title: Computational models of working memory: putting long-term memory into context.
abstract: Detailed computational modeling of human memory has typically been aimed at either short-term (working) memory or long-term memory in isolation. However, recent research highlights the importance of interactions between these systems for both item and order information. At the same time, computational models of both systems are beginning to converge onto a common framework in which items are associated with an evolving "context" signal and subsequently compete with one another at recall. We review some of these models, and discuss a common mechanism capable of modelling working memory and its interaction with long-term memory, focussing on memory for verbal sequences.
authors.affiliation: Institute of Cognitive Neuroscience and Department of Anatomy and Developmental Biology, University College London, 17 Queen Square, London, WC1N 3AR, UK. n.burgess@ucl.ac.uk
authors.forename: Neil
authors.initials: N
authors.lastname: Burgess
authors.forename: Graham
authors.initials: G
authors.lastname: Hitch
mesh_terms: D001288:Attention / Q000502:physiology; D019295:Computational Biology; D006801:Humans; D017774:Long-Term Potentiation; D008570:Memory, Short-Term / Q000502:physiology; D010700:Phonetics; D011932:Reading; D012153:Retention, Psychology / Q000502:physiology; D012660:Semantics; D014706:Verbal Learning / Q000502:physiology