Three-Box Foraging Behavioral Analysis

This is where I perform all my analyses of the behavioral data in the box foraging task. This repo is organized into the following sections:

1. data - this folder contains experiment data as well as any data structures generated by the code. Experiment data can be downloaded upon request and placed in data/experiments. Pre-existing data structures useful for analysis may be found under data/analysis.
2. docs - this folder contains useful documentation about the experiment, results, etc. Some of the files are directly generated from notebooks contained in notebooks.
3. figures - this folder is used to contain any figures generated by scripts.
4. notebooks - this folder contains jupyter notebooks that interface with the foraging package to render results and figures.
   • start with the Data Cleaning notebook first to get an overview of the data and how it's cleaned. Then proceed to the Data Exploration and then the Learning notebook.
5. src - this folder contains the foraging package, which you should install locally in your environment. See steps to setup below.

Dependencies

You can see the full list in the pyproject.toml under the dependencies field. There are also optional dev dependencies listed under project.optional-dependencies.

Setup

Environment

Here are a couple tested ways to quickly set up an environment

1. Unidep

With Unidep, you can install conda + pip dependencies in one line. It can even install the foraging project using pyproject.toml. Just run:

unidep install -n <YOUR_ENV> pyproject.toml

or

unidep install -e -n <YOUR_ENV> pyproject.toml

to install in editable mode.

2. Conda

Create a conda environment from environment.yml by running:

conda env create --name <YOUR_ENV> --file environment.yml

Note you'll still need to install the foraging package locally.

Install foraging

• This step assumes you have built a functional environment and are ready to install the package
• Cd to the root of this repo and just run:

pip install .

• To install in editable mode, where you may wish to edit the package files, run:

pip install -e .

Task

In the box foraging task, subjects aim to maximize their reward over time by interacting with boxes for reward. Boxes are placed equidistantly from each other in an arena, and reward arrives stochastically at each box according to that box's mean schedule. Depending on how many boxes there are, there can be a fast, medium, and slow box. Subjects also get observations in the form of a noisy color cue whose mean pixel value encodes the time until reward. Below is a schematic of the task.

Tricks and Tips

Some useful tricks and tips for this project are detailed here.

Generating HTML from notebook

After running the percent .py notebooks, you can use jupytext to convert them to whatever you like, including HTML. Personally, I like keeping a local .ipynb notebook that is paired with a percent.py notebook. To generate a .ipynb notebook from a percent py notebook, run:

jupytext --sync <notebook>.py

and convert the .ipynb to HTML without code by running:

jupyter nbconvert --to html <notebook>.ipynb --TemplateExporter.exclude_input=True

Pre-commit

If using precommit, I have found that the easiest way to avoid git staging hell is to run:

pre-commit run --all-files
git commit -am <your_message_here>

If you ever want to skip pre-commit, you can run:

git commit --no-verify -am <your_message_here>

Contact: [email protected]