Johns Hopkins University – Homewood Campus – (410-516-5250/office phone)
Thursday, October 27, 2005 - 3:30 p.m.
“Short-term memory for serial order“
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 recent work, we have pursued 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. The model suggests a Bayesian account of serial order memory, which we have begun to work out formally. This account, in turn, gives rise to a number of novel predictions, some of which have been tested empirically. In addition to reviewing this work, I'll also briefly describe related research in which the same computational architecture has been applied to the problem of action sequencing in everyday, object- and goal-directed routines.