Regression methods for metacognitive sensitivity

Publication date: February 2020Source: Journal of Mathematical Psychology, Volume 94Author(s): Simon Bang Kristensen, Kristian Sandberg, Bo Martin BibbyAbstractMetacognition 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-sensitivit...
Source: Journal of Mathematical Psychology - Category: Psychiatry & Psychology Source Type: research