Regression methods for metacognitive sensitivity

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaperJournal articleResearchpeer-review

Metacognition is an important component in basic science and clinical psychology, often studied through complex, cognitive experiments. While Signal Detection Theory (SDT) provides a popular and pervasive framework for modelling responses from such experiments, a shortfall remains that it cannot in a straightforward manner account for the often complex designs. Additionally, SDT does not provide direct estimates of metacognitive ability. This latter shortcoming has recently been sought remedied by introduction of a measure for metacognitive sensitivity dubbed meta-d. The new sensitivity measure, however, further accentuates the need for a flexible modelling framework. In the present paper, we argue that a straightforward extension of SDT is obtained by identifying the model with the proportional odds model, a widely implemented, ordinal regression technique. We go on to develop a formal statistical framework for metacognitive sensitivity by defining a model that combines standard SDT with meta- d in a latent variable model. We show how this agrees with the literature on meta-d and constitutes a practical framework for extending the model. We supply several theoretical considerations on the model, including closed-form approximate estimates of meta- d and optimal weighing of response-specific meta-sensitivities. We discuss regression analysis as an application of the obtained model and illustrate our points through simulations. Lastly, we discuss a software implementation of the model in R. Our methods and their implementation extend the computational possibilities of SDT and meta- d and are useful for theoretical and practical researchers of metacognition.

Original languageEnglish
Article number102297
JournalJournal of Mathematical Psychology
Number of pages17
Publication statusPublished - 1 Feb 2020

    Research areas

  • Metacognition, Modelling, Signal Detection Theory

See relations at Aarhus University Citationformats

ID: 173288429