Global and local aspects of temporal and lexical predictions for speech processing

人类大脑的特点是不断保持对即将发生的事件的感官预测，从而优化其性能。这种预测机制主要在视觉神经科学中得到研究，在某种程度上也在听觉神经科学中得到研究。对于语音理解，特别是对于单片段处理，对预测机制的系统描述在某种程度上被忽视了。为此，本研究项目旨在系统地研究语音中的预测机制，这些预测机制与语音片段、单词和含义的水平有关。重要的是，关于语音事件时间的预测（时间预测）将与关于语音事件词汇特征的预测（特征预测）区分开来。这种区别涉及到语音事件之间的同步在多大程度上有利于处理的问题，另一方面，语言特定属性（如语音出现的频率）在多大程度上影响预测处理。以往的研究要么集中在时间预测上，要么集中在特征预测上，而本项目的7个实验将系统地评估时间预测和特征预测的相互作用，并区分由单词上下文生成的预测和由句子上下文生成的预测。在方法上，所有实验都将基于脑电图（EEG）记录的事件相关脑电位（ERPs）。这保证了对大脑活动的既定测量具有非常好的时间分辨率。此外，先前的研究表明，预测效应发生在早期ERP潜伏期，因此该方法最适合本项目的目的。所有提出的实验的首要问题如下：(a)语音处理中的预测机制与一般听觉处理中的预测机制在多大程度上不同？(b)现有的具有交互处理水平的言语感知模型如何整合这些机制，该项目将对其特征进行细化？

The human brain is characterized by constantly maintaining sensory predictions about upcoming events, thereby optimizing its performance. Such predictive mechanisms have been mainly investigated in the visual, and to some degree, in the auditory neurosciences. For speech comprehension, and in particular, for single-segment processing, a systematic account of predictive mechanisms has somewhat been neglected. To that end, the proposed research project aims at systematically examining predictive mechanisms in speech that pertain to the level of speech segments, words, and meanings. Importantly, predictions regarding the timing of speech events (temporal predictions) will be distinguished from predictions regarding the lexical features of speech events (featural predictions). This distinction relates to the question to what degree synchrony between speech events is beneficial for processing and, on the other hand, to what degree language-specific properties such as frequency of speech sound occurrence influences predictive processing. While previous studies focused on either temporal or featural predictions, the 7 experiments in this project will systematically assess the interaction of temporal and featural predictions, and also distinguish between predictions generated by a word context and predictions generated by a sentence context. Methodologically, all experiments will be based on Event-Related brain Potentials (ERPs), obtained by Electroencephalography (EEG) recordings. This guarantees an established measure of brain activity with a very good temporal resolution. Furthermore, previous studies have shown that predictive effects occur at early ERP latencies, such that this method is optimal for the purpose of this project. The overarching questions for all proposed experiments are the following: (a) To what degree do predictive mechanisms in speech processing differ from predictive mechanisms in general auditory processing? (b) How can existing models of speech perception with interactive levels of processing integrate such mechanisms, the characterization of which shall be refined by this project?