Rahnev Dobromir
School of Psychology, Georgia Institute of Technology, Atlanta, GA, USA.
Computational Cognition Center of Excellence, Georgia Institute of Technology, Atlanta, GA, USA.
Nat Commun. 2025 Jan 15;16(1):701. doi: 10.1038/s41467-025-56117-0.
One of the most important aspects of research on metacognition is the measurement of metacognitive ability. However, the properties of existing measures of metacognition have been mostly assumed rather than empirically established. Here I perform a comprehensive empirical assessment of 17 measures of metacognition. First, I develop a method of determining the validity and precision of a measure of metacognition and find that all 17 measures are valid and most show similar levels of precision. Second, I examine how measures of metacognition depend on task performance, response bias, and metacognitive bias, finding only weak dependences on response and metacognitive bias but many strong dependencies on task performance. Third, I find that all measures have very high split-half reliabilities, but most have poor test-retest reliabilities. This comprehensive assessment paints a complex picture: no measure of metacognition is perfect and different measures may be preferable in different experimental contexts.
元认知研究中最重要的方面之一是元认知能力的测量。然而，现有元认知测量方法的特性大多是假设的，而非通过实证确定。在此，我对17种元认知测量方法进行了全面的实证评估。首先，我开发了一种确定元认知测量方法有效性和精确性的方法，发现所有17种测量方法都是有效的，且大多数显示出相似的精确水平。其次，我研究了元认知测量方法如何依赖于任务表现、反应偏差和元认知偏差，发现其对反应和元认知偏差的依赖较弱，但对任务表现的依赖较强。第三，我发现所有测量方法的分半信度都非常高，但大多数的重测信度较差。这一全面评估描绘了一幅复杂的图景：没有一种元认知测量方法是完美的，不同的测量方法在不同的实验情境中可能更受青睐。
