This folder contains the analysis scripts and the results of the study.Abbreviations used for the file or folder names are presented in red. <br>The analysis scripts were programmed with R within RStudio. These files are given in the folder "R scripts". <br>The data after merging and trimming are presented in the zip file called "datasets". This folder contains: - a subfolder called `logarcsin_meanRT_lc300_3SD` in which a logarithm transformation of the reaction times and an arcsine square root transformation of the error rates were applied. - a subfolder called `raw_meanRT_lc300_3SD` in which no data transformation was applied. In each subfolder, the data were aggregated for each task separately. Moreover, this aggregation occurred either across all trial types (`all`) or separately for odd and even trials (in order to compute the reliability estimates) (`parity`). <br>Results are given in the folder `Results`. In this folder, the analyses on raw data are presented (`raw`). Three different data transformations were used for the analyses: - residuals (`res`): - For the updating tasks, residuals were computed from a simple linear regression model predicting the RTs_{short CTI} from the RTs_{long CTI}. - For the number Stroop, arrow flanker, global-local, and Simon task, the residual score was calculated from a simple linear regression model predicting the RTs_incongruent from the RTs_congruent. - For the negative-compatibility task, the residual score was computed from a simple linear regression model predicting the RTs_congruent from the RTs_incongruent. - For the antisaccade task, the residual score was computed from a simple linear regression model predicting the RTs_antisaccade from the RTs_prosaccade - proportional scores (`pg`): - For the updating tasks, the proportional score was computed as [RTs_{short CTI} - RTs_{long CTI}] / RTs_{short CTI} - For the number Stroop, arrow flanker, global-local, and Simon task, the proportional score was computed as [RTs_incongruent – RTs_congruent] / RTs_incongruent - For the negative-compatibility task, the proportional score was computed as [RTs_congruent – RTs_incongruent] / RTs_congruent - For the antisaccade task, the proportional score was computed as [RTs_antisaccade – RTs_prosaccade] / RTs_antisaccade - bi-factor modeling approach (`bi`): For each construct (attentional control and removal), we estimated bi-factor models. - For removal, we fitted a bi-factor model in which performance from both CTI conditions was forced to load on a baseline factor, and performance from the short CTI condition was forced to load on a removal factor. - For attentional control, the bi-factor model was so fitted that performance on the antisaccade and prosaccade trials of the antisaccade task, and performance on incongruent and congruent trials of the number Stroop, arrow flanker, Simon, local and negative compatibility tasks, were forced to load on a baseline factor, and performance on antisaccade trials for the antisaccade task, on congruent trials for the negative compatibility task and on incongruent trials for all other tasks were forced to load on an attentional-control factor. Three different dependent measures were used for the analyses of attentional control: - the congruency effect (`eff1`): that is, the difference between incongruent and congruent trials for the number Stroop, arrow flanker, local, Simon and negative compatibility tasks, and the difference between antisaccade and prosaccade trials for the antisaccade task - the interference effect (`eff2`): that is, the difference between incongruent and neutral trials for the number Stroop, arrow flanker, and local tasks, and the difference between congruent and neutral trials for the negative-compatibility task - the facilitation effect (`eff3`): that is, the difference between congruent and neutral trials for the number Stroop, arrow flanker, and local tasks, and the difference between incongruent and neutral trials for the negative-compatibility task The analyses combining these different data transformations and dependent measures are presented in the subfolders of the folder `Results`. Each subfolder consists of: - a file named `corrmatrix` in which the correlations are saved in text format. - a file named `descriptives` in which the descriptive results (e.g., mean, SD, max, min) are saved in text format. - a file named `participantlist` consisting of the list of the participants used to compute the present results. This is saved in text format. - a file named `reliability` in which the reliability estimates are saved in text format. - a file named `sem` in which the the structural equation modeling results are saved as RDA-data. <br>When structural equation modeling was computed, overview files were created and saved in the folder `SEM_overview`. Each file contains information about the goodness-of-fit statistics, the dominance and significance of the loadings and error variances, the omega and the warnings. With these overview files, frequencies were computed and saved in the file named `frequencies_SEMresults`. <br>The models used to compute SEM are saved in the folder `models`. They are prepared to be used with lavaan. - In the subfolder named `bi`, the models using the bi-factor modeling approach are presented. - In the subfolder named `res_pg`, the models using residuals anad proportional scores are presented. These folders contain the following files: - `inhibition_eff12`: models for the attentional-control construct with the congruency effect and interference effect as dependent measures - `inhibition_eff13`: models for the attentional-control construct with the facilitation effect as dependent measures - `removal`: models for the removal construct - `removal_inhibitiontask`: models in which each attentional-control measure was the predictor of the removal factor <br>The results reported in the article are presented in the folder called `raw_res_eff1_withoutMultiOut`.