This project investigates relational rule learning by exploring how the degree of understanding of a relational rule affects participants’ mastery rates across tasks of varying difficulty, whether prior training on a specific relational rule (rule matching) improves task accuracy, and whether interleaved pretraining—mixing different relational rules during training—enhances task performance. This project was my first experience with multilevel modeling, which I learned through hands-on lab work rather than formal coursework.
Collaborators: Julia Conti and Paulo Carvalho
