All the code necessary to run the task, analysis, and modeling for Master, Curtis & Dayan 2023.
The majority of this is Matlab analysis and modeling code, written to analyze data collected on Amazon Mechanical Turk using the task contained within the /cost folder. The task code is javascript via JsPsych and custom JsPsych plugins.
The main scripts of interest are paper_graphs_and_stats.m, and modeling/HBI/plosCB_fits/run_model_fitting.m. Paper_graphs_and_stats is the script for producing the majority of figures in the manuscript, including most of the supplmental figures. Supplemental figures 3 and 4 are created within run_model_fitting.m. Run_supplemental_analyses, which is also at the top of the directory, cannot be run on its own. It can only be run from within paper_graphs_and_stats.m.
In /task, the experimental code (including images, custom plugins, and pre-built jspsych functions) cost.html is the main experimental page (initializes all, loads up entire experimental timeline) Index.html is an obsolete example for a main experiment page (the old version of cost.html) There is also code for collecting Need for Cognition and Perfectionism scores with the Need for Cognition Scale (NFC) and Short Almost Perfect Scale (SAPS).
The version of the task which is in the manuscript is version 4. The code for running the other versions is still fully functional, as long as you toggle into that version on the homepage for the task code. v1: cost of control with the BDM, a detection task, plain n-back training, and a combined n-back and detection task (based on Kool & Botvinick 2014) v2: cost of control with the BDM, and two versions of an n-switch task v3: cost of control with the BDM, and 1- and 2-back tasks v4: cost of control with the BDM, a 1- and 2-back task, and a 3-detect task (final version with N = 100 subjects collected)
In /data_loading_and_scoring, functions I wrote for loading in mturk data from .json format, or otherwise formatting data. The data come in as a series of long json's containing RT, button press, fair wage ratings, demographic questionnaire answers, and more. The data have been scored in terms of accuracy online, but there is also information there to re-compute accuracy if necessary. The directory has code for scoring the NFC and SAPS questionnaires. It also has code for formatting every subject's data into the same format, from the completely unprocessed MTurk files, for ease of comparison on the group level, stats running, etc.
In /public_data, there are processed data files, including the file toAnalyze, which is a table full of values important for running model fitting within /modeling. the version provided in the public repository contains all subject data from the PLOS CB paper, completely anonymized. toAnalyze is created within modeling/run_simple_sims.m
The outer level of /modeling has modeling functions that are generally necessary for running the cost learning/changing models described in the paper. They are used both in the HBI & type II ML model fitting directories, as they are the core of my models. You'll see from looking inside simulate_cost_model.m or getprobs_costlearning.m, that the scripts are written to be super flexible to the model specified such that they can handle many situations, including different parameter values, different included parameters (alpha vs. delta, for example), different fitting algorithms, different subjects, etc. simulate_cost_model.m simulates data given specific parameter values/model specifications, while getprobs_costlearning takes in the model & subject data and returns the probability of that subject's choices given those parameter values & that model. getprobs_costlearning.m can provide log likelihoods, negative log likelihoods, and maximum a priori values for each parameter set it's given.
In /modeling/HBI, code for running the Hierarchical Bayesian Inference model fitting package by Piray & Daw (2019). Included in this folder is multiple files per model which describe how well the model fits real subject data, as well as simulated subject data. These files are referenced by the outer shell test_model_recoverability.m, which can load any model you specify to display how well it fits with the current model settings. The main file for fitting real subject data is /modeling/HBI/plosCB_fits/run_model_fitting.m. Because model fitting with the CBM package (Piray & Daw) produces many files, I store specific iterations of model fitting within specific folders, instead of cluttering the outer directory with those files.
In modeling/typeII_ML_fitting is code I wrote for running type II maximum likelihood (expectation maximization (EM) algorithm) model fitting, incorporating group-level priors into individual level parameter fits. This is not the method we ended up using, so it's not entirely up to date with the final results, but it may be helpful to someone, so I'm including it. I can't promise it's perfect or all that readable.
In /plotting there are a few functions for making nice figures. There are two plugins in there, one called superbar (from https://www.mathworks.com/matlabcentral/fileexchange/57499-superbar), and the other called violin (https://www.mathworks.com/matlabcentral/fileexchange/45134-violin-plot).