Toward a Statistical Theory of Learning.

TLDR: The possibility of agreement on a theoretical framework, at least in certain intensively studied areas, may be maximized by defining concepts in terms of experimentally manipulable variables and developing the consequences of assumptions by strict mathematical reasoning as mentioned in this paper.

Abstract: Improved experimental techniques for the study of conditioning and simple discrimination learning enable the present day investigator to obtain data which are sufficiently orderly and reproducible to support exact quantitative predictions of behavior. Analogy with other sciences suggests that full utilization of these techniques in the analysis of learning processes will depend to some extent upon a comparable refinement of theoretical concepts and methods. The necessary interplay between theory and experiment has been hindered, however, by the fact that none of the many current theories of learning commands general agreement among researchers. It seems likely that progress toward a common frame of reference will be slow so long as most theories are built around verbally defined hypothetical constructs which are not susceptible to unequivocal verification. While awaiting resolution of the many apparent disparities among competing theories, it may be advantageous to systematize well established empirical relationships at a peripheral, statistical level of analysis. The possibility of agreement on a theoretical framework, at least in certain intensively studied areas, may be maximized by defining concepts in terms of experimentally manipulable variables, and developing the consequences of assumptions by strict mathematical reasoning. This essay will introduce a series of