Relational Integration Demands Are Tracked by Temporally Delayed Neural Representations in Alpha and Beta Rhythms Within Higher‐Order Cortical Networks

TL;DR

The study shows how the brain processes complex relationships using specific neural patterns in higher-order brain networks.

Contribution

The paper introduces a novel approach to tracking relational complexity using temporally delayed neural representations in alpha and beta rhythms.

Findings

RC representations peak in higher-order brain networks like frontoparietal and dorsal-attention systems.

RC is temporally encoded in alpha and beta frequency bands 2.5–4.1 seconds post-stimulus.

Multimodal analysis shows RC models better explain brain activity than cognitive effort models.

Abstract

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 s post‐stimuli window and emerged in the alpha and beta frequency range. Finally, multimodal fusion analysis demonstrated that shared variability within EEG‐fMRI signals within higher‐order cortical networks were better explained by the theorized RC model, relative to a model of cognitive effort (CE). Altogether, the results further our understanding of the neural representations supporting relational processing, highlight the spatially distributed coding of RC and CE across cortical networks, and emphasize the importance of late‐stage, frequency‐specific neural dynamics in resolving RC.

Using multimodal whole‐brain imaging, we found brain activity patterns corresponded to relational complexity (RC) transitions in higher‐cortical networks, encoded in frequency‐specific bands during late‐stage processing. Compared to an alternative model of “cognitive effort”, brain representations demonstrated stronger correspondence to RC, with individual encoding linked to participant performance.

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