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Front Hum Neurosci. 2014 Jul 15;8:443. doi: 10.3389/fnhum.2014.00443. eCollection 2014.

How to measure metacognition.

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Department of Experimental Psychology, University of Oxford Oxford, UK ; Center for Neural Science, New York University New York, NY, USA.
Department of Psychology, Columbia University New York, NY, USA ; Department of Psychology, University of California, Los Angeles Los Angeles, CA, USA.


The ability to recognize one's own successful cognitive processing, in e.g., perceptual or memory tasks, is often referred to as metacognition. How should we quantitatively measure such ability? Here we focus on a class of measures that assess the correspondence between trial-by-trial accuracy and one's own confidence. In general, for healthy subjects endowed with metacognitive sensitivity, when one is confident, one is more likely to be correct. Thus, the degree of association between accuracy and confidence can be taken as a quantitative measure of metacognition. However, many studies use a statistical correlation coefficient (e.g., Pearson's r) or its variant to assess this degree of association, and such measures are susceptible to undesirable influences from factors such as response biases. Here we review other measures based on signal detection theory and receiver operating characteristics (ROC) analysis that are "bias free," and relate these quantities to the calibration and discrimination measures developed in the probability estimation literature. We go on to distinguish between the related concepts of metacognitive bias (a difference in subjective confidence despite basic task performance remaining constant), metacognitive sensitivity (how good one is at distinguishing between one's own correct and incorrect judgments) and metacognitive efficiency (a subject's level of metacognitive sensitivity given a certain level of task performance). Finally, we discuss how these three concepts pose interesting questions for the study of metacognition and conscious awareness.


confidence; consciousness; metacognition; probability judgment; signal detection theory

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