The Stan codes of Meta-d' Model

• Term Project of Computational modeling course (Spring 2020)
• For the details of the term project, please visit here (Google Drive). You can find PPT slides and other versions of codes.
• The codes implements the meta-d' model (Maniscalco & Lau 2012) in a non-hierarchical and hierarchical Bayesian framework using STAN. The hierarchical version also referred HMeta-d' toolbox from Fleming 2017.
• Feel free to ask anything about the details! -- Heesun Park ([email protected])

• /codes: Stan codes and R codes for running the model. 'indiv' for non-hierarchical Bayesian modeling, 'hierarchical' for hierarchical Bayesian modeling
• /example_data: Example data for testing the codes. The processed open data from Samaha & Postle 2017
• /figure: Example figures

• Maniscalco, B., & Lau, H. (2012). A signal detection theoretic approach for estimating metacognitive sensitivity from confidence ratings. Consciousness and Cognition, 21(1), 422–430.Paper doi:10.1016/j.concog.2011.09.021 MATLAB code

• Fleming, S. M. (2017). HMeta-d: hierarchical Bayesian estimation of metacognitive efficiency from confidence ratings. Neuroscience of Consciousness, 3(1), nix007-. doi:10.1093/nc/nix007 Paper HMeta-d toolbox

• Samaha, J., & Postle, B. R. (2017). Correlated individual differences suggest a common mechanism underlying metacognition in visual perception and visual short-term memory. Proceedings of the Royal Society B, 284(1867), 20172035. doi:10.1098/rspb.2017.2035 Paper