This book extends the application of the neural network models described in my previous book on “Animal learning and cognition: A neural network approach” to a whole range of important classical conditioning paradigms, including recovery from overshadowing, recovery from blocking, backward blocking and recovery from backward blocking; extinction, and occasion setting, as well as the neurophysiology of some of those phenomena.

In the last decades, models of conditioning have shown increasing completeness and precision. This book describes a number of computational mechanisms (associations, attention, configuration, and timing) that first seemed necessary to explain a small number of conditioning results and then proved able to account for a large part of the extensive body of conditioning data. These computational mechanisms are implemented by artificial neural networks, which can be mapped onto different brain structures. Therefore, the approach permits to establish clear brain-behavior relationships.

The book is organized as follows. Part I presents major classical conditioning data and describes several theories proposed to explain them. Part II presents a neural network theory, which includes attentional and associative mechanisms, and applies it to the description of conditioning, latent inhibition, overshadowing and blocking, extinction, and creative processes. In addition, it examines the neurobiological bases of latent inhibition and extinction. Part III describes another neural network, which includes configural mechanisms, and applies it to the description of occasion setting. In addition, it examines the neurobiological bases of occasion setting.