AUTHOR=Daikoku Tatsuya 

TITLE=Entropy, Uncertainty, and the Depth of Implicit Knowledge on Musical Creativity: Computational Study of Improvisation in Melody and Rhythm

JOURNAL=Frontiers in Computational Neuroscience

VOLUME=12

YEAR=2018

URL=https://www.frontiersin.org/journals/computational-neuroscience/articles/10.3389/fncom.2018.00097

DOI=10.3389/fncom.2018.00097

ISSN=1662-5188

ABSTRACT=<p>Recent neurophysiological and computational studies have proposed the hypothesis that our brain automatically codes the <italic>n</italic>th-order transitional probabilities (TPs) embedded in sequential phenomena such as music and language (i.e., local statistics in <italic>n</italic>th-order level), grasps the entropy of the TP distribution (i.e., global statistics), and predicts the future state based on the internalized <italic>n</italic>th-order statistical model. This mechanism is called statistical learning (SL). SL is also believed to contribute to the creativity involved in musical improvisation. The present study examines the interactions among local statistics, global statistics, and different levels of orders (mutual information) in musical improvisation interact. Interactions among local statistics, global statistics, and hierarchy were detected in higher-order SL models of pitches, but not lower-order SL models of pitches or SL models of rhythms. These results suggest that the information-theoretical phenomena of local and global statistics in each order may be reflected in improvisational music. The present study proposes novel methodology to evaluate musical creativity associated with SL based on information theory.</p>