Cortical tracking of connected speech is interactively modulated by top-down predictions and bottom-up signal quality

Prediction facilitates speech comprehension, but how predictions are combined with sensory input during perception remains unclear. Prior studies suggest that prediction error computations, which represent the difference between heard and expected speech sound, play a central role in speech perception. However, these studies often rely on non-ecological listening conditions, involving isolated words predicted by artificial cues. In two experiments, we presented male and female participants with semantically coherent sentences that ended in words of varying predictability. We also manipulated the signal quality of the final word by applying time-specific degradation. Using linear encoding models of EEG responses, we found that neural representations of speech features were jointly influenced by top-down predictions and bottom-up signal quality. Specifically, for unpredicted final words, increasing signal quality resulted in enhanced neural tracking of spectral and temporal modulations. In contrast, for strongly predicted final words, greater signal quality led to reduced tracking of speech modulations. Computational simulations revealed that this interaction is consistent with prediction error computations, but not with alternative models such as signal sharpening. These findings extend the evidence for prediction error computations to more naturalistic listening situations.