To ensure we offer contemporary continuing education opportunities, the CE credit associated with this video is no longer available, however, the video remains available for viewing.
First presentation: Skinner's famous article, "Are Theories of Learning Necessary?" was provocative and elliptical. The answer to his title becomes obvious when the ellipses are resolved: ... Necessary for a Science of Behavior? His own life work, in particular his three-term contingency, provided an important start on such a theory. In this talk, Skinner's work is embedded in the Aristotlean causal framework that stipulates the four questions we must answer for a complete explanation—for a complete theory—of a phenomenon; and the fifth that is entailed in the case of a dynamic process such as learning. Skinner provided two of those answers, and repudiated the others as wrong because they are extra-dimensional. This talk reasserts those questions and discusses the techniques that may be used to determine the adequacy with which provisional answers to the questions accord with the phenomenon under study. The approach is exemplified with the work of some of the participants of this conference.
Second presentation: A complexity theory is stated as a set of relatively simple rules that operate repeatedly to generate an output. The output, which typically exhibits properties and features that are not immediately derivable from the rules themselves, is then compared to the natural phenomena the theory purports to explain. At least four instances of complexity theory have appeared in behavior analysis. These are Charles P. Shimp's theory of momentary maximizing, John Donahoe and colleagues' theory of neural networks, A. Charles Catantia's theory of the reflex reserve, and Jack McDowell's theory of behavioral evolution. Each theory generates behavior by means of the rules it implements. If the generated behavior is comparable to the behavior of live organisms, then what is one warranted in asserting about the rules that constitute the theory? For example, is it important that they correspond to events and processes in the natural world? What is the nature of a theory that generates output that is consistent with observation, but that consists of rules that do not correspond, or cannot be determined to correspond, to events in the natural world? Answers to these questions may lead to a more or less maintainable distinction between a model and a theory, and to a profitable discussion of the relative value of these two types of account.