A zero-programming package for Bayesian causal inference model
Project description
BCI Toolbox is a Python implementation of the hierarchical Bayesian Causal Inference (BCI) model for multisensory research. BCI model is a statistical framework for understanding the causal relationships between sensory inputs and prior expectations of a common cause, which can account for human perception in a number of tasks, including temporal numerosity judgment (Shams et al., 2005; Wozny et al., 2008), spatial localization judgment (Körding et al., 2007; Wozny & Shams, 2011), size-weight illusion paradigm (Peters et al., 2016), rubber-hand illusion paradigm (Chancel et al., 2022; Chancel & Ehrsson, 2023).
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for bcitoolbox-0.0.1.9-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b406d4f7594259ddb850d976bcce79206a94f250e72212aa76aa411421d1fca5 |
|
MD5 | 7f1d1d277fac14a939ebb6322a4cd35e |
|
BLAKE2b-256 | d0adb3af7954b383cd700c3d56a872be2914027cdf4340a1aa25b5d0807cf96b |