Department of Psychology, University of Washington, Seattle, WA 98195, United States.
Department of Psychology, University of Groningen, the Netherlands.
Cognition. 2021 Jul;212:104660. doi: 10.1016/j.cognition.2021.104660. Epub 2021 Mar 20.
Translational applications of cognitive science depend on having predictive models at the individual, or idiographic, level. However, idiographic model parameters, such as working memory capacity, often need to be estimated from specific tasks, making them dependent on task-specific assumptions. Here, we explore the possibility that idiographic parameters reflect an individual's biology and can be identified from task-free neuroimaging measures. To test this hypothesis, we correlated a reliable behavioral trait, the individual rate of forgetting in long-term memory, with a readily available task-free neuroimaging measure, the resting-state EEG spectrum. Using an established, adaptive fact-learning procedure, the rate of forgetting for verbal and visual materials was measured in a sample of 50 undergraduates from whom we also collected eyes-closed resting-state EEG data. Statistical analyses revealed that the individual rates of forgetting were significantly correlated across verbal and visual materials. Importantly, both rates correlated with resting-state power levels in the low (13-15 Hz) and upper (15-17 Hz) portion of the beta frequency bands. These correlations were particularly strong for visuospatial materials, were distributed over multiple fronto-parietal locations, and remained significant even after a correction for multiple comparisons (False Discovery Rate) and after robust correlation methods were applied. These results suggest that computational models could be individually tailored for prediction using idiographic parameter values derived from inexpensive, task-free imaging recordings.
认知科学的转化应用依赖于在个体或特定个体水平上具有预测模型。然而，特定个体模型参数，如工作记忆容量，通常需要从特定任务中估计，这使得它们依赖于特定任务的假设。在这里，我们探讨了特定个体参数反映个体生物学特征并可以从无任务神经影像学测量中识别的可能性。为了检验这一假设，我们将可靠的行为特征，即长期记忆中的个体遗忘率与一种现成的无任务神经影像学测量方法，即静息状态 EEG 频谱相关联。我们使用一种已建立的、自适应的事实学习程序，在 50 名本科生样本中测量了言语和视觉材料的遗忘率，我们还从他们那里收集了闭眼静息状态 EEG 数据。统计分析表明，言语和视觉材料的个体遗忘率之间存在显著相关性。重要的是，两种速率都与贝塔频带的低频（13-15 Hz）和高频（15-17 Hz）部分的静息状态功率水平相关。这些相关性在视觉空间材料中特别强，分布在多个额顶部位，并在进行多次比较校正（假发现率）和应用稳健相关方法后仍然显著。这些结果表明，可以使用从廉价的、无任务的成像记录中得出的特定个体参数值来为预测量身定制计算模型。
