Abstract
A description is given of a ‘back-tracking’ approach that could be used to model neural language development and language evolution. This approach aims to develop a neural model of human language capacity that incorporates important constraints on language structure, language comprehension and performance, and brain structure. The model is then to be used as a target for development or evolution. To this end, the target model would have to be simplified, so that the target model can be derived from the simplified version and learning algorithms (or structural changes). The benefit of this approach is that the development of important constraints such as the combinatorial productivity of human language is ensured. The paper illustrates the importance of including this constraint in models of language development and evolution. It then describes a neural model in which this constraint is satisfied. Finally, the paper describes how such a model could be used to investigate language development and/or evolution.
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