THE UTILITY OF CONNECTIONISM FOR MOTOR LEARNING - A REINTERPRETATION OF CONTEXTUAL INTERFERENCE IN MOVEMENT SCHEMAS

Using the general framework of schema theory, and building on it, the present article takes a connectionist approach to motor learning and to contextual interference effects. These phenomena were simulated in an exploratory manner in neural networks. The outcome closely reflects previous research with humans. In a simulated ballistic movement task, networks performed worse during practice but showed better transfer when target movement distances were presented in a random rather than a blocked fashion. Connectionism provides a parsimonious account of the effect in terms of properties inherent in the parallel distributed network.