MixedMotionCueingEffects is an experiment program for a study exploring whether the attentional phenomenon of 'inhibition of return' (IOR) is due to inhibition for locations in space or inhibition for objects. In this experiment, target placeholder boxes can either be stationary or rotate 90° either clockwise or counterclockwise following a cue stimulus, and targets can appear at any one of the four (top, bottom, left, or right) locations.
The experiment also compares performance in the different motion conditions between when participants are asked to respond to targets by making saccades towards them (the saccade condition) and when participants are asked to make a speeded detection response for them via the keyboard without moving their eyes (the keypress condition).
NOTE: This experiment program has been slightly modified from its original version. Specifically, it has been modified to ask participants their gender instead of their sex during demographics collection. The original code can still be examined and downloaded here.
MixedMotionCueingEffects is programmed in Python 2.7 using the KLibs framework. It has been developed and tested on macOS (10.9 through 10.13), but should also work with minimal hassle on computers running Ubuntu or Debian Linux. It is not currently compatible with any version of Windows, nor will it run under the Windows Subsystem for Linux.
The experiment is designed to be run with an EyeLink eye tracker, but it can be downloaded and tested without one (using the mouse cursor as a stand-in for gaze position) by adding the flag -ELx to the klibs run command.
First, you will need to install the KLibs framework by following the instructions here.
Then, you can then download and install the experiment program with the following commands (replacing ~/Downloads with the path to the folder where you would like to put the program folder):
cd ~/Downloads
git clone https://github.com/TheKleinLab/MixedMotionCueingEffects.git
If you intend to run this experiment with an EyeLink eye tracker, you will also need to install the EyeLink Developer's Kit for your OS (available on the SR Support forums here) and make sure the 'pylink' module has been properly installed.
MixedMotionCueingEffects is a KLibs experiment, meaning that it is run using the klibs command at the terminal (running the 'experiment.py' file using python directly will not work).
To run the experiment, navigate to the MixedMotionCueingEffects folder in Terminal and run klibs run [screensize],
replacing [screensize] with the diagonal size of your display in inches (e.g. klibs run 24 for a 24-inch monitor). If you just want to test the program out for yourself and skip demographics collection, you can add the -d flag to the end of the command to launch the experiment in development mode.
In the experiment, participants are placed into one of two response conditions: the keypress conditon, where they respond to targets using the space bar while keeping their eyes in the middle of the screen, or the saccade condition, where they respond to targets by making saccades towards them.
To choose which condition to run, launch the experiment with the --condition or -c flag, followed by either keypress or saccade. For example, if you wanted to run the saccade response condition on a computer with a 19-inch monitor, you would run
klibs run 19 --condition saccade
If no condition is manually specified, the experiment program defaults to running the keypress response condition.
To export data from MixedMotionCueingEffects, simply run
klibs export
while in the MixedMotionCueingEffects directory. This will export the trial data for each participant into individual tab-separated text files in the project's ExpAssets/Data subfolder. If you want to export the trial error data (a log of the trials on which participants made an error that caused the trial to be recycled) or the saccade data (the saccades that participants made on each trial), you can similarly export those using
klibs export -t trials_err # for trial error data
klibs export -t saccades # for saccade data
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent