A computational model of human memory for serial order is described (OSCillator-based Associative
Recall [OSCAR]). In the model, successive list items become associated to successive states of a
dynamic learning-context signal. Retrieval involves reinstatement of the learning context, successive
states of which cue successive recalls. The model provides an integrated account of both item memory
and order memory and allows the hierarchical representation of temporal order information. The model
accounts for a wide range of serial order memory data, including differential item and order memory,
transposition gradients, item similarity effects, the effects of item lag and separation in judgments of
relative and absolute recency, probed serial recall data, distinctiveness effects, grouping effects at various
temporal resolutions, longer term memory for serial order, list length effects, and the effects of
vocabulary size on serial recall.