Robinson Conor, Cocchi Luca, Ito Takuya, Hearne Luke
Clinical Brain Networks Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia.
Faculty of Medicine, University of Queensland, Brisbane, Queensland, Australia.
Hum Brain Mapp. 2025 Jul;46(10):e70272. doi: 10.1002/hbm.70272.
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.
关系推理是推断和理解多个元素之间关系的能力。在人类中，这种能力支持更高层次的认知功能，并与流体智力相关联。关系复杂性（RC）是一种认知框架，它提供了一种可通用的方法来对推理问题的复杂性进行分类。迄今为止，RC的增加与额顶叶系统支持的大脑活动静态模式有关，但评估编码RC的多变量时空动态的工作有限。为了解决这个问题，我们在两个独立的神经成像数据集中进行了表征相似性分析（数据集1功能磁共振成像，n = 40；数据集2脑电图，n = 45），在参与者完成包括不同RC水平的视觉空间推理任务（拉丁方任务）时记录大脑活动。我们的研究结果表明，在空间上，RC表征广泛分布，在与高阶认知相关联的脑网络（额顶叶、背侧注意和扣带回-脑岛）中达到峰值。在时间上，RC在刺激后2.5-4.1秒的窗口中得到表征，并出现在α和β频率范围内。最后，多模态融合分析表明，相对于认知努力（CE）模型，高阶皮质网络内脑电图-功能磁共振成像信号中的共享变异性能更好地由理论化的RC模型解释。总之这些结果进一步加深了我们对支持关系处理的神经表征的理解，突出了RC和CE在皮质网络中的空间分布式编码，并强调了后期特定频率神经动力学在解决RC中的重要性。
