Did John B. Watson Really “Found” Behaviorism?

Developments culminating in the nineteenth century, along with the predictable collapse of introspective psychology, meant that the rise of behavioral psychology was inevitable. In 1913, John B. Watson was an established scientist with impeccable credentials who acted as a strong and combative promoter of a natural science approach to psychology when just such an advocate was needed. He never claimed to have founded “behavior psychology” and, despite the acclaim and criticism attending his portrayal as the original behaviorist, he was more an exemplar of a movement than a founder. Many influential writers had already characterized psychology, including so-called mental activity, as behavior, offered many applications, and rejected metaphysical dualism. Among others, William Carpenter, Alexander Bain, and (early) Sigmund Freud held views compatible with twentieth-century behaviorism. Thus, though Watson was the first to argue specifically for psychology as a natural science, behaviorism in both theory and practice had clear roots long before 1913. If behaviorism really needs a “founder,” Edward Thorndike might seem more deserving, because of his great influence and promotion of an objective psychology, but he was not a true behaviorist for several important reasons. Watson deserves the fame he has received, since he first made a strong case for a natural science (behaviorist) approach and, importantly, he made people pay attention to it.     

Bayesian evaluation of informative hypotheses for multiple populations

The software package Bain can be used for the evaluation of informative hypotheses with respect to the parameters of a wide range of statistical models. For pairs of hypotheses the support in the data is quantified using the approximate adjusted fractional Bayes factor (BF). Currently, the data have to come from one population or have to consist of samples of equal size obtained from multiple populations. If samples of unequal size are obtained from multiple populations, the BF can be shown to be inconsistent. This paper examines how the approach implemented in Bain can be generalized such that multiple-population data can properly be processed. The resulting multiple-population approximate adjusted fractional Bayes factor is implemented in the R package Bain.