Predicting speech from a cortical hierarchy of event-based time scales

Lea Maria Schmitt*, Julia Erb, Sarah Tune, Anna U. Rysop, Gesa Hartwigsen, Jonas Obleser*

*Corresponding author for this work
11 Citations (Scopus)

Abstract

How do predictions in the brain incorporate 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. This yields a hierarchical, multilayered organization of predictive language comprehension. Training artificial neural networks to predict the next word in a story at five stacked time scales and then using model-based functional magnetic resonance imaging, we observe an event-based “surprisal hierarchy” evolving along a temporoparietal pathway. Along this hierarchy, surprisal at any given time scale gated bottom-up and top-down connectivity to neighboring time scales. In contrast, surprisal derived from continuously updated context influenced temporoparietal activity only at short time scales. Representing context in the form of increasingly coarse events constitutes a network architecture for making predictions that is both computationally efficient and contextually diverse.

Original languageEnglish
Article numbereabi6070
JournalScience Advances
Volume7
Issue number49
DOIs
Publication statusPublished - 12.2021

Research Areas and Centers

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

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