Several theories of learning have been proposed to account for the acquisition of abstract, generative knowledge including schema theory, analogical learning and implicit learning. However, past research has not compared these three theories directly. In the present studies we instantiated each theory as a learning scenario (i.e., direct instruction, analogy training and implicit training) and then tested all three training groups on a common problem. Results show that the analogy training groups and one of the direct instruction groups performed significantly better than the other groups on problem solving performance. The findings are interpreted in terms of opportunity to practice generating a response of the relevant type. Theories of Deep Learning In order to solve complex, novel problems one must be able to retrieve previously learned information from memory and apply it to the current situation. For instance, students learning geometry need to be able to apply mathematical formulas acquired during study to novel problems encountered at test. Although surface features of the problems change (e.g., specific values: a=5 to a=15) the abstract operators used to solve the problems stay the same (e.g., the Pythagorean theorem: a 2 + b 2 = c 2). Thus, in order for the knowledge gained from study to be helpful on the test it must be both abstract and generative. How such deep knowledge is acquired remains a central question for researchers in psychology, philosophy and education. Several theoretical explanations have been proposed as to the origin of such abstract, generative knowledge