Publication information

These data are associated with the publication:

Adam, K.C.S., Patel, T., Rangan, N. & Serences, J.T. (2021) Classic visual search effects in an additional singleton task: An open dataset. Journal of Cognition

These data are free for re-use for other researchers. If these data end up being useful to you (e.g., planning power analyses, conference presentation, new re-analysis publication), we would really appreciate hearing back from you! Contact: Kirsten Adam, kadam@ucsd.edu

Technical specifications used for original analysis

Data were collected using a Linux machine running Ubuntu 16.04 and Psychtoolbox. Data were analyzed in Matlab R2018A on an iMac (Mac OS 10.13.6), in JASP (0.13.1) and in an Anaconda Python 3.6.5 distribution with JupyterLab.

Overview of components

Each component includes a read-me file (e.g., a data dictionary, helpful tips) that should be useful for navigating the files. The README files are in a markdown format (.md). These can be viewed directly within OSF by clicking on the file name. They can also be opened with any text editor of your choice, or a mark-down specific editor.

Data

Link to Data README file: https://osf.io/sqn8d/

• Aggregate files (csv long): Long-format aggregate CSV files that are useful for general-purpose analysis (e.g., Python pandas, R). An example Python analysis using these files is provided in Analysis/Python.
• Aggregate files (csv wide): Wide-format aggregate CSV files that are useful for general-purpose analysis (e.g., JASP, SPSS). An example JASP analysis using these files is provided in Analysis/JASP.
• Aggregate files (mat): Aggregate data files for use with MATLAB. An example Matlab analysis using these files is provided in Analysis/MATLAB.
• Individual files (csv): Long format CSV files for individual participants' data (1 file per participant; files can be concatenated across experiments)
• Raw data (mat): The raw data stored in a Matlab format (.mat). These files were used to generate the CSV files (see Analysis/Make_CSV_files and to do the original analysis in MATLAB).

Analysis

Link to Analysis README file: https://osf.io/nk4m8/

JASP

• These .jasp format files can be used with the statistical program JASP. When you open the file, you'll see the aggregate wide format data and the results of repeated measures ANOVA. There is 1 file per sub-experiment (e.g., Search_1a), as well as a concatenated file for each full experimetn (e.g., Search_1abcd)

MATLAB

• Accessory_files: Files that need to be on the path in order for some analysis and plotting scripts to function properly.
• Combine_raw_data: Generate the aggregate .mat files starting from the raw .mat files for individual participants.s
• Make_CSV_Files: Make .csv files from aggregate and individual .mat files in order to allow flexible analysis in other platforms.
• Plot_data: Plot the data using MATLAB.

Python

• These python notebooks (.ipynb) can be used to plot the long-format aggregate data using pandas and seaborn and to run a repeated-measures ANOVA for each sub-experiment.

Task Code

Link to Task Code README file: https://osf.io/b359k/

• This component contains the Psychtooblox code used to present the stimuli to the participants. The code for each sub-experiment is in a separate .zip folder.