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

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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?