Observing contingencies: taxonomy and methods

The DSM series has been extremely popular despite the fact that it is largely an atheoretical descriptive system devoid of etiological significance and therapeutic implication. A diagnostic system based on causal variables is preferred because it carries treatment implications. Reinforcement contingencies have long been known to exert large clinically important behavioral effects. Neural network learning theory augments behavioral explanations of normal and disordered behavior and behavior modification therapies based on reinforcement contingencies. The first part of this article presents an expanded version of a contingency taxonomy, first published by Woods in 1974, capable of organizing information about reinforcement contingencies obtained from clinical interview, direct behavioral observation, and/or audio or videotaped behavioral samples. Behavioral diagnoses made in terms of this taxonomy have etiological implications and lead to clear behavioral prescriptions. The second portion of this article discusses efficient methods for observing contingencies, thereby increasing the clinical accessibility of this approach.