All-or-none subprocesses in the learning of complex sequences☆

Abstract This paper reports a study designed to investigate whether the all-or-none conception of the learning process can be extended to a learning task more complex than conditioning or simple verbal association. The experimental task is to learn numerical sequences by anticipating each new member of the sequences. Although the obtained sequence learning appears very complex, it proves to be analyzable into constituent all-or-none subprocesses.