Yamada & Samejima (2026)

ReadMe This folder includes all data needed for analysis, all code for analysis and plotting figures, and code for experimental task. Note Data, Analysis and Plot The instructions below describe the steps required to generate figures for our paper and perform statistical tests. Since this project includes the output files generated by the processes needed to create the figures from the start, you can skip several steps and run “Plot figures” immediately. However, the benefit simulation does not include these output files to reduce file size. If you wish to generate figures for the benefit simulation, please follow the instructions below to run the benefit simulation first, and then run “Plot figures.” In Experiment1, we use the term "benefit" to describe the conditions which include both reward volatility and the complexity of the state-action space (ll: M2, lh: L1, hh: M1, hl: H1). In merged data for analysis, the abbreviations of benefit conditions are the same as those used in the paper. Experimental task Our task code collects e-mail address of participants. This information records in raw data and e-mail csv files, so pay attention for protect of personal information. Instructions in the task is Japanese. Files in English version are in "english" folder. Preparation Making data for analysis: Data/MakingAnalysisData.py Data Analysis - Better choice rate analysis 1. Perform statistical tests - Exp1: Test/Exp1/BetterChoiceRate.py - Exp2: Test/Exp2/BetterChoiceRate.py 2. Plot figures - Exp1: Figure/FigureBetterExp1.py - Exp2: Figure/FigureBetterExp2.py - One-trial back analysis 1. Perform GLMM - Exp1: Test/Exp1/OneTrialBack/Glmm.jl - Exp2: Test/Exp2/OneTrialBack/Glmm.jl 2. Plot figures - Exp1: Figure/FigureDataOneExp1.py - Exp2: Figure/FigureDataOneExp2.py - Multi-trial back analysis 1. Perform GLM - Exp1: Test/Exp1/MultiTrialBack/Glm.jl - Exp2: Test/Exp2/MultiTrialBack/Glm.jl 2. Perform permutation test - Exp1: Test/Exp1/MultiTrialBack/Perm.py - Exp2: Test/Exp2/MultiTrialBack/Perm.py 3. Plot figures - Exp1: Figure/FigureDataMultiExp1.py - Exp2: Figure/FigureDataMultiExp2.py - Model-fitting analysis 1. Perform model-fitting - Exp1: Fit/MainHierarchicalExp1.R - Exp2: Fit/MainHierarchicalExp2.R 2. Perform statistical tests - Exp1: Test/Exp1/VolaCompParams.py, ITIParams.py - Exp2: Test/Exp2/VolatilityParams.py 3. Plot figures about parameters - Exp1: Figure/FigureParamExp1.py - Exp2: Figure/FigureParamExp2.py 4. Plot figures about fitness - Exp1: Figure/FigureFitnessExp1.py - Exp2: Figure/FigureFitnessExp2.py - Analysis of comparison RL simulation and experimental data 1. Perform model-fitting (If you haven't done it yet) 2. Generate simulation data - Simulation/Semi-model-dependent/SimMappingData.py 3. Perform GLMM - Simulation/Semi-model-dependent/SimMappingData.jl 4. Plot figure - Figure/FigureSemi.py - Statistical tests - Exp1 better choice rate: Test/Exp1/BetterChoiceRate.py - Exp1 one trial back: Test/Exp1/OneTrialBack/Glmm.jl - Exp1 multi trial back: Test/Exp1/MultiTrialBack/Glm.jl -> Perm.py - Exp1 parameters between ITI: Test/Exp1/ITIParams.py - Exp1 parameters between volatility and complexity: Test/Exp1/VolaCompParams.py - Exp2 better choice rate: Test/Exp2/BetterChoiceRate.py - Exp2 one trial back: Test/Exp2/OneTrialBack/Glmm.jl - Exp2 multi trial back: Test/Exp2/MultiTrialBack/Glm.jl -> Perm.py - Exp2 parameters between volatility: Test/Exp2/VolatilityParams.py Simulation - Theoretical minimum number of trials for learning the model of transition probability 1. Plot figure - Figure/FigureSTL.py - Simulation of One-trial back analysis 1. Generate Data - Simulation/Model-agnostic/Generate/DataGenerate.py 2. Perform GLMM - Exp1: Simulation/Model-agnostic/OneTrialBack/Exp1/Glmm.jl - Exp2: Simulation/Model-agnostic/OneTrialBack/Exp2/Glmm.jl 3. Plot figures - Exp1: Figure/FigureSimOneExp1.py - Exp2: Figure/FigureSimOneExp2.py - Simulation of Multi-trial back analysis 1. Generate Data - Simulation/Model-agnostic/Generate/DataGenerate.py (If you haven't done it yet) 2. Perform GLMM - Exp1: Simulation/Model-agnostic/MultiTrialBack/Exp1/Glmm.jl - Exp2: Simulation/Model-agnostic/MultiTrialBack/Exp2/Glmm.jl 3. Plot figures - Exp1: Figure/FigureSimMultiExp1.py - Exp2: Figure/FigureSimMultiExp2.py - Simulation of Pure model-free agents 1. Generate Data - Simulation/Model-agnostic/Generate/DataGenerate.py (If you haven't done it yet) 2. Perform GLMM - Exp1: Simulation/Model-agnostic/OneTrialBack/Exp1/Glmm.jl (If you haven't done it yet) - Exp2: Simulation/Model-agnostic/OneTrialBack/Exp2/Glmm.jl (If you haven't done it yet) 3. Plot figures - Exp1: Figure/FigurePureMFExp1.py - Exp2: Figure/FigurePureMFExp2.py - Simulation of the correlation between parameter and effect sizes 1. Generate Data - Simulation/Model-agnostic/Generate/DataGenerate.py (If you haven't done it yet) 2. Perform GLMM - Exp1: Simulation/Model-agnostic/OneTrialBack/Exp1/ParamsCoef.jl - Exp2: Simulation/Model-agnostic/OneTrialBack/Exp2/ParamsCoef.jl 3. Plot figures - Exp1: Figure/FigureParamsCoefExp1.py - Exp2: Figure/FigureParamsCoefExp2.py - Parameter recovery 1. Generate Data - Simulation/ParamRecov/Generate.py 2. Perform model-fitting - Simulation/ParamRecov/Fit.R 3. Plot figures - Figure/FigureParamRecov.py - Benefit 1. Generate Data - Exp1: Simulation/Benefit/Exp1/Exp1Choice1.py - Exp1: Simulation/Benefit/Exp1/Exp1Choice2.py - Exp2: Simulation/Benefit/Exp2/Exp2.py 2. Plot figures - Exp1: Figure/FigureBenefitExp1.py - Exp2: Figure/FigureBenefitExp2.py - Correlation between Fitness and Balance parameter 1. Perform model-fitting (If you haven't done it yet) 2. Plot figures - Figure/FigureCorrFitnessBalance.py Task - Task was coded using jsPsych. To execute task, open the html files. Exp1 - number between underbar means the condition sequence type. - number at the last (250/500) means iti condition. Exp2 - "bb" means a version in which black spaceship leads to blue state commonly. "bo" means a version in which black spaceship leads to orange state commonly. - "pattern" means the condition sequence type.