Statistical learning (SL) is a crucial capacity supporting efficient cognition and action throughout the lifespan. SL is an organism’s ability to attune to coherent covariation in its environment (e.g., an infant learning sound patterns of a language, or visual patterns corresponding to objects). This project uses recurrent neural network modeling to investigate the types of computations that may support human SL. The modeling motivates EEG and MEG studies with human subjects. Together, the modeling and experiments will advance our understanding of the nature and computational basis for human SL.
Job description: collaborating on finalizing experimental designs, implementing them, and collecting data; analysing EEG and MEG data; writing research papers under the supervision of the PI, aiming to publish at top-tier journals; dissemination of results at international scientific conferences.
Application deadline: 15 November 2024