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Neural correlates of metacognition in education: a machine learning approach

  • Damien S. Fleur
  • , Esra C.S. de Groot
  • , Bert Bredeweg
  • , Wouter van den Bos

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

Metacognition, the ability to reflect and regulate one's cognitive processes, has been shown to play a role in various aspects of life, particularly in academic settings. While important steps have been made in uncovering the neural basis of metacognition for highly specific domains (such as perceptual and mnemonic decision-making), little is known about how these findings relate to general forms of metacognition relevant in education. In this study, we use a data-driven approach to (i) identify brain regions associated with metacognition in education, and (ii) investigate the issue of domain-generality and to what extent these brain regions overlap with regions involved in metacognition in the context of specific decision-making tasks used in cognitive neuroscience. Individual differences in grey-matter volume in the precuneus and neighbouring brain regions were associated with education-related metacognitive knowledge and regulation. We also found overlaps between task-related mnemonic metacognitive abilities and education-related metacognitive knowledge, for example in in the superior frontal cortex. There were also regions specifically associated with metacognition in education, such as the banks of the superior temporal sulcus. Together, our findings suggest a link between lab-setting, domain-specific metacognitive abilities and real-life metacognition in the context of education.

Original languageEnglish
Article number109265
Number of pages11
JournalNeuropsychologia
Volume219
DOIs
Publication statusPublished - 15 Dec 2025

Funding

We would like to thank the University of Amsterdam for supporting this research through the Interdisciplinary Doctorate Agreement grant. W.v.d.B. is further supported by the Jacobs Foundation, European Research Council (grant no. ERC-2018-StG-803338), the European Union Horizon 2020 research and innovation programme (grant no. DiGYMATEX-870578), and the Netherlands Organization for Scientific Research (grant no. NWO-VIDI 016.Vidi.185.068).

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