The scaffolding of individual variability in language processing by domain-general neural networks

Language processing is supported by distributed neural systems. Yet most research examines these systems at the population-average level, obscuring how individual cognitive differences shape language-related brain activity. In this study, we combined comprehensive cognitive assessments and task-based fMRI in a large sample of healthy adults (N = 205) to examine how variability in linguistic knowledge, working memory, processing speed, and non-verbal reasoning influenced neural responses in four language tasks: lexical decision, picture naming, sentence comprehension, and sentence production. All tasks showed robust left-lateralized activation patterns and varied in the extent to which they engaged the classical perisylvian language network. At the level of individual differences, cognitive skills were not primarily associated with modulation of commonly activated language regions but largely associated with activity in domain-general systems outside these regions, particularly within the default mode, executive and attentional networks. Activation in these domain-general regions was predominantly negatively correlated with cognitive skills, indicating greater recruitment in individuals with lower cognitive skills. Overall, our findings demonstrate that individual cognitive profiles determine how distributed brain systems are dynamically engaged to scaffold language processing.