Multi-feature predictive processing of stochastic sounds in the human auditory system

人类听觉系统中随机声音的多特征预测处理

• 批准号: 9759446
• 负责人: Benjamin Skerritt-Davis
• 金额: $ 4.5万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-12-18 至 2020-12-17
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9759446
• 关键词: Acoustics; Affect; Agreement; Auditory; Auditory Perception; Auditory system; Behavior; Behavioral; Brain; Complex; Computer Simulation; Data; Dependence; Detection; Diagnostic; Dimensions; Electroencephalography; Entropy; Environment; Exhibits; Future; Human; Individual; Individual Differences; Joints; Laboratories; Lead; Location; Loudness; Measures; Memory; Modeling; Nature; Output; Participant; Pattern; Perception; Performance; Process; Property; Sensory; Series; Short-Term Memory; Source; Stimulus; Structure; System; Testing; Time; Uncertainty; Work; deviant; experimental study; follow-up; high dimensionality; improved; individualized medicine; insight; normal hearing; predictive modeling; response; sensory input; sound; statistics; theories; tool

Project Summary/Abstract Normal-hearing listeners are able to effortlessly interpret their acoustic surroundings, parsing ongoing sound into distinct sources and tracking these sources through time. To perform this task, the brain represents relevant sensory information in memory to be compared to future inputs, but the nature of this memory representation is not well understood. Typically, this mechanism is studied using predictable patterns in sound sequences, where listeners are asked to detect deviants from established patterns. Previous work has demonstrated the brain is sensitive to a wide variety of patterns in sound along multiple acoustic dimensions. These patterns, however, do not probe how the brain represents sound in natural listening environments, where relevant information is often not predictable and cannot be represented explicitly and with certainty. This project uses stochastic sound sequences—which exhibit statistical properties rather than deterministic patterns—to investigate the extent to which the brain represents statistical information from sequences of sounds in the presence of uncertainty. Our central hypothesis is that the brain collects high-dimensional statistical information (beyond mean and variance) to capture uncertainty across time and across perceptual features to interpret ongoing sound. In a series of change detection experiments, listeners will be asked to detect changes in the entropy of sound sequences varying along multiple perceptual features: pitch, timbre, and spatial location. A computational model for predictive processing will be developed to compare alternative representations of statistical information in the brain. Perceptual constraints in the model will be fit to individual behavior, and the fitted model will be used to predict deviance responses in Electroencephalography (EEG) data. Additionally, individual differences in perceptual abilities will be measured using a separate task in the same listeners, and these measures will be compared to findings from the model to add interpretative heft and improve the model. A computational model for how the brain processes complex sounds will open the possibility of investigating more natural, “messy” stimuli in the laboratory, and a better understanding of the individual differences in perception of stochastic sounds could lead to better diagnostic tools for assessing temporal processing abilities.

项目总结/摘要 听力正常的听众能够毫不费力地解释他们的声学环境， 声音分成不同的来源，并通过时间跟踪这些来源。为了完成这项任务，大脑 代表记忆中相关的感官信息，与未来的输入进行比较，但 这种存储器表示不是很好理解。通常，使用可预测的方法来研究这种机制 声音序列中的模式，要求听众从既定模式中发现偏差。 先前的研究已经证明，大脑对声音沿着的各种模式都很敏感 多个声学维度然而，这些模式并没有探索大脑如何在大脑中代表声音。 在自然的收听环境中，相关信息往往是不可预测的， 明确而确定地表达出来。这个项目使用随机声音序列-这表现出 统计特性，而不是确定性的模式，以调查大脑在多大程度上 表示存在不确定性时来自声音序列的统计信息。我们的中央 一个假设是，大脑收集高维统计信息（超出均值和方差） 捕捉时间和感知特征的不确定性，以解释正在进行的声音。在一系列 在变化检测实验中，听众将被要求检测声音熵的变化 序列沿着多个感知特征变化沿着：音高、音色和空间位置。一 预测处理的计算模型将被开发，以比较替代表示 大脑中的统计信息。模型中的感知约束将适合个人 行为，拟合模型将用于预测脑电图中的异常反应 (EEG)数据此外，知觉能力的个体差异将使用单独的 任务在相同的听众，这些措施将比较，从模型的结果，以增加 解释的分量和改进模型。大脑如何处理复杂信息的计算模型 声音将打开研究更自然的可能性，“凌乱”的刺激在实验室里， 更好地理解个体对随机声音的感知差异， 用于评估时间处理能力的诊断工具。