Math Modeling Seminar: An ancient evolutionary calculus for attention signaling retained in modern music
Math Modeling Seminar
An ancient evolutionary calculus for attention signaling retained in modern music
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Dr. Gregory Babbitt
Assistant Professor, School of Life Sciences
Rochester Institute of Technology
Abstract:
The ultimate mechanism(s) underlying the evolution of human music have been the subject of both speculation and rigorous scientific study. However, the proximate mechanism of Darwinian fitness signaled through music has not. As popular music and dance functionally aim to hold an observer’s attention, we investigate features in musical sound that might stimulate attentive behavior in an audience. We propose a calculus of attention to moving sounds and bodies to ascertain their f(x) = location = positional control, f’(x) = energy = change in position, and f”(x) = surprise = change in direction. We developed statistical machine learning software to (A) analyze nine features of sound related to acoustic control, energy, and surprise (CES), (B) map dynamic song trajectories within the multivariate space of CES, and (C) measure how stable performances are within this space (i.e. signaling fitness). When musical sounds are compared to animal vocalizations, human speech, and non-biological sounds including noise, we statistically confirm that music maintains the highest stability in the space of CES. This fitness signal varies across musical genres and is also detected animal calls as well. Fitness signaling also differs significantly between expert versus novice musicians, adult versus immature bird songs, and is constrained by musical performance interactions with live audiences as well. We conclude that fitness signaling via this simple calculus of attention to behavioral display reflects the fundamental physics of observing bodies in motion and probably originated from the selective pressure of ancient intra/interspecific conflict and is still retained in music today.
Bio: Dr. Gregory Babbitt is a computational biologist with a focused interest in the development of modern statistical tools for comparative molecular dynamic simulation applied to the function and evolution of proteins and their interactions with other molecules. He is also interested in the co-evolution of gene regulatory systems and their biophysical implications for the optimization of the genetic code. He is also broadly interested in the evolution of complex and stochastic natural processes across all scales.
Intended Audience: Beginners, undergraduates, graduates. Those with interest in the topic.
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