Fruehlinger Christoph, Paul Katharina, Kührt Corinna, Wacker Jan
Department of Differential Psychology and Psychological Assessment, Institute of Psychology, University of Hamburg, Von-Melle-Park-5, 20146, Hamburg, Germany.
Faculty of Psychology, Technische Universität Dresden, Zellescher Weg 17, 01069, Dresden, Germany.
Cogn Affect Behav Neurosci. 2025 Jul 1. doi: 10.3758/s13415-025-01323-y.
Previous electroencephalogram (EEG) studies linked measures of spectral power under rest and fluid intelligence; however, subsequent high-powered studies challenged this relationship. The present study aimed to address previous limitations (low statistical power, lack of preregistration) and investigated the predictability of intelligence measures from resting-state EEG in the CoScience data set (N = 772). Support vector regressions were applied to analyze 8 min of resting-state EEG with eyes open and closed before and after unrelated tasks. The decoding performance between the spectral power of 59 EEG channels within 30 frequency bins and fluid and crystallized intelligence, was evaluated with a tenfold cross-validation. We could not identify any meaningful associations between resting-state EEG spectral power and either fluid or crystallized intelligence-a null finding that is unlikely to be entirely due to a relatively modest restriction of fluid intelligence variance in our student sample. Moreover, we did replicate the previously reported association between state sleepiness and theta power, attesting to the integrity of the CoScience data set. Furthermore, the decomposition of the EEG signal into its periodic and aperiodic components revealed that the aperiodic offset parameter is significantly correlated with state sleepiness, emphasizing the relevance of aperiodic signal components in understanding states of alertness versus sleepiness.
先前的脑电图(EEG)研究将静息状态下的频谱功率测量值与流体智力联系起来;然而,随后的高功率研究对这种关系提出了质疑。本研究旨在解决先前的局限性(统计功效低、缺乏预注册),并在CoScience数据集中(N = 772)研究静息态脑电图对智力测量的可预测性。应用支持向量回归分析在无关任务前后睁眼和闭眼状态下8分钟的静息态脑电图。采用十折交叉验证法评估了59个脑电图通道在30个频率区间内的频谱功率与流体智力和晶体智力之间的解码性能。我们未能发现静息态脑电图频谱功率与流体智力或晶体智力之间存在任何有意义的关联——这一零结果不太可能完全归因于我们学生样本中流体智力方差的相对适度限制。此外,我们确实重复了先前报道的状态困倦与θ功率之间的关联,证明了CoScience数据集的完整性。此外,将脑电图信号分解为其周期性和非周期性成分表明,非周期性偏移参数与状态困倦显著相关,强调了非周期性信号成分在理解警觉与困倦状态方面的相关性。