Exploring the impact of social relevance on the cortical tracking of speech: viability and temporal response characterisation

Human speech is inherently social. Yet our understanding of the neural substrates underlying continuous speech perception relies largely on neural responses to monologues, leaving substantial uncertainty about how social interactions shape the neural encoding of speech. Here, we bridge this gap by studying how EEG responses to speech change when the input includes a social element. In Experiment 1, we compared the neural encoding of synthesised undirected monologues, directed monologues, and dialogues. In Experiment 2, we extended this by using podcasts, addressing the additional challenges of real speech dialogue, such as dysfluency. Using temporal response function analyses, we show that the presence of a social component strengthens the cortical tracking of the speech envelope, despite identical acoustic properties. Neural responses to synthesised speech showed a strong correlation with those for real speech podcasts, with a stronger alignment emerging for more socially-relevant speech material. In addition, we demonstrate that robust neural indices of sound and lexical-level processing can be derived using real podcast recordings despite the presence of dysfluencies. Finally, we present a simulation to put to the test the robustness of temporal response function analyses under increasing levels of dysfluency. Together, these findings highlighting the impact of social elements in shaping auditory neural processing, providing a framework for future investigation and analysis of social speech listening and speech interaction. Significance Statement Human speech is rarely produced or processed in a social vacuum. Yet, our understanding of continuous speech neurophysiology mostly comes from experiments involving speech monologues. This study reveals how social context modulates the neural encoding of speech. We directly contrast neural signals recorded when participants listened to monologues and dialogues, using controlled material from speech synthesis and real podcast recordings. We found that the social element amplifies the neural encoding of speech features, reflecting greater engagement. We also show strong correlation between synthetic and real podcast neural responses, scaling with social relevance. Finally, we demonstrate that lexical processing can be measured robustly even amid natural dysfluencies. These insights advance our understanding of speech neurophysiology, informing future research on social speech.