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https://github.com/lacerbi/visvest-causinf
Bayesian comparison of causal inference strategies in multisensory heading perception
https://github.com/lacerbi/visvest-causinf
data modeling multisensory-integration visuo-vestibular-interaction
Last synced: 23 days ago
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Bayesian comparison of causal inference strategies in multisensory heading perception
- Host: GitHub
- URL: https://github.com/lacerbi/visvest-causinf
- Owner: lacerbi
- License: mit
- Created: 2016-05-23T20:10:36.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2018-10-31T05:52:00.000Z (about 6 years ago)
- Last Synced: 2024-03-29T21:20:25.483Z (8 months ago)
- Topics: data, modeling, multisensory-integration, visuo-vestibular-interaction
- Language: Matlab
- Homepage:
- Size: 46.2 MB
- Stars: 7
- Watchers: 4
- Forks: 2
- Open Issues: 1
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Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# Bayesian Comparison of Explicit and Implicit Causal Inference Strategies in Multisensory Heading Perception
This repository accompanies the manuscript by Acerbi et al. (2018), published in *PLoS Computational Biology* [[1](https://github.com/lacerbi/visvest-causinf/blob/master/README.md#reference)].
It includes human subjects' data and the code used for fitting and comparing the models reported in the paper.
## The repository
The original working name of the repository was `VestBMS` (standing for *Bayesian Model Selection of Vestibular experiment*), which appears as prefix of many functions and folders.
The structure of the repository is as follows:
- *Analytics*: Functions to analyze data and results of model fits.
- *ModelWork*: Core model fitting functions (to be used in conjunction with the [ModelWork](https://github.com/lacerbi/ModelWork) toolbox).
- *PlotFunctions*: Functions for plotting results.
- *data*: Raw data for each subject, in `.mat` form.
- *scripts*: Bash scripts used to submit jobs on the computer cluster (using [SLURM](https://slurm.schedmd.com/)).
- *supplement*: Files used to analyze data in [de Winkel et al. (2017)](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0169676), for a control analysis reported in the Supporting Information.
Please contact me at if you have any questions.
## Reference
1. Acerbi\*, L., Dokka\*, K., Angelaki, D. E. & Ma, W. J. (2018). Bayesian Comparison of Explicit and Implicit Causal Inference Strategies in Multisensory Heading Perception, *PLoS Computational Biology* 14(7): e1006110. (\*equal contribution; [link](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006110))
### License
This code is released under the terms of the [MIT License](https://github.com/lacerbi/visvest-causinf/blob/master/LICENSE.txt).