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---
title: "Advanced Cognitive Modeling Notes"
author: "Riccardo Fusaroli"
date: "`r Sys.Date()`"
site: bookdown::bookdown_site
# output: bookdown::gitbook
documentclass: book
bibliography: [book.bib, packages.bib]
biblio-style: apalike
link-citations: yes
github-repo: openscapes/series
description: "My notes for the advanced cognitive modeling course - 2022"
---
# Advanced Cognitive Modeling
These are the teaching notes for Advanced Cognitive Modeling - taught in 2022 at the M.Sc. in Cognitive Science at Aarhus University.
1. The syllabus is available at URL
2. The videos are available at URL
3. A github repository with all the materials is available at URL
## The goal of the course
Advanced cognitive modeling is a course on how to think through, formalize and validate models of cognitive processes. In other words, we will be thinking about how people learn, and make decisions both in the lab and in the real world, and to robustly assess our hypothesized mechanisms.
The course has 3 interrelated aims:
to guide you through how models of cognitive processes are thought through and built (more than a toolbox of existing scripts);
to provide (or reinforce) a good Bayesian workflow (simulation, prior assessment, parameter/model recovery, model fit assessment) to build robust and reliable models;
to develop your probabilistic modeling skills (we will be dealing with brms, and also directly with stan).
At the end of the course, you should be able to start thinking about how to use your own theoretical knowledge in cognitive science to build your own models, as well as to robustly evaluate existing models and their applicability.
The course will be very hands-on. The main goal of the course is not just for you to understand how cognitive modeling works, but to build and use your own models. The lectures will include conceptual discussions of cognitive modeling and the specific models we will be dealing with, but also introduction to the coding exercises in the practical exercises (e.g. how to code in Stan). During the practical exercises, we will collect some data or explore existing datasets, design models together, and code them up: simulating how a person using those processes would perform, inferring parameters from simulated and real data, assessing model quality. We will take the time to do this together, and there will be time for lots of questions. The schedule for the course will therefore be somewhat flexible, and adaptive to your collective learning speed. See the planned schedule below.
## List of lectures and practical exercises
## Preparation before the course
Before starting the course, you need to get your computers and brains in ship-shape so we can focus on modeling!
In terms of computers, you need to make sure you have the following software installed and working:
* up-to-date R (version 4 or above) and Rstudio (version 1.3 or above) installed and working. See here for a more detailed instruction on how to install R and Rstudio: https://happygitwithr.com/install-r-rstudio.html
* the “brms” package installed: https://github.com/paul-buerkner/brms N.B. it’s not always as simple as doing an install.packages(“brms”), so do follow the linked guide!
* the “cmdstanr” package: https://mc-stan.org/cmdstanr/articles/cmdstanr.html N.B. it’s not always as simple as doing an install.packages(“cmdstanr”), so do follow the linked guide! N.B. technically you can run all our exercises without cmdstanr if it turns to be too demanding, but your computer will be much slower.
Without these packages working, you will not be able to tackle the practical exercises, so install them before you move to the next section and make sure there are no errors or worrying warnings.
Once your computer is ready, you should also get your brain ready.
This workshop focuses on how to do Bayesian data analysis and does not go into the details of Bayes’ theorem. If you are not familiar with the theorem or need a quick refresh, we strongly recommend you give this 15 min video a watch before the workshop. This should make talk of priors and posteriors much easier to parse. https://www.youtube.com/watch?v=HZGCoVF3YvM
This workshop does not cover basic R coding and basic statistical modeling, they are taken for granted. I know not everybody comes from the Bsc in Cognitive Science, so if you feel you need some practice:
* An amazing intro to R and the tidyverse (free online): https://r4ds.had.co.nz/ (I know some of you have also been referred to swirl and datacamp, I don't know those resources, so have a look at the one above to check you know enough)
* A intro to Bayesian statistics in brms (summarizing key points from methods 4 in the bachelor): https://4ccoxau.github.io/PriorsWorkshop/ videos + exercises.