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Simulating German verb inflection with a constructivist neural network

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review



Taking seriously neurobiological and psychological evidence on the constructivist, experience dependent nature of brain development, we present a constructivist neural network model which builds its architecture in response to the task of learning German (past participle) inflection. Our model captures developmental profiles, as well as healthy and impaired adult performance, because two complementary processing pathways develop from the interaction of the constructivist learning mechanism and the distributional properties of the inflectional paradigm. Instead of a regular/irregular dichotomy it suggests an emergent dissociation between verbs that are easy or hard to learn, thus obviating the need for in-built assumptions such as verb type specific processing mechanisms or knowledge of grammatical class. We focus on the German participle in order to demonstrate that the performance of the model, though based on associative learning mechanisms, does not depend on the existence of a dominant 'default' class, as has been claimed by proponents of the dual-mechanism camp within the continuing past tense debate.