Uncovering the neural mechanisms of predictive processing in language

Project Description for Find-A-PhD Advert (max 400 words). This will be the text that will be advertised to prospective students.

This project uses advanced analyses of electrical brain activity (EEG) to investigate the neural mechanisms of predictive processing in the human brain. Rather than passively waiting for input to arrive, the brain actively thinks ahead and continuously compares the input against internally generated predictions. Predictions likely underpin rapid processing of predictable input as well as learning from unexpected input.

This is especially the case for human language, which unfolds on a particularly rapid time scale (around 250 written words or 200 spoken syllables per minute). However, it remains unclear exactly how predictions modulate the way stimuli are processed, and whether predictive mechanisms are specific to language or generalize to the processing of non-linguistic sequences.

This project will systematically vary predictability in sentences and/or other stimulus sequences and examine its effects on temporal and spectral aspects of the EEG. In addition, the project will apply state of the art machine learning techniques to the EEG signal that make it possible to decode the predictions that people form about upcoming input before the input is perceived.

This will allow for testing a range of hypotheses about predictive mechanisms. Is predictable input processed thoroughly, because predicted and actual input converge to yield a strong representation? Or is predictable input instead processed in a shallow fashion, as the brain merely verifies that the prediction was confirmed?

And when a prediction gets disconfirmed, what happens to the original prediction: does it linger, or is it suppressed and updated? The results will help advance our understanding of the fundamental mechanisms of predictive processing and shed new light on the processes that make people such efficient language users.