The ArgumentDespite the enormous historical attention psychoanalysis has attracted, its popularization in Britain (as opposed to the United States) in the wake of the Great War has been largely overlooked. The present paper explores the sources and fate of the sudden “craze” for psychoanalysis after 1918, examining the content of the books through which the doctrine became widely known, along with the roles played by religious interests and the popular press. The percolation of Freudian and related language into everyday English (...) was effectively complete by the 1930s. Crucially, it is argued that in Britain the character of psychoanalytic theory itself demonstrably converged with the psychological needs of the British population in the postwar period. The situation in Britain was clearly different in many respects from that in the United States. This episode bears on numerous questions about scientific popularization, the distinctiveness of British psychoanalysis, and though it is treated here only peripherally the epistemological status or nature of psychoanalysis. More generally the present paper may be read as an exercise in reflexive disciplinary historiography, in which the levels of discipline (“Psychology”) and subject matter (“psychology”) are viewed as interpenetrating and mutually constitutive. (shrink)

Within the discipline of psychology, the conventional history outlines the development of two fundamental approaches to the scientific study of emotion—“basic emotion” and “appraisal” traditions. In this article, we outline the development of a third approach to emotion that exists in the psychological literature—the “psychological constructionist” tradition. In the process, we discuss a number of works that have virtually disappeared from the citation trail in psychological discussions of emotion. We also correct some misconceptions about early sources, such as work by (...) Darwin and James. Taken together, these three contributions make for a fuller and more accurate account of ideas about emotion during the century stretching from 1855 to just before 1960. (shrink)

Learning general concepts in imperfect environments is difficult since training instances often include noisy data, inconclusive data, incomplete data, unknown attributes, unknown attribute values and other barriers to effective learning. It is well known that people can learn effectively in imperfect environments, and can manage to process very large amounts of data. Imitating human learning behavior therefore provides a useful model for machine learning in real-world applications. This paper proposes a new, more effective way to represent imperfect training instances and (...) rules, and based on the new representation, a Human-Like Learning (HULL) algorithm for incrementally learning concepts well in imperfect training environments. Several examples are given to make the algorithm clearer. Finally, experimental results are presented that show the proposed learning algorithm works well in imperfect learning environments. (shrink)