We re-analyzed pilot data and used joint modeling techniques integrating the Drift Diffusion Model (DDM) with CPP data, investigating whether CPP serves as a robust ERP marker of evidence accumulation across various perceptual decision-making tasks.
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├── 1_Preprocess_Data.py # Raw EEG/behavioral data preprocessing
├── 2_Extract_Feature_for_DDM.py # CPP feature extraction for DDM fitting
├── 3_Run_DDM_Models.py # Main DDM fitting script
├── model.py # DDM model specifications
├── 4_Check_DDM_Models_Result.py # Model diagnostics (e.g., convergence)
├── 5_Data_for_Two_Step.py # Prepares data for two-stage analysis
├── 5_Two_Step.Rmd # Implements two-stage correlation (DDM-CPP)
├── 6_Figure4a&s1.Rmd # Generates main figures (Fig4a & S1)
├── 7_Sensitivity_analysis_run_model.ipynb # Subject-level model robustness tests
├── 8_Sensitivity_analysis_load_model.ipynb # Sensitivity results comparison
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├── Multiverse_Model_Data/ # Temporary DDM results (multiverse CPP)
└── Sensitivity_Model_Data/ # Temporary DDM results (sensitivity analysis)