Partly in response to the apparent limitations of explicit symbol processing used by traditional artificial intelligence research, there has been, within the last decade, a growing interest in artificial neural networks. This thesis focuses on the development and testing of a model for describing certain kinds of biological phenomena. The many artificial neural networks available may be classified into three types: (1) self-organizing networks, which have input but no feedback; (2) unsupervised networks, requiring minimal feedback (perhaps a signal indicating success or failure); and (3) supervised models, which employ far more extensive (and, I think, biologically implausible) feedback mechanisms. In this thesis I examine only models of the second type. The Rescorla-Wagner trial-level model gives a quantitative description of what happens as a result of a conditioning trial. But that model, along with more detailed, temporal (i.e., intratrial) models, such as a traditional Hebbian model and the Sutton-Barto model, make predictions which are at odds with empirical data. Klopf\u27s drive-reinforcement model is a much more robust account, from which I develop a simplified drive-reinforcement (SDR) model. I prepare a number of experiments to test my SDR model\u27s correspondence with empirical data derived from animal learning experiments; I demonstrate that the model is capable of describing a wide variety of classical conditioning phenome na; and I 6how how the model may form the basis for instrumental conditioning as well. Finally, I add a simple motivating principle (or drive ) and show that such an addition seems to enhance the learning capabilities of the model
