Learning the structure of event sequences is a ubiquitous problem in cognition and particularly in language. One possible solution is to learn a probabilistic generative model of sequences that allows making predictions about upcoming events. Though appealing from a neurobiological standpoint, this approach is typically not pursued in…

The ability to learn temporal patterns in sequenced actions was investigated in elementary-school age children. Temporal learning depends upon a process of integrating timing patterns with action sequences. Children ages 6-13 and young adults performed a serial response time task in which a response and a timing sequence were presented repeatedly…

An associative model of serial learning is described based on the assumption that the effective stimulus for a serial-list item is generated by adaptation-level coding of the item's ordinal position. How the model can generate predictions of aspects of serial-learning data is illustrated. (SLD)