Detecting change in stochastic sound sequences

Our ability to parse our acoustic environment relies on the brain’s capacity to extract statistical regularities from surrounding sounds. Previous work in regularity extraction has predominantly focused on the brain’s sensitivity to predictable patterns in sound sequences. However, natural sound environments are rarely completely predictable, often containing some level of randomness, yet the brain is able to effectively interpret its surroundings by extracting useful information from stochastic sounds. It has been previously shown that the brain is sensitive to the marginal lower-order statistics of sound sequences (i.e., mean and variance). In this work, we investigate the brain’s sensitivity to higher-order statistics describing temporal dependencies between sound events through a series of change detection experiments, where listeners are asked to detect changes in randomness in the pitch of tone sequences. Behavioral data indicate listeners collect statistical estimates to process incoming sounds, and a perceptual model based on Bayesian inference shows a capacity in the brain to track higher-order statistics. Further analysis of individual subjects’ behavior indicates an important role of perceptual constraints in listeners’ ability to track these sensory statistics with high fidelity. In addition, the inference model facilitates analysis of neural electroencephalography (EEG) responses, anchoring the analysis relative to the statistics of each stochastic stimulus. This reveals both a deviance response and a change-related disruption in phase of the stimulus-locked response that follow the higher-order statistics. These results shed light on the brain’s ability to process stochastic sound sequences.人类对声学环境的解析能力，依赖于大脑从周遭声响中提取统计规律的功能。过往有关规律提取的研究，大多聚焦于大脑对声音序列中可预测模式的敏感性。然而，自然声学环境极少完全可预测，往往存在一定程度的随机性，但大脑仍可通过从随机（stochastic）声响中提取有效信息，高效解读周遭环境。已有研究表明，大脑对声音序列的边缘低阶统计特征具有敏感性（即均值与方差）。本研究通过一系列变化检测实验，探究大脑对描述声音事件间时序依赖关系的高阶统计特征的敏感性；实验中要求受试者检测音调序列音高维度上的随机性变化。行为数据显示，受试者会通过收集统计估计值来处理输入的声音信号；而基于贝叶斯推理（Bayesian inference）的感知模型则表明，大脑具备追踪高阶统计特征的能力。对个体受试者行为的进一步分析显示，感知约束在受试者高保真追踪这些感官统计特征的能力中发挥着重要作用。此外，该推理模型可辅助神经脑电图（electroencephalography, EEG）响应的分析，将分析锚定至每个随机刺激（stochastic stimulus）的统计特征层面。该分析揭示出，遵循高阶统计特征的刺激锁定响应（stimulus-locked response）中，同时存在偏差响应（deviance response）与变化相关的相位扰动。上述研究结果阐明了大脑处理随机声响序列的能力机制。