I would like to announce the beta release of Bayesian Causal Inference toolbox (BCIT), developed at the UCLA Multisensory Perception Lab:
BCIT is a MATLAB toolbox developed for learning and using the Bayesian Causal Inference Model of multisensory perception. This NSF-sponsored toolbox was developed for two aims: a) serving as a pedagogical tool for learning and understanding how the model works, and b) for adapting and using the model to explain experimental data in a variety of experimental paradigms. The toolbox is designed for use by researchers of any background, and does not require any computational training or skills.
This is a beta release and we welcome feedback on the existing functionality, as well as suggestions for additional functionality to be added in future releases. Please email the lead developer Dr. Majed Samad (firstname.lastname@example.org) or the assistant developer Kellienne Sita (email@example.com) with any questions. Please cc me (firstname.lastname@example.org) on your emails.
We look forward to your feedback.
Acknowledgement: The development of this toolbox was sponsored by NSF grant BCS-1057969.
Directions for downloading the toolbox:
- Go to: https://github.com/multisensoryperceptionlab/BCIT
- Click the green button labelled “Clone or Download”
- From the dropdown menu select “Download ZIP” to download a folder containing all of the files to the toolbox called “BCIT-Master”
- Read the brief introduction contained in the README file within the repository.
- For further explanations regarding the toolbox and its components please consult the documentation provided under the “Documentation” folder.