BCI Toolbox: An open-source python package for the Bayesian causal inference model

Zhu, Haocheng; Beierholm, Ulrik; Shams, Ladan

Abstract

Psychological and neuroscientific research over the past two decades has shown that the Bayesian causal inference (BCI) is a potential unifying theory that can account for a wide range of perceptual and sensorimotor processes in humans. Therefore, we introduce the BCI Toolbox, a statistical and analytical tool in Python, enabling researchers to conveniently perform quantitative modeling and analysis of behavioral data. Additionally, we describe the algorithm of the BCI model and test its stability and reliability via parameter recovery. The present BCI toolbox offers a robust platform for BCI model implementation as well as a hands-on tool for learning and understanding the model, facilitating its widespread use and enabling researchers to delve into the data to uncover underlying cognitive mechanisms.

Citation

Zhu, H., Beierholm, U., & Shams, L. (2024). BCI Toolbox: An open-source python package for the Bayesian causal inference model. PLoS Computational Biology, 20(7), Article e1011791. https://doi.org/10.1371/journal.pcbi.1011791

• Journal Article Type: Article
• Acceptance Date: Jun 5, 2024
• Online Publication Date: Jul 8, 2024
• Publication Date: Jul 8, 2024
• Deposit Date: Jun 7, 2024
• Publicly Available Date: Jul 12, 2024
• Journal: PLoS Computational Biology
• Print ISSN: 1553-734X
• Electronic ISSN: 1553-7358
• Publisher: Public Library of Science
• Peer Reviewed: Peer Reviewed
• Volume: 20
• Issue: 7
• Article Number: e1011791
• DOI: https://doi.org/10.1371/journal.pcbi.1011791
• Public URL: https://durham-repository.worktribe.com/output/2475211