On working through: a model from artificial intelligence

Working through is centrally important to clinical psychoanalysis. It is inadequately explained in analytic theory. An artificial intelligence model of the process is proposed. Models of problem solving show that the complexity of necessary computation is an important determinant of how a problem is solved. Not optimal, but only good enough solutions are usually found. The quality of solutions depends on the time and resources available. Generally it is far easier to use existing methods than to develop new approaches. When problems must be solved in an emergency fashion, as in trauma, poor solutions are likely to emerge. In studying the annealing of metals and other complex optimization problems, a process, the Boltzman algorithm, was discovered, which continues the search for better solutions while gradually developing a coherent structure of the overall solution. The algorithm provides a model both for psychoanalytic working through and for the normally ongoing process of psychological development and reworking whose deficiency is characteristic of much psychopathology. Working through in the analytic situation is the reactivation of this normal process, and a good analytic outcome is achieved when the process can continue without the analyst. Properties of the Boltzman algorithm clarify such concepts as "optimal" frustration and anxiety which correspond to working in the area where the stable but not rigid structures emerge in the algorithms operation. These studies are an example of how computer science and artificial intelligence are a potentially rich source for psychoanalytic theory.     

Usefulness of structural Rorschach data in identifying inpatients with depressive symptoms: a preliminary study

To explore the usefulness of various structural Rorschach data in identifying psychiatric inpatients with symptoms of depression, 54 adult inpatients were administered a Rorschach. Results indicated that the Comprehensive System, Rorschach Depression Index did not identify many of these individuals as depressed, but did identify most of the extratensive depressed individuals. Each variable included in the Depression Index and other potential, Rorschach correlations of depression were also investigated. Most of them were found to occur more often among these depressed patients than among nonpatients. Furthermore, adding other variables and using more liberal cutoffs may result in more accurate identification of patients with depressive symptoms.