A Model of the Phonological Loop: Generalization and Binding

We present a neural network model that shows how the prefrontal cortex, interacting with the basal ganglia, can maintain a sequence of phonological information in activation-based working memory (i.e., the phonological loop). The primary function of this phonological may be to transiently encode arbitrary bindings ofloop information necessary for tasks - the combinatorial expressive power of language enables very flexible binding of essentially arbitrary pieces of information. Our model takes advantage of the closed-class nature of phonemes, which allows different neural representations of all possible phonemes at each sequential position to be encoded. To make this work, we suggest that the basal ganglia update signal that allocates phonemes toprovide a region-specific the appropriate sequential coding slot. To demonstrate that flexible, arbitrary binding of novel sequences can be supported by this we show that the model can generalize to novel sequencesmechanism, after moderate amounts of training.