Computational Models of Animal and Human Associative Learning

This chapter provides a selective review of the issues that have dominated computational models of associative learning in recent decades. Associative learning research concerns the simplest and most fundamental processes by which humans and other animals come to predict events in their environment based on past experience. It has far-reaching implications for understanding adaptive and maladaptive human behavior. With a focus on Pavlovian conditioning and adjacent subdisciplines, this chapter explores how the prediction error learning algorithm has shaped understanding of competitive learning, selective attention, stimulus representation, and learning about absent events. A number of alternative computational approaches will be introduced, along with some remaining challenges in the computational modeling of human and animal associative learning.