Bayesian Semiparametric Mixed Effects Markov Models With Application to Vocalization Syntax

Studying the neurological, genetic, and evolutionary basis of human vocal communication mechanisms using animal vocalization models is an important field of neuroscience. The datasets typically comprise structured sequences of syllables or “songs” produced by animals from different genotypes under different social contexts. It has been difficult to come up with sophisticated statistical methods that appropriately model animal vocal communication syntax. We address this need by developing a novel Bayesian semiparametric framework for inference in such datasets. Our approach is built on a novel class of mixed effects Markov transition models for the songs that accommodate exogenous influences of genotype and context as well as animal-specific heterogeneity. Crucial advantages of the proposed approach include its ability to provide insights into key scientific queries related to global and local influences of the exogenous predictors on the transition dynamics via automated tests of hypotheses. The methodology is illustrated using simulation experiments and the aforementioned motivating application in neuroscience. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.利用动物发声模型研究人类发声交流机制的神经、遗传与进化基础，是神经科学的重要研究领域。该类数据集通常包含来自不同基因型的动物在不同社会情境下发出的结构化音节序列或‘鸣唱’内容。过往学界始终难以开发出能够恰当拟合动物发声交流句法的高阶统计方法。为此，我们针对此类数据集开发了全新的贝叶斯半参数推理框架。我们的方法基于一类针对鸣唱数据的新型混合效应马尔可夫转移模型，该模型可同时纳入基因型与社会情境的外源性影响，以及动物个体的特异性异质性。所提方法的核心优势在于，可通过自动化假设检验，解析外源性预测变量对转移动态的全局与局部影响，从而为关键科学问题提供研究视角。我们通过模拟实验与前述作为研究动机的神经科学应用场景，对该方法进行了演示验证。本文的补充材料（包含可用于复现研究的标准化材料说明）可在线获取。