Proceedings of the Annual Meeting of the Cognitive Science Society

Studies of human expertise suggest that experts and novices “see“ problems differently. Experts not only acquire a bodyof domain-specific strategies and knowledge, but also learn to quickly identify when those concepts apply to problemswithin the domain. We propose modeling these elements as an iterative process of domain-specific language (DSL)learning, while jointly training a neural network to recognize when learned concepts apply to new problems. We showthat the algorithm solves problems more accurately and quickly than either a neural network alone, or a model that simplyacquires new concepts without learning when to use them. We also examine the implicit problem representations learnedby the neural network recognition model, and find that they increasingly come to reflect abstract relationships betweenproblems, rather than surface features, as the model acquires domain expertise. A full paper and additional details areavailable at: https://sites.google.com/view/neurally-guided-expertise-mit