Zhao Sijia, Skerritt-Davis Benjamin, Elhilali Mounya, Dick Frederic, Chait Maria
Ear Institute, University College London, London WC1X 8EE, UK; Department of Experimental Psychology, University of Oxford, Oxford OX2 6GG, UK.
Applied Physics Laboratory, Johns Hopkins University, Laurel, MD 20723, United States.
Prog Neurobiol. 2025 Jan;244:102696. doi: 10.1016/j.pneurobio.2024.102696. Epub 2024 Dec 6.
How does the brain track and process rapidly changing sensory information? Current computational accounts suggest that our sensations and decisions arise from the intricate interplay between bottom-up sensory signals and constantly changing expectations regarding the statistics of the surrounding world. A significant focus of recent research is determining which statistical properties are tracked by the brain as it monitors the rapid progression of sensory information. Here, by combining EEG (three experiments N ≥ 22 each) and computational modelling, we examined how the brain processes rapid and stochastic sound sequences that simulate key aspects of dynamic sensory environments. Passively listening participants were exposed to structured tone-pip arrangements that contained transitions between a range of stochastic patterns. Predictions were guided by a Bayesian predictive inference model. We demonstrate that listeners automatically track the statistics of unfolding sounds, even when these are irrelevant to behaviour. Transitions between sequence patterns drove a shift in the sustained EEG response. This was observed to a range of distributional statistics, and even in situations where behavioural detection of these transitions was at floor. These observations suggest that the modulation of the EEG sustained response reflects a process of belief updating within the brain. By establishing a connection between the outputs of the computational model and the observed brain responses, we demonstrate that the dynamics of these transition-related responses align with the tracking of "precision" - the confidence or reliability assigned to a predicted sensory signal - shedding light on the intricate interplay between the brain's statistical tracking mechanisms and its response dynamics.
大脑如何追踪和处理快速变化的感官信息？当前的计算模型表明，我们的感觉和决策源于自下而上的感官信号与对周围世界统计信息不断变化的预期之间的复杂相互作用。近期研究的一个重要重点是确定大脑在监测感官信息的快速进展时会追踪哪些统计特性。在这里，我们通过结合脑电图（三个实验，每个实验N≥22）和计算建模，研究了大脑如何处理模拟动态感官环境关键方面的快速且随机的声音序列。被动聆听的参与者接触到包含一系列随机模式之间转换的结构化音调序列。预测由贝叶斯预测推理模型指导。我们证明，即使这些声音与行为无关，听众也会自动追踪正在展开的声音的统计信息。序列模式之间的转换导致了脑电图持续反应的转变。在一系列分布统计中都观察到了这种情况，甚至在对这些转换的行为检测处于最低水平的情况下也是如此。这些观察结果表明，脑电图持续反应的调制反映了大脑内部信念更新的过程。通过在计算模型的输出与观察到的大脑反应之间建立联系，我们证明这些与转换相关的反应的动态与“精度”的追踪一致——“精度”是指赋予预测感官信号的置信度或可靠性——这揭示了大脑统计追踪机制与其反应动态之间的复杂相互作用。
