statConfR: An R Package for Static Models of Decision Confidence and Metacognition

Abstract

We present the statConfR package for R, which allows researchers to conveniently fit and compare nine different static models of decision confidence applicable to binary discrimination tasks with confidence ratings: the signal detection rating model (Green & Swets, 1966), the Gaussian noise model (Maniscalco & Lau, 2016), the independent Gaussian model (Rausch & Zehetleitner, 2017a), the weighted evidence and visibility model (Rausch, Hellmann, et al., 2018), the lognormal noise model (Shekhar & Rahnev, 2021), the lognormal weighted evidence and visibility model (Shekhar & Rahnev, 2023), the independent truncated Gaussian model (Rausch et al., 2023) based on the meta-d′/d′ (Maniscalco & Lau, 2012, 2014), and the independent truncated Gaussian model based on the Hmetad method (Fleming, 2017). In addition, the statConfR package provides functions for estimating meta-d′/d′, the most widely-used measure of metacognitive efficiency, allowing both Maniscalco and Lau (2012)'s and Fleming (2017)'s model specification.

dynConfiR: The R package for sequential sampling models of decision confidence

“…In addition, the wiener module for JAGS (Wabersich & Vandekerckhove, 2014a) and Stan allow researchers to easily incorporate the DDM in more complex scenarios, for instance, in reinforcement learning situations (Fontanesi et al, 2019). For fitting confidence data, the statConfR package (Rausch & Hellmann, 2024) allows for parameter fitting in the context of static confidence models and the computation of popular measures of metacognitive performance like meta-d ′ /d ′ (Maniscalco & Lau, 2012). However, to our knowledge, no such software for sequential sampling models of confidence is available.…”