**Tasks**
We provide both tasks from the paper here. Both have been written using Josh deLeeuw's jsPsych toolbox (http://www.jspsych.org/).
Both tasks require communication with a PHP server in order to run. You can achieve this by running them on your own domain, or by using a tool like XAMPP to run the PHP server locally.
In order to save the data, you will need to be able to connect to a SQL database on your server. The code has some default names for the tables it stores data in (e.g., daw_data, daw_subinfo, daw_subid), but feel free to change anything about the way the data storage is managed.
**Data**
We provide all the raw data for the stake versions of both two-step paradigms, and the functions that are used to analyze them. These functions are written in Matlab.
We have also provided the raw data files, and group-level-analyzed data in csv format, for those who prefer to analyze the data in other software programs. The variable names can be found in the headers of these csv files.
All processed data, including reinforcement-learning model fits and hierarchical logistic regression fits, are stored in the groupdata.mat variables in each experiment's directory.
These groupdata variables can also be computed on the fly using the wrapper.m functions in each experiment's root folder.
The maximum a posteriori model-fitting procedure can be run in the subdirectories called 'standard' and 'exhaustive'. These procedures require the mfit package developed by Sam Gershman, which can be found here: https://github.com/sjgershm/mfit
The functions that fit the data to the RL models (wrapper.m) will take a long time to finish, especially for the exhaustive models which contain almost double the number of parameters. This process can be sped up by breaking up these analyses and running them in parallel jobs on your institution's computing cluster, or by decreasing the number of iterations in the wrapper.m starting file.
**Contact**
If you have any questions, please do not hesitate to contact Wouter Kool (wkool@fas.harvard.edu).
