Predicting speech from a cortical hierarchy of event-based timescales

Lea-Maria Schmitt, Julia Erb, Sarah Tune, Anna Uta Rysop, Gesa Hartwigsen, Jonas Obleser


How can anticipatory neural processes structure the temporal unfolding of context in our natural environment? We here provide evidence for a neural coding scheme that sparsely updates contextual representations at the boundary of events and gives rise to a hierarchical, multi-layered organization of predictive language comprehension. Training artificial neural networks to predict the next word in a story at five stacked timescales and then using model-based functional MRI, we observe a sparse, event-based “surprisal hierarchy”.The hierarchy evolved along a temporo-parietal pathway, with model-based surprisal at longest timescales represented in inferior parietal regions. Along this hierarchy, surprisal at any given timescale gated bottom-up and top-down connectivity to neighbouring timescales. In contrast, surprisal derived from a continuously updated context influenced temporo-parietal activity only at short timescales. Representing context in the form of increasingly coarse events constitutes a network architecture for making predictions that is both computationally efficient and semantically rich.### Competing Interest StatementThe authors have declared no competing interest.
Original languageEnglish
Publication statusPublished - 12.2020

Research Areas and Centers

  • Academic Focus: Center for Brain, Behavior and Metabolism (CBBM)


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