Ambiguity resolution in a Neural Blackboard Architecture for sentence structure

We simulate two examples of ambiguity resolution found in human language processing in a neural blackboard architecture for sentence representation and processing. The architecture also accounts for a related garden path effect. The architecture represents and processes sentences in terms of neuronal assemblies, related to the words and the structure of the sentence. The assemblies are simulated as Wilson-Cowan neuronal populations. During sentence processing predictions are generated in the architecture about the remaining structure of the sentence. In the course of processing, the resulting sentence (structure) and word representations in the architecture interact in a dynamical competition. These interactions produce the language effects simulated here. The characteristics of the architecture reveal how forms of higher level symbol-like cognitive processing could be implemented in a neuronal manner.