August 2023
Volume 23, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2023
Optimal metacognitive decision strategies in Signal Detection Theory
Author Affiliations & Notes
  • Megan Peters
    University of California Irvine
  • Lucie Charles
    Institute of Cognitive Neuroscience, University College London
  • Brian Maniscalco
    University of California Irvine
  • Footnotes
    Acknowledgements  Canadian Institute for Advanced Research Azrieli Global Scholars Program; postdoctoral fellowship of the British Academy
Journal of Vision August 2023, Vol.23, 5465. doi:
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      Megan Peters, Lucie Charles, Brian Maniscalco; Optimal metacognitive decision strategies in Signal Detection Theory. Journal of Vision 2023;23(9):5465.

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      © ARVO (1962-2015); The Authors (2016-present)

  • Supplements

Signal detection theory (SDT) has long provided the fields of vision science and psychology with a simple but powerful model of how observers make choices under uncertainty, allowing us to distinguish sensitivity from response bias and characterize optimal decision strategies. Recent work has extended SDT to quantify metacognitive sensitivity in perceptual decision-making. In this project, we further advance the application of SDT to the study of metacognition by providing a formal account of normative metacognitive decision strategies – i.e., type 2/confidence criterion setting – for ideal observers. Because optimality is always defined relative to a given objective, we use SDT to derive formulae for optimal type 2 criteria under four distinct objectives: maximizing type 2 accuracy, maximizing type 2 reward, calibrating confidence to accuracy, and maximizing the difference between type 2 hit rate and false alarm rate. Where applicable, we consider these optimization contexts alongside their type 1 counterparts (e.g. maximizing type 1 accuracy) to deepen understanding. We examine the different strategies implied by these formulae and further consider how optimal type 2 criterion setting differs when metacognitive sensitivity deviates from SDT expectation. We also provide an online toolbox for implementing these analyses, e.g. in visual discrimination experiments. The theoretical framework provided here can be used to better understand the metacognitive decision strategies of real observers. Possible applications include characterizing observers’ spontaneously chosen metacognitive decision strategies, assessing their ability to fine-tune metacognitive decision strategies to optimize a given outcome when instructed, determining over- or under-confidence relative to an optimal standard, and more. This framework opens new avenues for enriching our understanding of metacognition in visual, multimodal, and cognitive domains.


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