This is the project page for the manuscript "Using artificial neural networks to reveal the human confidence computation". All codes are written in Python and Matlab. If you have any questions, write to Medha Shekhar at medha@gatech.edu Below is a brief description of the files The folder 'codes' contains all the codes for 1) simulating the CNN models with different confidence strategies, 2) fitting these models to human confidence and 3) running all analyses reported in the paper. The 'simulations' folder contains the files for simulating the four CNN models (RTNet, MDSNet, BLNet and CNet). To run these codes, you will need the pre-trained models. However, due to size restrictions, the pre-trained models could not be uploaded here. If you want access to them, please write to me. The folder also contains an 'index.csv' file which lists the MNIST indices of the images used in the experiment. You will need this to identify the images. The 'modelFitting' folder contains codes to fit the raw confidence values from the simulations to human confidence. To run the fitting procedure, run the file called 'runFitting.m'. The folder also contains all the fitting results for each CNN architecture and confidence models. The 'analysis' folder contains codes for running all the analyses reported in the main manuscript. The 'data' folder contains the data from human subjects as well as the results of the simulations for each CNN architecture. The analysis and modelFitting folders use these files.