Learning to speak a foreign language as an adult can be hard as it involves learning new speech sounds and motor commands. In a speech learning environment, feedback from our own speech allows us to monitor and correct ourselves. This project addresses the role of feedback-based speech monitoring in speech learning. Although feedback provides a mechanism to adapt our behaviour, no research to date has directly investigated the relationship between speech learning and feedback. We will investigate this link using behavioural and electrophysiological measures that capture individual variability in speech learning and changes to the brains speech production systems.

Our brain makes predictions about the future based on what it knows. However, what the brain actually predicts has remained largely unclear. This requires examining prediction in the real world, which is much more complex than typical lab experiments. This research will use new tools from artificial intelligence to examine how our brain makes predictions in real-life situations, how our brain is wired to do this, and how this affects our behavior, like what sparks our curiosity. By understanding this, we hope to achieve a richer understanding of how our brain works in naturalistic conditions.

Although it feels to us as if we perceive the external world objectively, perception is strongly influenced by prior expectations and beliefs (i.e. "priors"). How these priors are neurally implemented and combined with sensory input is still largely unclear. In the current proposal we focus on the role of spontaneous fluctuations of neural activity in perception. We hypothesize that these fluctuations constitute the ?priors?, which maintain an internal model of the environment. By studying their dynamics, relevance and implementation in the visual cortex, we hope to uncover a crucial computational role of spontaneous activity in embodying a prior world model.

Brain rhythms: the building blocks of the brain Dr. S. Haegens, Radboud University Nijmegen — Donders Institute for Brain, Cognition and Behaviour Every day, our brains receive an enormous amount of information, which needs to be filtered and processed. To accomplish this, it is crucial to connect the right brain areas at the right moment. The researchers will study how brain rhythms organize this.

The brain is the most intelligent information processor we know, but it does not come into the world this way. Most of the brain’s intelligent functions have to be learned from experience with the world. The key to understanding the brain, therefore, is to understand how the brain learns. I will target this important question, by combining knowledge about the brain and the environment from which it learns, with insights from self-learning computer algorithms. Thanks to recent, exponential developments in these algorithms, we are now in a position to apply similar techniques to model learning in the brain. Using visual perception as a test bed, I will adapt existing supervised learning methods into a new computational model of unsupervised learning in the brain’s visual cortex. From this model, I will distil concrete, testable predictions that I will validate against data from human participants performing perceptual tasks. By thus dovetailing computational and empirical methods, this research aims to understand how neurons wire together into complex information-processing networks. This not only addresses a fundamental and outstanding question in our understanding of the brain, but may also aid the development of more advanced self learning computer algorithms based on the same principles.