# Overview and organization of this project This page contains data associated with the publication: Adam, K.C.S., & Vogel, E.K. (2018) Improvements to visual working memory performance with practice and feedback. *PLOS ONE* **Data citation:** See citation list in the top-right corner of the main project page. **Usage:** If you would like to use the data in published work, please cite both the paper and OSF data set. We analyzed the data in [MATLAB][1]. To run the analysis scripts and psychtoolbox code, you will need access to MATLAB. Unfortunately, MATLAB requires a paid license. An open-source alternative to Matlab is [GNU Octave][2]. This open source alternative should work well, but keep in mind that code has not been tested for compatibility with Octave. For any further questions or comments, don't hesitate to email me: kadam1@uchicago.edu **Note on Markdown files:** If you are not familiar, `.md` files are "markdown" formatted text files. These are just text files that are interpreted by OSF / a program to render nice formatting when you view them. You can open `.md` files just fine in a plain-text editor of your choice, but they will not be formatted in a "pretty" way. To view and write markdown files in their pretty format, you will need to download a "mark down interpreter" (e.g. <https://macdown.uranusjr.com/> for Mac OS ; <https://www.ossblog.org/markdown-editors/>). ## Status As of 24 June 2018 this page contains the Task Code and Materials, the raw data for individual participants (`.mat`), aggregate data files used for the main analyses (`CSV` & `.mat`), and the main analysis scripts (`.m`). ## Task Code and Materials This component contains the Matlab code used to present the stimuli to participants, as well as copies of paper/pencil questionnaires administered to participants. To use the Psychtoolbox code, you need to have Matlab installed and to download and install the Psychophysics toolbox (<http://psychtoolbox.org/>). To run an experiment, make sure that you place the whole folder in the path. Navigate to the folder so it is the current directory, and hit "play" on the main experiment script (e.g. `Run_Experiment.m`) These experiments were run on Dell PC's running Windows XP. Some tinkering may need to be done with the code to make it forward-compatible with Windows 7/10 or Mac OS! Folders: * **Crossword puzzles** (pen and paper) * **Questionnaires and Instructions** (pen and paper) * **TaskCode_PostTest.zip** (zip of matlab tasks for post test) * **TaskCode_Practice.zip** (zip of matlab tasks for practice sessions) * **TaskCode_PreTest.zip** (zip of matlab tasks for pre test) ## Data These components contain the Matlab files that were collected for individual subjects. Data for each individual is stored in a matlab structure with different fields for each variable. For more information on the meaning of each variable, see the separate "data guide" file within each experiment folder. When applicable, this file provides a "dictionary" of the variables that were saved in individual Matlab files. Folders and sub-folders: * **Aggregate data files:** Aggregate files for each task used to analyze and plot data (Matlab files, ".mat") * **Individual data files:** All variables and raw data for each individual subject. Each task has a separte folder with the individual subject files. These folders have sub-folders for pre-test, post-test, and training sessions (if applicable). * **Antisaccade** (.mat) * **Color change detection** (.mat) * **Color whole-report** (.mat) * **Crossword puzzles** (Pen and paper, photocopies) * **Orientation whole-report** (.mat) * **Questionnaires** (Pen and paper, photocopies) * **Raven's advanced progressive matrices** (.mat) * **Visual search** (.mat) ## Analysis This component contains Matlab scripts for generating all the plots found in the paper from the aggregate data * **Analyze_PostTest**: Generate aggregate files for post-test data * **Analyze_Practice**: Generate aggregate files for 6 practice sessions * **Analyze_PreTest**: Generate aggregate files for pre-test data * **Make_CSVs** Make some CSVs from the aggregate files * **Plot_all_data**: Make graphs from the aggregate matlab files [1]: https://www.mathworks.com/ [2]: https://www.gnu.org/software/octave/