A Simple Recurrent Network for Implicit Learning of Temporal Sequences

A behavioural paradigm for learning arbitrary visuo-motor associations established that human observers learn to associate visual objects with their corresponding motor responses faster if the objects follow a temporal rule rather than if they were presented in a random order. Here, we use a simple recurrent network with a back propagation training algorithm adapted to a reinforcement learning scheme. Our simulations fit quantitatively as well as qualitatively to the behavioural results, endorsing the role of temporal context in associative learning scenarios.