Any successful attempt at explaining and replicating the complexity and generality of human and animal learning will require the integration of a variety of learning mechanisms. Here we introduce a computational model which integrates associative learning and reinforcement learning. We contrast the integrated model with associative learning and reinforcement learning models in two simulation studies. The first simulation demonstrates performance advantages for the integrated model in an environment with a dynamic and complex reward structure. The second simulation contrasts the performances of the three models in a classic latent learning experiment (Blodgett, 1929), demonstrating advantages for the integrated model in predicting and explaining the behavioral data.