This chapter considers how connectionist neural networks offer a contrast to the symbolic view of representation discussed in previous chapters. We start by reviewing the structure of neural networks inspired by neurobiology, comparing a single unit in a neural network to a biological neuron. The second section looks at the simplest form of neural network -- a single-layer neural network using the perceptron convergence rule for learning. The third section introduces multilayer neural networks and the development of the backpropagation algorithm. Next, we look at how the multilayer neural network can be trained, and its biological plausibility. The last section summarizes three critical features of information processing in neural networks, as opposed to physical symbol systems: distributed representations, the lack of a clear distinction between storing and processing information, and the ability to learn.

We examine why science is important to applied psychology, even if one’s motivation to be a psychologist is primarily practical. Helping others takes knowledge and skill, and often applied psychologists face situations that do not produce immediate or clear outcomes. In such situations experiential learning can only do so much, and science is needed to be effective long term. When the history of training models in applied psychology is reviewed from the inception of the field to the present day, it is clear that students of applied psychology need to learn how to do research that will inform practice, how to assimilate the research evidence as it emerges, and how to incorporate empiricism into practice itself. We argue that the kind of knowledge needed by practitioners requires a focus on the needs of those served by psychologists, a more personalized and process-based research approach, and a laser-like focus on issues of broad importance. A scientist-practitioner is a consumer of research, but is also able to identify, acquire, develop, and apply empirically supported treatments and assessments to those in need, and to think about their own work with an empirical mind set.