| Literature DB >> 34481178 |
Kai Xue1, Medha Shekhar2, Dobromir Rahnev2.
Abstract
We recently found a positive relationship between estimates of metacognitive efficiency and metacognitive bias. However, this relationship was only examined on a within-subject level and required binarizing the confidence scale, a technique that introduces methodological difficulties. Here we examined the robustness of the positive relationship between estimates of metacognitive efficiency and metacognitive bias by conducting two different types of analyses. First, we developed a new within-subject analysis technique where the original n-point confidence scale is transformed into two different (n-1)-point scales in a way that mimics a naturalistic change in confidence. Second, we examined the across-subject correlation between metacognitive efficiency and metacognitive bias. Importantly, for both types of analyses, we not only established the direction of the effect but also computed effect sizes. We applied both techniques to the data from three tasks from the Confidence Database (N > 400 in each). We found that both approaches revealed a small to medium positive relationship between metacognitive efficiency and metacognitive bias. These results demonstrate that the positive relationship between metacognitive efficiency and metacognitive bias is robust across several analysis techniques and datasets, and have important implications for future research. Published by Elsevier Inc.Entities:
Keywords: Confidence; Metacognition; Metacognitive noise; Perceptual decision making
Mesh:
Year: 2021 PMID: 34481178 PMCID: PMC8560567 DOI: 10.1016/j.concog.2021.103196
Source DB: PubMed Journal: Conscious Cogn ISSN: 1053-8100