Name: Haiyang Jin
Type: User
Company: Zhejiang Sci-Tech University
Bio: Face perception; vision science;
fMRI; EEG/ERP;
Linear mixed model; Bayesian hierarchical model. Bayesian meta-analysis with selection models
Twitter: Haiyang_J
Location: Hangzhou, Zhejiang, China
Blog: haiyangjin.github.io
Haiyang Jin's Projects
This repository stores the analysis and other materials for "two faces of holistic face processing" paper.
Here save the Stan codes I tried to build the Bayesian meta-analysis with selection models (to deal with publication bias).
A lightweight multivariate pattern analysis (MVPA) toolbox in Matlab / Octave
Create GitHub Resource Download Link (git-github-direct-zip-directory-folder-file)
Matlab scripts to analyze ERP data
This is the code used for the Face Word Project.
Matlab Wrapper of Surface-based Analysis. Custom Matlab functions for running analyses with FreeSurfer, CoSMoMVPA, HCP pipeline (and SamSrf)...
Backup for .gitignore used in my repositories.
Processing pipelines for the HCP
A collection of R functions facilitating fitting models (e.g., `lmer` and `brm`) in HPC (high-performance computing).
Some personal notes for using Julia.
Mixed-effects models in R using S4 classes and methods with RcppEigen
Example codes to analyze the composite face task with generalized linear mixed-effect modeling.
Data Analysis for "All-or-none neural mechanisms underlying visual categorisation: evidence of the N170".
Data analysis for the paper titled "Holistic face processing is influenced by non-conscious visual information".
Custom codes for building psychological experiments based on PsychoToolbox.
This tutorial (briefly) introduces the long and wide format of tidied up data and employs a concrete example to convert raw data into the desired long and wide formats.
This repository stores the documents for the rethink ANOVA paper.
a Matlab / Octave toolbox for surface-based voxel neighborhood selection on the cerebral cortex, intended for informationg mapping of functional magnetic resonance imaging (fMRI) data