A robust measure of metacognitive sensitivity and confidence criteria

Author:

Arnold Derek H.ORCID,Clendinen Mitchell,Johnston AlanORCID,Lee Alan L.F.,Yarrow KielanORCID

Abstract

Humans experience feelings of confidence in their decisions. In perception, these feelings are typically accurate – we tend to feel more confident about correct decisions. The degree of insight people have into the accuracy of their decisions is known as metacognitive sensitivity. Currently popular methods of estimating metacognitive sensitivity are subject to interpretive ambiguities because they assume that humans experience normally-shaped distributions of different experiences when they are repeatedly exposed to a single input. If, however, people have skewed distributions of experiences, or distributions with excess kurtosis (i.e. a distribution containing greater numbers of extreme experiences than is predicted by a normal distribution), calculations can erroneously underestimate metacognitive sensitivity. Here, we describe a means of estimating metacognitive sensitivity that is more robust against violations of the normality assumption. This improved method relies on estimating the precision with which people transition between making categorical decisions with relatively low to high confidence, and on comparing this with the precision with which they transition between making different types of perceptual category decision. The new method can easily be added to standard behavioral experiments. We provide free Matlab code to help researchers implement these analyses and procedures in their own experiments.Public Significance StatementSignal-detection theory is one of the most popular frameworks for analysing data from experiments of human behaviour – including investigations of confidence. The authors demonstrate that if a key assumption of this framework is inadvertently violated, analyses of confidence can lead to unwarranted conclusions. They develop a new and more robust measure of confidence.

Publisher

Cold Spring Harbor Laboratory

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3