Serial learning: Identification of subjective subsequences

Learning of fixed arbitrary sequences proceeds by idiosyncratic subsequencing and assembly of the resulting subsequences. An error item that just precedes a subjective subsequence is a closely constrained member of that subsequence. Identification and characterization of subjective subsequences were accomplished by analyses of forward serial learning, free recall, and backward serial learning. We conclude that ordinary serial learning cannot be represented by traditional continuous-process theories, but instead must be treated by an organizational model.