ABSTRACT
Current adaptive network, or “connectionist,” theories of human learning are reminiscent of statistical learning theories of the 1950s and early 1960s, the most influential of which was Stimulus Sampling Theory, developed by W. K. Estes and colleagues (Atkinson & Estes, 1963; Estes, 1959). Both Stimulus Sampling Theory and adaptive network theory are general classes of learning theories— formal frameworks within which theorists search for a small number of concepts and principles that will illuminate a wide variety of psychological phenomena when applied in varying combinations. To the extent that adaptive networks represent cumulative progress in theory development, we should expect them to incorporate the strengths of Stimulus Sampling Theory but overcome the problems that limited these earlier approaches to modeling associative learning.
