[](https://github.com/alpn-lab/BayesCog/graphs/contributors)

Further content from the [BayesCog workshop at UKE Hamburg](https://github.com/lei-zhang/BayesCog_Part1) (2023) have also been added.

**Outreach**: [**Twitter thread**](https://twitter.com/lei_zhang_lz/status/1276506555660275714?s=20) (being **liked 700+ times** on Twitter) summarizing the contents of the course.

Computational modeling and mathematical modeling provide an insightful quantitative framework that allows researchers to inspect latent processes and to understand hidden mechanisms. Hence, computational modeling has gained increasing attention in many areas of cognitive science through **cognitive modeling**. One illustration of this trend is the growing popularity of Bayesian approaches to cognitive modeling. To this end, this course teaches the theoretical and practical knowledge necessary to perform, evaluate and interpret Bayesian modeling analyses, with a specific emphasis towards modeling latent cognitive processes.

In the course, students will be formally grounded in key principles of cognitive modeling including Bayesian statistics, statistical distibrutions and reinforcement learning.

10.model_based | WIP | WIP

11.delay_discounting | WIP | WIP

There is no additional set-up needed if you aim to work solely from the website. However, to work on the exercises yourself, several software and packages are required.

The various analyses across the workshops require specific packages to be installed. This project uses `renv` to manage package dependencies.

After executing this command once, the project environment will **load automatically whenever you open the project,** for both project root and individual workshops.

This will mount the Docker image onto the BayesCog repository.

We welcome contributions regarding content or with fixing errors on the current website. Making and committing these changes is simple.

Assuming you have cloned the repository and have [Quarto](https://quarto.org/) installed, you can preview any changes that you have in the browser by running:

If you are fortunate enough to have admin access (i.e., are a member of the ALPN Lab), you can just commit the changes:

git add .

git commit -m "your commit message"

git push origin main

And once the changes have been pushed, re-build the website by running:

If you do not have admin privileges, you can commit the changes to the `main` branch by following the steps below:

git remote add upstream https://github.com/Alpn-Lab/BayesCog.git

git checkout -b your-feature-name

git add .

git commit -m "Describe your changes here"

git push origin your-feature-name

You will then need to go to your fork on GitHub, where you'll see a prompt to "Compare & pull request" for your new branch. Click that to create a pull request to suggest your changes to the `main` repository.

If approved, we will both add the changes and you to the 'Contributors' list below!

This course is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/deed.en).

You are free to share, copy, and redistribute the material in any medium or format. Additionally, you can adapt, remix, transform, and build upon the material for any purpose, including commercial use.

However, you must provide appropriate attribution, including credit to the original source, a link to the license, and an indication if changes were made. Furthermore, if you remix, transform, or build upon the material, you are required to distribute your contributions under the same license as the original.

[^1]: Bayes' photo credit: Burrsettles (2016). The three faces of Bayes. Slackprop.

left: "© Zhang & Sohail, 2025"

`05.regression_height_poly`* | Observed weight and height | Polynomial regression model

`10.model_based`* | WIP | WIP

`11.delay_discounting`* | WIP | WIP

*Workshops 5, 10 and 11 have yet to be added, so watch this space!

This course is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-sa/4.0/deed.en).

You are free to share, copy, and redistribute the material in any medium or format. Additionally, you can adapt, remix, transform, and build upon the material for any purpose, including commercial use.

However, you must provide appropriate attribution, including credit to the original source, a link to the license, and an indication if changes were made. Furthermore, if you remix, transform, or build upon the material, you are required to distribute your contributions under the same license as the original.

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