Age-Related Nuances in Knowledge Assessment

Although crystallized intelligence (gc) is a prominent factor in contemporary theories of individual differences in intelligence, its structure and optimal measurement are elusive. Analogously to the personality trait hierarchy, we propose the following hierarchy of declarative fact knowledge as a key component of gc: a general fact knowledge factor at the apex, followed by broad knowledge areas (e.g., natural sciences, social sciences, humanities), knowledge domains (e.g., chemistry, law, art), and nuances. In most scientific contexts we are predominantly concerned with aggregate levels, but we argue that the sampling of knowledge items strongly affects distinctions at higher levels of the hierarchy. We illustrate the magnitude of item-level heterogeneity by predicting chronological age differences through knowledge differences at different levels of the hierarchy. Analyses were based on an online sample of 1,629 participants between 18 and 70 years old who completed 120 broadly sampled declarative knowledge items across twelve domains. The results of linear and elastic net regressions, respectively, demonstrated that the majority of the age variance was located at the item level, and the strength of the prediction decreased with increasing aggregation. Knowledge nuances seem to tap important variance that is not covered with aggregate scores (e.g., sum or factor scores) and that is useful in the prediction of age. In turn, these effects extend our understanding how knowledge is acquired and imparted. On a more general stance, to gain new insights into the nature of knowledge, its optimal measurement and psychometric representation, item and person sampling issues should be considered.

尽管结晶智能（gc）是当代个体差异智力理论中的一个显著因素，但其结构及最佳测量方法尚不明确。类比于人格特质层级，我们提出以下陈述性事实知识的层级结构，将其视为结晶智能的关键组成部分：在顶层是一个普遍的事实知识因素，随后是广泛的认知领域（例如，自然科学、社会科学、人文科学），知识领域（例如，化学、法律、艺术），以及细微差别。在大多数科学情境中，我们主要关注总体水平，但我们认为知识项目的抽样强烈影响了层级更高层面的区分。我们通过预测不同层级知识差异来阐述项目层级异质性的程度。分析基于一个在线样本，其中1,629名18至70岁的参与者完成了涵盖十二个领域的120个广泛抽样的陈述性知识项目。线性回归和弹性网络回归的结果分别显示，年龄差异的大部分位于项目层级，且预测强度随着总体水平的增加而减弱。知识的细微差别似乎触发了未被总体分数（例如，总和或因子分数）涵盖的重要差异，这对于年龄预测是有益的。反过来，这些效应扩展了我们对知识获取和传授方式的理解。从更普遍的角度来看，为了深入了解知识的本质、其最佳测量和心理学表征，以及项目和个体抽样问题，应予以考虑。