An integrated neural network-event-related potentials model of temporal and probability context effects on event categorization

The authors present a neural network that adapts and integrates several preexisting or new modules to categorize events in short-term memory (STM), encode temporal order in working memory, and evaluate timing and probability context in medium- and long-term memory. The model shows how processed contextual information modulates event recognition and categorization, focal attention, and incentive motivation. The model is based on a compendium of event related potentials (ERPs) and behavioral results either collected by the authors or compiled from the classical ERP literature. Its hallmark is, at the functional level, the interplay of memory registers endowed with widely different dynamical ranges, and at the structural level, the attempt to relate the different modules to known anatomical structures. Simulation results are presented