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

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  • Haocheng Zhu
  • Ulrik Beierholm
  • Ladan Shams

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.

Suggested Citation

  • Haocheng Zhu & Ulrik Beierholm & Ladan Shams, 2024. "BCI Toolbox: An open-source python package for the Bayesian causal inference model," PLOS Computational Biology, Public Library of Science, vol. 20(7), pages 1-12, July.
    • Handle: RePEc:plo:pcbi00:1011791
    DOI: 10.1371/journal.pcbi.1011791
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    References listed on IDEAS

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    1. Tim Rohe & Ann-Christine Ehlis & Uta Noppeney, 2019. "The neural dynamics of hierarchical Bayesian causal inference in multisensory perception," Nature Communications, Nature, vol. 10(1), pages 1-17, December.
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