Temporally delayed representations in alpha and beta rhythms in higher-order cortical networks track increasing relational integration demands

Relational reasoning is the ability to infer and understand the relations between multiple elements. In humans, this ability supports higher cognitive functions and is linked to fluid intelligence. Relational complexity (RC) is a cognitive framework that offers a generalisable method for classifying the complexity of reasoning problems. To date, increased RC has been linked to static patterns of brain activity supported by the frontoparietal system, but limited work has assessed the multivariate spatiotemporal dynamics that code for RC. To address this, we conducted representational similarity analysis in two independent neuroimaging datasets (Dataset 1 fMRI, n=40; Dataset 2 EEG, n=45), where brain activity was recorded while participants completed a visuospatial reasoning task that included different levels of RC (Latin Square Task). Our findings revealed that, spatially, RC representations were widespread, peaking in brain networks associated with higher-order cognition (frontoparietal, dorsal-attention, and cingulo-opercular). Temporally, RC was represented in the 2.5 - 4.1 seconds post-stimuli window and emerged in the alpha and beta frequency range. Finally, multimodal fusion analysis revealed the information carried in the representation is concordant with the theorised RC model relative to a model of cognitive effort in higher-order cortical networks. Altogether, the results further our understanding of what information is held during relational processing, highlight the spatially distributed nature of RC representations, and emphasise the importance of late frequency-resolved neural processes to resolve RC. Significance statement: Assessing cognitive abilities in human reasoning requires a cognitive framework that can define the complexity of a problem. Relational complexity (RC) theory has emerged as a robust framework, parameterising complexity based on the number of relations that must be processed in parallel to solve a problem's underlying relational representation. To investigate where or when the brain codes for this cognitive abstraction, our study examined multimodal whole-brain activity during short reasoning problems at various complexity levels. We found that higher-cortical networks coded a representation tracking the transition between complexity demands, emerging during late-stage cognitive processing. Our findings provide neuroscientific evidence for a neural coding of relational complexity, pinpointing the frequency-resolved dynamics associated with these mechanisms.