How underconfidence Is maintained in the face of reality

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All code and data for reproducing analyses and figures for:

Sucharit Katyal, Quentin J. Huys, Raymond J. Dolan, and Stephen M. Fleming. 2023. “How Underconfidence Is Maintained in the Face of Reality.” PsyArXiv. May 24. https://psyarxiv.com/qcg92/

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Model-free analyses along with the generation of figures for the paper was done in R v4.3.0 in RStudio (see: code/r). For reproducing the analyses and figures, step through the following code:

analysis and figures for Exp1:                    code/r/mbsAnalyses_Exp1.R

analysis and figures for Exp2:                    code/r/mbsAnalyses_Exp2.R

figures for the model simulations and parameters: code/r/mbsAnalyses_model.R

NOTE: Before performing analyses for Exp 2, you need to unzip the two zipped .csv files in data/exp2

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Model was fitted using Matlab (v 2022b) + Jags toolbox (v 3.4.1) (see code/matlab)

To run the model / reproduce the model analyses, perform the following steps:

1. Ensure that JAGS (an MCMC language similar to BUGS) is installed on your machine. See here for further details: http://mcmc-jags.sourceforge.net/. Note that there are compatibility issues between matjags (MATLAB interface for JAGS) and newer version of JAGS (e.g., 4.X). You will need to install JAGS 3.4.0 rather than the latest version (https://sourceforge.net/projects/mcmc-jags/files/JAGS/3.x/).

2. The matlab code to fit the model is available in the script: code/matlab/mbsMainScript.m The code contains separate cells for fitting Exp 1 and Exp 2. For each Exp, you would need to re-evaluate the cell for each model variant by changing the modelName variable to 'model_N' (where N is between 0-10 for Exp 1 and 0-11 for Exp 2). The model numbers corresponding to the model variants are the same as in the manuscript and are also specfied in the matlab script. Currently, the script specifies to run the model for 2000 iterations that takes a long time to run (>1 hour), but the model should give the same results with fewer iterations (for example, you can use 50 or 100 iterations to speeed up processing, which should run in 5-10 minutes depending on your machine). After each successful run of the model, there will be a plot of the posterior distributions of the relevant model parameters. The fitted model will be returned in a field named fitZ or fitRegZ inside the returned data structure.

3. After running all the model variants, you can save the model using the following matlab cell (note that this will overwrite the previous model fits). You can then reproduce the model figures from the paper by using the same R script as above: mbsAnalyses_model.R

For running model simulations (and recovery), see script: code/matlab/mbsSimulateExperiment.m

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To run the models on your own data with self-performance estimates + local confidence + accuracy [+ feedback (optional)]), use the following function (function help contains all necessary inputs for the model):

code/matlab/model/fit_globalSPE.m

If you need help with running the model on your data or if you need help customising the model to your dataset, contact: sucharit.katyal [@] gmail [dot] com

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Pre-registration doc for Exp 2 can be found at https://osf.io/2pq6y/

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stimuli directory contains javascript stimuli used in Exp 1 and Exp 2

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License

This code is being released with a permissive open-source license. Please feel free to use or adapt the code as long as you follow the terms of the license enumerated below. If you use the model in a publication, we ask that you cite the following paper:

Katyal, S., Huys, Q. J., Dolan, R. J., & Fleming, S. M. (2023, May 24). How underconfidence is maintained in anxiety and depression. https://doi.org/10.31234/osf.io/qcg92

Copyright (c) 2023, Sucharit Katyal

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