Good me Bad me

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This is the project files for Chuan-Peng Hu et al (2020):https://doi.org/10.1525/collabra.301.

This open repo include scripts for the procedure and analysis script and data for two experiments.

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structure of the current folder:

moralSelf_ddm
│   README.md
│
└───1_pilot_study
│   │
│   └───Procedure
│   │   │
│   │   └───procedure_final    # matlab code for procedure
│   │   
│   └───Results
│       │
│       └───1_preproc          # R code for preprocessing data and plots
│       │
│       └───2_trad_analysis    # ANOVA results in JASP
│       │
│       └───3_exGaussian       # ex-Gaussian analysis of categorization task
│       │
│       └───4_hddm             # hddm analysis, only python scripts (.py, .ipynb)
│
└───2_confirm_study
│   │   MS_rep_categ_sample_size.jasp  # jasp used for determine whether or not stop collecting data
│   │   README.md
│   │
│   └───Pre-registration
│   │
│   └───Procedure
│   │   │
│   │   └───confirmStudy_proc  # matlab code for procedure
│   │   
│   └───Results
│       │
│       └───1_preproc          # R code for preprocessing data and plots
│       │
│       └───2_trad_analysis    # ANOVA results in JASP
│       │
│       └───3_hddm             # hddm analysis in python and jupyter notebook
│
└───3_manuscript
        MoralSelf_pilot_design.docx
        ...

Pilot Study

Preprocessing:

clean rawdata -> preproc_pilot.r -> summary data for JASP analysis

Input:

• MS_matchTask_raw.csv

• MS_categTask_raw.csv

Output:

• MS_match_behav_wide.csv # in folder "2_trad_analysis", for trad analysis in JASP

• MS_categ_behav_wide.csv # in folder "2_trad_analysis", for trad analysis in JASP

• MS_categ_behav_noTask_wide.csv # in folder "2_trad_analysis", for trad analysis in JASP

• MS_categ_exG.csv # in folder "3_exGaussian"", for ex-Gaussian analysis

• MS_match_hddm.csv # in folder "4_hddm", for HDDM analysis

• MS_mismatch_hddm.csv # in folder "4_hddm", for HDDM analysis

• MS_categ_id_hddm_stim.csv # in folder "4_hddm", for HDDM analysis

• MS_categ_val_hddm_stim # in folder "4_hddm", for HDDM analysis

• plots

Tranditional analysis (NHST & BF)

Using the wide-format output from preprocessing as the input to JASP

Input:

• MS_match_behav_wide.csv

• MS_categ_behav_wide.csv

• MS_categ_behav_noTask_wide.csv

Output:

• MS_match_behav_wide.jasp

• MS_categ_behav_wide.jasp

• MS_categ_behav_noTask_wide.jasp

Ex-Gaussian analysis

data (input) -> ex-Gaussaion analysis (R script) -> parameters estimation for each participant (output csv files) -> JASP

script: Ex_Gaussian_analysis.R (dependency: Initial_exgaussian.r). Read the comments in the R script, it include more than just what presented in the results part ;-).

Input:

• MS_categ_exG.csv

Output:

• exGaussian_params_mu_w.csv (parameter mu in wide format)

• exGaussian_params_sigma_w.csv (parameter sigma in wide format)

• exGaussian_params_tau_w.csv (parameter tau in wide format)

• exGaussian_params_sigma_noTask_w.csv

These output csv files were then taken as input for JASP, get the following statistical files:

• ExGaussian_params_wide_sigma.jasp

• ExGaussian_params_wide_tau.jasp

• ExGaussian_params_wide_mu.jasp

• ExGaussian_params_wide_sigma_noTask.jasp

HDDM analysis

Note 1: results from these analysis were not reported in the manuscript, but the results and code were nonetheless presented here.

Note 2: install and run HDDM is not always straight forward, especially if HDDM is the first python package you ever use. I've written a brief tutorial about how to install and run HDDM on my blog, using the current data and script as example. Please check here: http://www.huchuanpeng.com/2019/04/29/how-to-install-and-use-hddm/.

data (input) -> HDDM analysis (python & juypter notebook script)

Scripts:

• Exp_MS_HDDM_match.py

• Exp_MS_HDDM_categ.py

• Exp_categ_stim.ipynb

Input:

• MS_match_hddm.csv # for accuracy-coding modeling

• MS_mismatch_hddm.csv # for accuracy-coding modeling

• MS_categ_id_hddm.csv # for accuracy-coding modeling

• MS_categ_val_hddm.csv # for accuracy-coding modeling

• MS_categ_val_hddm_stim.csv # for response-coding modeling

• MS_categ_id_hddm_stim.csv # for response-coding modeling

Confirmative Study: a preregistered study

The confirmative study basically has the similar scripts

Preprocessing:

clean rawdata -> MS_rep_preproc.r -> summary data for JASP analysis

Input:

• MS_rep_matchingTask_raw.csv

• MS_rep_categTask_raw.csv

Main output:

• MS_match_behav_wide.csv # in 2_trad_analysis, for trad analysis in JASP

• MS_categ_behav_wide.csv # in 2_trad_analysis, for trad analysis in JASP

• MS_cross_taskeffect_wide.csv # in 2_trad_analysis, for cross task correlation analysis

• MS_match_hddm_stim.csv # in 3_hddm, for HDDM analysis

• MS_categ_id_hddm_stim.csv # in 3_hddm, for HDDM analysis

• MS_categ_val_hddm_stim # in 3_hddm, for HDDM analysis

• plots # in 2_trad_analysis

Tranditional analysis (NHST & BF)

Using the wide-format output from preprocessing as the input to JASP

Input:

• MS_match_behav_wide.csv

• MS_categ_behav_wide.csv

• MS_categ_behav_noTask_wide.csv

Output:

• MS_match_behav_wide.jasp

• MS_categ_behav_wide.jasp

• MS_categ_behav_noTask_wide.jasp

HDDM analysis

Note: We reported the results from hddm analysis with sitmulus coding approach, but I also remained the python script for accuracy-coding.

data (input) -> HDDM analysis (python & juypter notebook script)

Scripts:

• HDDM_MS_Rep_match_stim.ipynb

• HDDM_MS_Rep_categ_stim.ipynb

Input:

• MS_match_hddm.csv # for accuracy-coding modeling

• MS_mismatch_hddm.csv # for accuracy-coding modeling

• MS_categ_id_hddm.csv # for accuracy-coding modeling

• MS_categ_val_hddm.csv # for accuracy-coding modeling

• MS_categ_val_hddm_stim.csv # for response-coding modeling

• MS_categ_id_hddm_stim.csv # for response-coding modeling