Proceedings of the Annual Meeting of the Cognitive Science Society

Research in cognitive science and machine learning suggests that learning systems can use small subsets of valuabletraining items in order to quickly learn to achieve good task performance. We hypothesize that people often use smallsubsets of stored exemplars to quickly train new neural networks, called extemporaneous networks, when faced with tasksfor which they do not currently have dedicated networks. We explore this hypothesis using participants’ responses in abehavioral experiment to identify easy versus difficult training items. We find that a network confidence measure indicatesa network trained with a small set of good items provides the best account of participants’ reaction times. Furthermore,computer simulations demonstrate that learning systems can achieve good performance when trained with small sets ofeasy exemplars. Our results indicate that humans may complete tasks using extemporaneously-created networks trainedinternally on small datasets.