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**Regression methods for metacognitive sensitivity.**
*(English)*
Zbl 1437.91361

Summary: 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-\(d^\prime\). 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- \(d^\prime\) in a latent variable model. We show how this agrees with the literature on meta-\(d^\prime\) and constitutes a practical framework for extending the model. We supply several theoretical considerations on the model, including closed-form approximate estimates of meta- \(d^\prime\) 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- \(d^\prime\) and are useful for theoretical and practical researchers of metacognition.

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\textit{S. B. Kristensen} et al., J. Math. Psychol. 94, Article ID 102297, 17 p. (2020; Zbl 1437.91361)

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