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

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.

Automated summary

The brain incorporates the temporal unfolding of context in a hierarchical manner by sparsely updating contextual representations at event boundaries. Training artificial neural networks and using functional magnetic resonance imaging reveal an event-based surprisal hierarchy evolving along a temporoparietal pathway. Surprisal influences connectivity between neighboring time scales and temporoparietal activity, demonstrating an efficient and contextually diverse network architecture for predictions.