Unraveling Predictive Mechanism in Speech Perception and Production: Insights from EEG Analyses of Brain Network Dynamics

How neural networks coordinate to support speech perception and speech production represents a forefront research topic in both contemporary neuroscience and artificial intelligence. Despite the successful incorporation of hierarchical and predictive attributes from biological neural networks (BNNs) into artificial counterparts, substantial disparities persist, particularly in terms of real-time feedback and nonlinear regulation. To gain a more profound understanding of how BNNs manifest these attributes, the present study employed electroencephalography (EEG) techniques to examine the spatiotemporal brain network dynamics involved in listening and oral reading of identical sentences. These two tasks engage distinct sensorimotor modalities while sharing high-level semantic and syntactic representations. According to a hierarchical feedforward model, the low-level auditory and visual inputs would be progressively transformed towards abstract representations of the sentence meaning, leading to a convergence of brain network patterns in higher cognitive regions. However, our findings challenged this viewpoint by revealing an early resemblance of network activation in the prefrontal and parietal areas in both tasks. It implies a top-down predictive mechanism along with the bottom-up progression. This bidirectional interaction could be potentially implemented through frequency-specific synchronization and desynchronization between functional-specific cortical regions, laying the foundation of the speech chain system with common neural substrates.