Johns Hopkins University, Baltimore, Maryland 21218.
Johns Hopkins University, Baltimore, Maryland 21218
J Neurosci. 2021 Aug 4;41(31):6726-6739. doi: 10.1523/JNEUROSCI.1887-20.2021. Epub 2021 Jun 30.
The human brain extracts statistical regularities embedded in real-world scenes to sift through the complexity stemming from changing dynamics and entwined uncertainty along multiple perceptual dimensions (e.g., pitch, timbre, location). While there is evidence that sensory dynamics along different auditory dimensions are tracked independently by separate cortical networks, how these statistics are integrated to give rise to unified objects remains unknown, particularly in dynamic scenes that lack conspicuous coupling between features. Using tone sequences with stochastic regularities along spectral and spatial dimensions, this study examines behavioral and electrophysiological responses from human listeners (male and female) to changing statistics in auditory sequences and uses a computational model of predictive Bayesian inference to formulate multiple hypotheses for statistical integration across features. Neural responses reveal multiplexed brain responses reflecting both local statistics along individual features in frontocentral networks, together with global (object-level) processing in centroparietal networks. Independent tracking of local surprisal along each acoustic feature reveals linear modulation of neural responses, while global melody-level statistics follow a nonlinear integration of statistical beliefs across features to guide perception. Near identical results are obtained in separate experiments along spectral and spatial acoustic dimensions, suggesting a common mechanism for statistical inference in the brain. Potential variations in statistical integration strategies and memory deployment shed light on individual variability between listeners in terms of behavioral efficacy and fidelity of neural encoding of stochastic change in acoustic sequences. The world around us is complex and ever changing: in everyday listening, sound sources evolve along multiple dimensions, such as pitch, timbre, and spatial location, and they exhibit emergent statistical properties that change over time. In the face of this complexity, the brain builds an internal representation of the external world by collecting statistics from the sensory input along multiple dimensions. Using a Bayesian predictive inference model, this work considers alternative hypotheses for how statistics are combined across sensory dimensions. Behavioral and neural responses from human listeners show the brain multiplexes two representations, where local statistics along each feature linearly affect neural responses, and global statistics nonlinearly combine statistical beliefs across dimensions to shape perception of stochastic auditory sequences.
人类大脑从不断变化的动态和多感知维度（例如音高、音色、位置）交织的不确定性中提取现实场景中嵌入的统计规律，以筛选复杂性。虽然有证据表明，不同听觉维度的感觉动态是由独立的皮质网络跟踪的，但这些统计数据如何整合以产生统一的对象仍然未知，特别是在缺乏特征之间明显耦合的动态场景中。本研究使用具有频谱和空间维度随机规律的音调序列，研究了人类听众（男性和女性）对听觉序列中变化统计数据的行为和电生理反应，并使用预测贝叶斯推理的计算模型来制定多个特征跨特征统计整合的假设。神经反应揭示了反映额中央网络中单个特征局部统计的多路复用大脑反应，以及中央顶网络中的全局（对象级）处理。每个声学特征的局部惊讶的独立跟踪揭示了神经反应的线性调制，而全局旋律级统计数据则遵循特征之间统计置信度的非线性整合，以指导感知。在沿频谱和空间声学维度的单独实验中获得了几乎相同的结果，这表明大脑中存在用于统计推断的共同机制。统计整合策略和记忆部署的潜在变化揭示了听众之间在行为功效和对随机变化的神经编码的保真度方面的个体差异。我们周围的世界是复杂且不断变化的：在日常听力中，声源沿着多个维度（例如音高、音色和空间位置）演变，并且它们表现出随时间变化的突发统计属性。面对这种复杂性，大脑通过沿着多个维度从感觉输入中收集统计信息来构建外部世界的内部表示。使用贝叶斯预测推理模型，这项工作考虑了关于统计数据如何跨感觉维度组合的替代假设。人类听众的行为和神经反应表明，大脑多路复用两种表示形式，其中每个特征的局部统计线性影响神经反应，而全局统计非线性地跨维度组合统计置信度以形成对随机听觉序列的感知。
