Short-term memory for serial order: a recurrent neural network model.
Despite a century of research, the mechanisms underlying short-term or working memory for serial order remain uncertain. Recent theoretical models have converged on a particular account, based on transient associations between independent item and context representations. In the present article, the authors present an alternative model, according to which sequence information is encoded through sustained patterns of activation within a recurrent neural network architecture. As demonstrated through a series of computer simulations, the model provides a parsimonious account for numerous benchmark characteristics of immediate serial recall, including data that have been considered to preclude the application of recurrent neural networks in this domain. Unlike most competing accounts, the model deals naturally with findings concerning the role of background knowledge in serial recall and makes contact with relevant neuroscientific data. Furthermore, the model gives rise to numerous testable predictions that differentiate it from competing theories. Taken together, the results presented indicate that recurrent neural networks may offer a useful framework for understanding short-term memory for serial order.
Botvinick
Matthew M
MM
Department of Psychiatry, University of Pennsylvania, 3720 Walnut Street, Philadelphia, PA 19104, USA. mmb@mail.med.upenn.edu
Plaut
David C
DC
D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D051188:Knowledge Bases; D008570:Memory, Short-Term; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012691:Serial Learning