ACTRTutorials.jl

A set of tutorials for building likelihood based models in ACT-R
Author itsdfish
Popularity
5 Stars
Updated Last
12 Months Ago
Started In
June 2022

ACTRTutorials.jl

ACTRTutorials.jl is a collection of tutorials for developing ACT-R models within a likelihood framework.

Key Features

  1. Tutorials are written in interactive Jupyter notebooks with integrated code and annotation in HMTL and LaTeX markdown.

  2. The collection of model tutorials spans declarative memory, procedural memory, visual search, and ranges in difficulty from simple to advanced.

  3. The collection includes background tutorials on mathematical notation, Bayesian inference, MCMC sampling, the Julia programming language, supporting software libraries and more.

  4. Additional tutorials cover approximate likelihood methods and Bayesian adaptive design optimization.

Installation

Please follow the steps below to install the tutorial.

1. Download Julia

Install the latest stable version of Julia from the following download page:

https://julialang.org/downloads/

2. Launch Julia

Launch Julia. Most operating systems will create a shortcut in your programs or apps folder.

3. Open Pluto

In the REPL (command line), type the following:

using Pluto

As shown below, if Pluto has not been installed, you will need to type y to install once prompted.

julia> using Pluto
 │ Package Pluto not found, but a package named Pluto is available from a registry. 
 │ Install package?
 │   (@v1.7) pkg> add Pluto 
 └ (y/n) [y]: 

4. Launch the notebook

Once Pluto is installed and loaded, type the following to launch the notebook in your browser:

Pluto.run()

5. Select a Tutorial

The main page for Pluto features a text field labeled Open from file: where you can type the directory in which the tutorial is location. Once you have reached the main folder of the tutorial, navigate to /Table_Of_Contents/Table_Of_Contents.jl. and press enter or click open. This will open a table of contents with hyperlinks to specific tutorials.

Bug Reporting

If you encounter a bug or a problem during installation, please report the following information when applicable:

  1. A brief discription of the problem.

  2. A description of the expected behavior.

  3. An error message if available.

  4. The version of your operating system, Julia, and package version information. Package version information can be found in the package mode with the command:

    ] status

  5. A minimal reproducable example if possible.

Citing This Tutorial

Please use the citation below to cite the tutorial.

APA CITATION

Houpt, J. W., Fisher, C. R., & Gunzelmann, G. (2022, July). Developing analytic likelihood functions for ACT-R [Workshop]. 20th International Conference on Cognitive Modeling (ICCM), Toronto, ON, Canada.

bib tex

@inproceedings{houpt2022actr,
  author = {Houpt, Joseph W., Fisher, Christopher R., and Gunzelmann, Glenn},
  title = {{Developing analytic likelihood functions for ACT-R}},
  year = {2022},
  maintitle = {{20th International Conference on Cognitive Modeling (ICCM)}},
  howpublished = "\url{https://github.com/itsdfish/ACTRTutorials.jl}"
}

Related Paper

@article{fisher2022fundamental,
  title={Fundamental tools for developing likelihood functions within {ACT-R}},
  author={Fisher, Christopher R and Houpt, Joseph W and Gunzelmann, Glenn},
  journal={Journal of Mathematical Psychology},
  volume={107},
  pages={102636},
  year={2022},
  publisher={Elsevier},
  doi = {https://doi.org/10.1016/j.jmp.2021.102636},
  url = {https://www.sciencedirect.com/science/article/pii/S0022249621000997},
  abstract = {Likelihood functions are an integral component of statistical approaches to parameter estimation and model evaluation. However, likelihood functions are rarely used in cognitive architectures due, in part, to challenges in their derivation, and the lack of accessible tutorials. In this tutorial, we present fundamental concepts and tools for developing analytic likelihood functions for the ACT-R cognitive architecture. These tools are based on statistical concepts such as serial vs. parallel process, convolution, minimum/maximum processing time, and mixtures. Importantly, these statistical concepts are highly composable, allowing them to be combined to form likelihood functions for many models. We demonstrate how to apply these tools within the context of Bayesian parameter estimation using five models taken from the standard ACT-R tutorial. Although the tutorial focuses on ACT-R due to its prevalence, the concepts covered within the tutorial are applicable to other cognitive architectures.}
}

DISTRIBUTION A. Cleared for public release, distribution unlimited (AFRL-2022-3018)