Grammar types in language explain tone sequence processing in music

Abstract

In this ERP study, linear and center-embedded musical sequences are built according to two artificial grammar types in language, named finite state grammar (FSG) and phrase structure grammar (PSG). The aim is to prove if neural sources and processing mechanisms for artificial grammar settings across domains are the same. Isochronous pitch sequences were constructed by two interval categories (3rd and 6th) in upward and downward direction. FSG sequences, which have the general form ABAB in artificial grammar, are translated into “small up/small down/large up/large down”. PSG sequences of form A[AB]B are transposed to “small up/large up/large down/small down”. In two ERP recordings testing FSG and PSG separately, non-musicians had to distinguish between correct and false examples after getting familiar with each grammar type. Deviant sequences either include an item of reverse interval or contour. Our main results are: (1) N1 components indicate a 2-item-chunking in FSG and a 4-item-chunking in PSG based on immediate repetition between adjacent tones, thus low-level grouping is different for each grammar type. (2) A late processing negativity at sequence offset indicates syntax-based integration-and-memory processes primarily for PSG. The partially congruent ERP results for artificial grammar learning in language and music confirm that the linguistic perspective on music may be justified.