Modeling paired associative learning in human cognition

The goal of this project is to find evidence supporting some of the theories underlying human associative memory models. Using Recurrent neural networks to model associative memory, we analyze its ability to fit human associative learning data and provide support for the extent of correlation among forward and backward associations in paired-associate learning tasks. Joint work with Dr. David Huber, Psychology & Brain Sciences.