Patches papers

Author: Eitan Hemed

This repository contains the code and data associated with the following pre-prints:

1. [Hemed, E., Bakbani Elkayam, S., Teodorescu, A., Yona, L., & Eitam, B. (2022). Evaluation of an Action’s Effectiveness by the Motor System in a Dynamic Environment: Amended.](Manuscripts/Revisited MS.pdf)

2. [Hemed, E., & Eitam, B. (2022). Control feedback increases response speed independently of the feedback’s goal- and task-relevance. ](Manuscripts/Relevance MS.pdf)

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Repository's Structure

The repository structure is as follows:

1. Manuscripts - the preprints.
2. Code - the code and data to reproduce the analyses associated with the papers.

The relevant pre-registrations are available here.

Setup

To run the pipeline you can either choose between running it using docker, or using a local installation.

Docker

First, follow this guide to install Docker on your system: https://docs.docker.com/get-docker/

After setting up Docker on your system, pull the image from Docker Hub by running the following command in the terminal:

docker pull eitanhemed/patches-papers:latest

Then, run the following command to start the container, which will also launch up a Jupyter server:

docker run -p 8888:8888 eitanhemed/patches-papers

Once the Jupyter server is up, you can access it by opening the following link in your browser, for example by going to the terminal and clicking on the link: http://127.0.0.1:8888/tree, or any other link that appears in your terminal. Your entrypoint will be a jupyter notebook, allowing you to explore the data and output, edit the project code, etc.

Local installation

You will need to install a few dependencies. The best option is to do it on a new conda environment, as follows:

conda create -n po_utils_env python=3.9.12
conda activate po_utils_env
conda install -c conda-forge r-base=4 -y
cd Code
pip install .

Note that installing robusta involves setting up R on your system. The first session in which robusta is imported will require R to install many packages. The first time you import robusta the dependencies installation process can take a few minutes on Windows, and about 10-15 minutes on Linux.

Usage

Regardless of setting up locally or via Docker, using the project environment, run python run_all.py from the Code directory.

FAQ

I want to analyze the data using something different from robusta ( R, SPSS, etc.). What are my options?

Your best option is to use the data exported during any of the preprocessing stages (e.g., Code/Experiments/relevance/e1/Output/amended/13b9435ca5add3409d7fb2cbc6f836a0/Data/Data/pre_aggregation.csv)

The wide-format dataframe found under the output data directory was used to compare the results of the pipeline to the results given by JASP.

How to change the screening parameters? (e.g., minimum valid response time, proportion of allowed invalid trials)

Edit po_utils.constants.SCREENING_PARAMS, before running the pipeline.

Each unique combination of screening parameters is hashed, so the output of a set of screening parameters will be saved under the respective output directory (e.g., Code/Experiments/relevance/e1/Output/amended/13b9435ca5add3409d7fb2cbc6f836a0) .