Brain signal variability as a window into the bidirectionality between music and language processing: moving from a linear to a nonlinear model

• Published in: Frontiers in psychology 2013-12, Vol.4, p.984-984
• Main Authors: Hutka, Stefanie, Bidelman, Gavin M, Moreno, Sylvain
• Format: Article
• Language: English
• Subjects: brain signal variability; musical training; Nonlinear dynamical systems; Psychology; Tone Language; transfer effects
• ISSN: 1664-1078; 1664-1078
• DOI: 10.3389/fpsyg.2013.00984

There is convincing empirical evidence for bidirectional transfer between music and language, such that experience in either domain can improve mental processes required by the other. This music-language relationship has been studied using linear models (e.g., comparing mean neural activity) that conceptualize brain activity as a static entity. The linear approach limits how we can understand the brain's processing of music and language because the brain is a nonlinear system. Furthermore, there is evidence that the networks supporting music and language processing interact in a nonlinear manner. We therefore posit that the neural processing and transfer between the domains of language and music are best viewed through the lens of a nonlinear framework. Nonlinear analysis of neurophysiological activity may yield new insight into the commonalities, differences, and bidirectionality between these two cognitive domains not measurable in the local output of a cortical patch. We thus propose a novel application of brain signal variability (BSV) analysis, based on mutual information and signal entropy, to better understand the bidirectionality of music-to-language transfer in the context of a nonlinear framework. This approach will extend current methods by offering a nuanced, network-level understanding of the brain complexity involved in music-language transfer.