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Previous research has indicated that breaking a task into subtasks can both facilitate and interfere with learning in neural networks.  Although these results appear to be contradictory, they actually reflect some underlying principles governing learning in neural networks.  Using the cascade-correlation learning algorithm, we devised a concept learning task that would let us specify the conditions under which subtasking would facilitate or interfere with learning. The results indicated that subtasking facilitated learning when the initial subtask involved learning a function compatible with that characterizing the rest of the task, and inhibited learning when the initial subtask involved a function incompatible with the rest of the task.  These results were then discussed with regard to their implications for understanding the effect of knowledge on concept learning. 


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