., catch a ball) suggests that our brain generates predictions of unfolding dynamics.

While predictive processing theories assume an internal representation of future external states, empirical research typically employs neural measures that capture an indirect consequence of prediction. The representational nature of predictions remains largely unexplored.

One approach for investigating neural representations is representational similarity analysis (RSA), which typically uses models of static stimulus features at different hierarchical levels of complexity (e.g., color, shape, category, concept) to investigate how these features are represented in the brain.

I have recently developed a novel dynamic extension to RSA that uses temporally variable models to capture neural representations of dynamic stimuli. A proof-of-concept MEG study unveiled predictive neural representations of naturalistic dynamic input.

These promising initial results open the door for addressing important outstanding questions on how our brain represents and predicts the dynamics of the world. In this project I aim to answer these questions.

I will execute the project at the prestigious Donders Institute in the lab of Floris de Lange, a leading expert in the field of predictive processing.

The proposed project is unique as it tackles the rich dynamics of predictive processing in terms of neural representations.

It will allow me to conduct creative, original and important research, which will propel my scientific career, and strengthen my competitiveness as a scientist.