The overlooked role of unisensory precision in multisensory research

Zhu, Haocheng; Beierholm, Ulrik; Shams, Ladan

doi:10.1016/j.cub.2024.01.057

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2024

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“…Therefore, we will continue to explore additional possible methods of quantifying the error which may yield better fits. Updates to the BCI Toolbox and its documentation will be provided in due course to reflect these advancements [ 25 ].…”

Section: Availability and Future Directionsmentioning

confidence: 99%

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

Zhu,

Beierholm,

Shams

2024

PLoS Comput Biol

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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.

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Section: Availability and Future Directionsmentioning

confidence: 99%