Source code for research article:
"To integrate or not to integrate: Temporal dynamics of hierarchical Bayesian Causal Inference"

By Máté Aller and Uta Noppeney

Published in Plos Biology

Cite: Aller M, Noppeney U (2019) To integrate or not to integrate: Temporal dynamics of hierarchical Bayesian causal inference. PLOS Biology 17(4): e3000210. https://doi.org/10.1371/journal.pbio.3000210

Description

• Class bci contains the methods for fitting Bayesian Causal Inference models to behavioural and EEG data
• Class mvpa contains the methods for performing Support Vector Regression and related statistics on EEG data
• Class mvpares handles the mvpa result data and plotting as an mvpares object.
• The rest of the functions are for running the behavioural analysis, preprocessing, mvpa, bci, TF-analysis, statistics and figure drawing, and some auxiliary functions.

Dependencies

Code is written in MATLAB, tested on MATLAB R2016a.

1. FieldTrip
2. SPM12
3. libsvm
4. CircStat
5. MathWorks File Exchange functions
   • barwitherr
   • consolidator
   • fminsearchbnd
   • jheapcl
   • numSubplots
   • parfor_progress
   • redblue
   • shadedErrorBar
   • sort_nat
   • suplabel

Contributors

The code was mainly written by Máté Aller. Ulrik Beierholm contributed the Bayesian Causal Inference fitting code, Tim Rohe contributed the code for log-likelihood ratio statistic on circular indices (circ_AWTest.m).