Distorted learning from local metacognition supports transdiagnostic underconfidence

S Sucharit Katyal (Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London) Q Quentin JM Huys R Raymond J. Dolan (Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, Queen Square Institute of Neurology) S Stephen M. Fleming

Abstract

Abstract Individuals experiencing symptoms of anxiety and depression have been shown to exhibit persistent underconfidence. The origin of such metacognitive biases presents a puzzle, given that individuals should be able to learn appropriate levels of confidence from observing their own performance. In two large general population samples ( N  = 230 and N  = 278), we measure both 'local' confidence in individual task instances and 'global' confidence as longer-run self-performance estimates while manipulating external feedback. Global confidence is sensitive to both local confidence and feedback valence—more frequent positive (negative) feedback increases (respectively decreases) global confidence, with asymmetries in feedback also leading to shifts in affective self-beliefs. Notably, however, global confidence exhibits reduced sensitivity to instances of higher local confidence in individuals with greater subclinical anxious-depression symptomatology, despite sensitivity to feedback valence remaining intact. Our finding of blunted sensitivity to increases in local confidence offers a mechanistic basis for how persistent underconfidence is maintained in the face of intact performance.

Article Details

Volume / Issue Vol. 16, Issue 1
Published February 21, 2025
ISSN 2041-1723
Publisher Nature Portfolio

Journal Info

Nature Communications

Nature Portfolio

ISSN: 2041-1723 Open Access Life Sciences

Authors (4)

S

Sucharit Katyal

Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London

Q

Quentin JM Huys

R

Raymond J. Dolan

Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, Queen Square Institute of Neurology

S

Stephen M. Fleming