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Enhancing concept learning

We are interested in developing methods to improve human learning and performance on challenging visual categorization tasks, e.g., bird species identification, diagnostic dermatology. Our approach involves inferring psychological embeddings--internal representations that individuals use to reason about a domain. Using predictive cognitive models that operate on an embedding, we perform surrogate-based optimization to determine efficient and effective means of training domain novices as well as amplifying an individual's capabilities at any stage of training. Our cognitive models leverage psychological theories of similarity judgement and generalization, contextual and sequential effects in choice, and the relationship between the structure of the embedding and attention. Rather than searching over all possible training policies, we focus our search on policy spaces motivated by the literature, including manipulation of exemplar difficulty and the sequencing of category labels. We show that our models predict human behavior not only in the aggregate but at the level of individual learners and individual exemplars, and preliminary experiments show the benefits of surrogate-based optimization on learning and performance.


Brett Roads (Computer Science, Colorado)
Spencer Hanson (Computer Science, Colorado)


Hal Pashler (Psychology, UCSD)
James Tanaka (Psychology, U Victoria)