From Computational Memory Lab
Revision as of 14:44, 21 June 2019 by Kahana
- PyEPL (the Python Experiment-Programming Library) is a library for coding psychology experiments in Python. It supports presentation of both visual and auditory stimuli, and supports both manual (keyboard/joystick) and sound (microphone) input as responses. Visit the PyEPL SourceForge page for more information and downloads, or click here for an updated installer, capable of working on El Capitan. (Methods paper can be found here.)
- PandaEPL is a cross-platform Python library for programming 3D spatial navigation experiments. (Methods paper can be found here.)
- UnityEPL is a library that interacts with the Unity VR software to facilitate creation of memory experiments. The code and documentation are posted to the following GitHub site.
PyEPL-based experiments used in the Kahana Lab.
- pyFGS: Face/Grating Sternberg task (tgz)
- pyFR: Free Recall task (tgz)
- YellowCab II: Virtual Driving task (tgz (58.3 MB))
- ycCross: YellowCab Variant (tgz (30.5 MB))
- ycMagellan: PandaEPL-based YellowCab variant, as used in Manning et al., submitted (experiment tgz (50.8 MB), buildings tgz (3.1 GB))
- Trackball: Blinking and eye-movement task (tgz)
- Testsync: Simple program to send sync pulses (tgz)
- Penn TotalRecall: score and annotate behavioral audio files (replaces PyParse)
- Behavioral Toolbox: a suite of MATLAB functions to aid in analyzing behavioral Free Recall data
- Python Behavioral Toolbox: our MATLAB Behavioral Toolbox has been ported into Python
- Our EEG Toolbox is a set of Matlab functions to help in analyzing EEG data.
- The latest public release can be downloaded here (zip). Current version is 1.3.2, last update June 25, 2008.
- Lab members and collaborators (e.g., members of the RAM team) should checkout the the most recent version from the lab’s SVN server (for instructions, see the internal wiki EEG Toolbox page)
- For documentation, please see the newest (January 14, 2015) tutorial for EEG analyses. Additionally, use the
helpfunction in MATLAB for assistance with individual functions.
- The Context Maintenance and Retrieval model (CMR).
Please see our CRP tutorial page.
Please see our Wavmark page.