TY - JOUR

T1 - Regression methods for metacognitive sensitivity

AU - Kristensen, Simon Bang

AU - Sandberg, Kristian

AU - Bibby, Bo Martin

PY - 2020/2/1

Y1 - 2020/2/1

N2 - 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.

AB - 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.

KW - Metacognition

KW - Modelling

KW - Signal Detection Theory

UR - http://www.scopus.com/inward/record.url?scp=85074885183&partnerID=8YFLogxK

U2 - 10.1016/j.jmp.2019.102297

DO - 10.1016/j.jmp.2019.102297

M3 - Journal article

AN - SCOPUS:85074885183

VL - 94

JO - Journal of Mathematical Psychology

JF - Journal of Mathematical Psychology

SN - 0022-2496

M1 - 102297

ER -