Abstract

Explanations of psychological regularities in terms of biological regularities are undoubtedly appealing for many reasons. In addition, the scientific methodology that searches for such explanations certainly has merit. Nonetheless, the history of neuroscience, psychology, and computer science over the last one hundred years, indicates that such explanations are difficult to find and that the methodology of searching for them often frustrating. Recent attempts to provide "neurally-inspired" explanations of psychological regularities embodied in the theory of parallel distributed processing, PDP, also show signs of these difficulties. The more neurobiologically accurate portions of PDP are isolated from the more psychologically plausible portions, while the portions dedicated to computational analyses of neural networks tend to be isolated from both biology and psychology. Since, however, we may be sure that some day there will be some true scientific and philosophic theory of the role of neurons in mentation, we may press on in the enterprise, using what we have now as a point of departure. In this spirit, we may consider evolutionary explanations of features of our neural hardware. By pursuing evolutionary explanations via computer simulations, genetic algorithms, we have a new direction in which to proceed in our neural network theorizing. A second project we pursue is the application of a neural network studied in PDP computer science, the Boltzmann machine, to psychological phenomena. Here the psychological phenomena are those found in human decision making under risk and captured in Kahneman and Tversky's prospect theory