This project aims at developing a model of sequence learning that is able to (1) simulate the time course of information
processing during serial reaction time tasks, and (2) to make it possible to account for the differences between incidental anticipation
(upon which implicit learning is based in these situations) and conscious prediction. To do so, we use neurally inspired models,
such as Elman's Simple Recurrent Network.