The exercise paradox: Avoiding physical inactivity stimuli requires higher response inhibition

Dataset related to the paper on Response inhibition to physical inactivity stimuli using go/no-go tasks. 

This dataset includes:

1) A codebook (including the name of the main variables)

--> "code_book_Go_noGo_Miller.xlsx"

2) Raw data of the behavioral outcomes (i.e., reaction times) of the affective go/no-go task

--> "corrected.behavioral.data.csv"

--> "correct_Order.csv"

3) Self-reported data 

--> "Self_report_data.csv"

4) R script for the data management (i.e., from the raw data to data ready to be analyzed)

--> "Data_management_Self_report_go_no_go_Miller.R" for the self-reported data (return the file: "Data_SR_final.RData")

--> "Data_management_behav_go_no_go_Miller.R" for the behavioral outcomes (return the file: "Data_GNG_behav.RData")

--> Data ready to be analyzed "Data_GNG_final_all.RData"

6) Eprime script for the affective go/no-go task ("Go_no_go_task.zip")

--> Images depicting physical activity and physical inactivity stimuli were kindly Share by Kullmann et al. (2014)

7) R script for the models tested

--> "Models_GoNogo_Miller_VZenodo.R"

本数据集对应一篇采用Go/No-Go任务（go/no-go task）探究身体不活动刺激反应抑制的研究论文。

本数据集包含以下内容：
1) 编码手册（含核心变量名称）：`code_book_Go_noGo_Miller.xlsx`
2) 情感型Go/No-Go任务的行为结果原始数据（即反应时数据）：
- `corrected.behavioral.data.csv`
- `correct_Order.csv`
3) 自我报告数据：`Self_report_data.csv`
4) 用于数据管理的R脚本（可实现从原始数据到待分析数据的转换流程）：
- 针对自我报告数据的脚本：`Data_management_Self_report_go_no_go_Miller.R`，输出文件为`Data_SR_final.RData`
- 针对行为结果数据的脚本：`Data_management_behav_go_no_go_Miller.R`，输出文件为`Data_GNG_behav.RData`
- 最终待分析数据集：`Data_GNG_final_all.RData`
6) 情感型Go/No-Go任务的Eprime脚本：`Go_no_go_task.zip`
- 其中用于呈现身体活动与身体不活动刺激的图像由Kullmann等人（2014）友情提供。
7) 用于所测试模型的R脚本：`Models_GoNogo_Miller_VZenodo.R`