Bridging machine classification and human cognition: discriminative vs. generative learning

Classification, one of the most widely studied problems in machine learning, is also a central research topic in human cognitive psychology. So far these two parallel fields have mostly developed in isolation. Our research bridges machine learning with human psychology research by investigating discriminative vs. generative learning in humans. The distinction between discriminative and generative approaches is much studied in machine learning, but has not been examined in human learning. Discriminative learners find a direct mapping between inputs and class labels whereas generative learners model a joint distribution between inputs and labels. These approaches often result in classification differences. Our preliminary work indicated that humans can be prompted to adopt discriminative or generative approaches to learning the same dataset. In this work we conducted experiments in which we measured learning curves of humans who are trained on datasets under discriminative vs. generative learning conditions. We used datasets which have been previously used as machine learning benchmarks and also datasets of brain imaging scans used for medical diagnosis. Humans still outperform the most powerful computers in many tasks, such as learning from small amounts of data and comprehending language. Thus, insights from human learning have great potential to inform machine learning. An understanding of how humans solve the classification problem will be instructive for machine learning in several ways: for the many situations where humans still outperform computers, human results can set benchmarks for machine learning challenges. Additionally, understanding human learning approaches can help give direction to the machine learning approaches that will have the most potential. Finally, in many situations we want machines to behave like humans in order to facilitate human computer interactions. An understanding of human cognition is important for developing machines that think like humans.