Social Network Diversity Leads to More Flexible Speech Perception in School-aged Children

Every day, listeners encounter a wide range of acoustic signals. Successfully solving this variability problem allows them to interpret these signals accurately. While this mechanism tends to be less effortful for adults, children need to learn stable categories in the face of such variability. It is unknown to what extent general maturation or diversity of the input plays a role in shaping different speech categorization profiles that children can employ. Here, we tested school-aged children’s speech categorization with a continuous speech categorization task called the Visual Analogue Scaling (VAS) task. We measured the linguistic diversity in each child’s social environment through a social network analysis. We found that increased linguistic diversity led to more flexible and gradient speech categorization. On the other hand, less diverse linguistic input led to more categorical speech categorization. We argue that these findings have implications for speech perception as well as linguistic diversity research.