An attractor network model of serial recall

We present a neural network model of verbal working memory which attempts to illustrate how a few simple assumptions about neural computation can shed light on cognitive phenomena associated with the serial recall of verbal material. We assume that neural representations are distributed, that neural connectivity is massively recurrent, and that synaptic efficacy is modified based on the correlation between pre- and post-synaptic activity (Hebbian learning). Together these assumptions give rise to emergent computational properties that are relevant to working memory, including short-term maintenance of information, time-based decay, and similarity-based interference. We instantiate these principles in a specific model of serial recall and show how it can both simulate and explain a number of standard cognitive phenomena associated with the task, including the effects of serial position, word length, articulatory suppression (and its interaction with word length), and phonological similarity.