Hierarchical meta-d' model (HMeta-d)
Steve Fleming email@example.com
This MATLAB toolbox implements the meta-d’ model (Maniscalco & Lau, 2012) in a hierarchical Bayesian framework using Matlab and JAGS, a program for conducting MCMC inference on arbitrary Bayesian models. A paper with more details on the method and the advantages of estimating meta-d’ in a hierarchal Bayesian framework is available here https://academic.oup.com/nc/article/doi/10.1093/nc/nix007/3748261/HMeta-d-hierarchical-Bayesian-estimation-of. For a more general introduction to Bayesian models of cognition see Lee & Wagenmakers, Bayesian Cognitive Modeling: A Practical Course http://bayesmodels.com/
The code is designed to work “out of the box” without much coding on the part of the user, and it receives data in the same format as Maniscalco & Lau’s toolbox, allowing easy switching and comparison between the two.
- To get started, you need to first ensure JAGS (an MCMC language similar to BUGS) is installed on your machine. See here for further details:
http://mcmc-jags.sourceforge.net/ The code has been tested on JAGS 3.4.0; there are compatibility issues between matjags and JAGS 4.X. The model files should work with later versions of JAGS when called from R.
- The main functions are fit_meta_d_mcmc (for fitting individual subject data) and fit_meta_d_mcmc_group (for hierarchical fits of group data). More information is contained in the help of these two functions and in the wiki https://github.com/smfleming/HMM/wiki/HMeta-d-tutorial. To get started try running exampleFit or exampleFit_group.
Please get in touch with your experiences with using the toolbox, and any bug reports or issues to me at firstname.lastname@example.org
This code is being released with a permissive open-source license. You should feel free to use or adapt the utility code as long as you follow the terms of the license, which are enumerated below. If you use the toolbox in a publication we ask that you cite the following paper:
Fleming, S.M. (2017) HMeta-d: hierarchical Bayesian estimation of metacognitive efficiency from confidence ratings, Neuroscience of Consciousness, 3(1) nix007, https://doi.org/10.1093/nc/nix007
Copyright (c) 2017, Stephen Fleming
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