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How to measure metacognition

  • Stephen M. Fleming
  • , Hakwan C. Lau
  • University of Oxford
  • New York University
  • Columbia University
  • University of California at Los Angeles

Research output: Contribution to journal › Review article › peer-review

Abstract

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.

Original languageEnglish
Article number443
JournalFrontiers in Human Neuroscience
Volume8
Issue numberJULY
DOIs
StatePublished - 15 Jul 2014
Externally publishedYes

Keywords

  • Confidence
  • Consciousness
  • Metacognition
  • Probability judgment
  • Signal detection theory

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