title: Learning melodies from non-adjacent tones.
abstract: Language acquisition might heavily rely on statistical learning mechanisms. This has led many researchers to investigate the computational constraints that limit such learning. In particular, it has been argued that statistical relations among non-adjacent items cannot be tracked, as this might lead to a "computational explosion" making statistical learning intractable. In line with this view, previous research suggests that listeners cannot track relations among non-adjacent musical tones (Creel, Newport, & Aslin, 2004). Here I show that participants readily track non-adjacent tone relations when these are implemented in a musically meaningful way. Specifically, participants readily track non-adjacent tone relations in tonal melodies, but find it more difficult to track non-adjacent tone relations in random melodies, suggesting that non-adjacent relations are easier to track when listeners face "ecological", musically meaningful stimuli.
authors.affiliation: Massachusetts Institute of Technology, Cambridge, MA 02139, USA. ansgar.endress@m4x.org
authors.forename: Ansgar D
authors.initials: AD
authors.lastname: Endress
mesh_terms: D000293:Adolescent; D000328:Adult; D005260:Female; D006801:Humans; D007558:Italy; D007858:Learning; D008297:Male; D009146:Music; D046709:Pattern Recognition, Physiological; D011336:Probability; D014481:United States