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https://github.com/contextlab/mental-time-travel-paper

Code for exploring how we distribute our thoughts over time when we remember, using data from a naturalistic memory experiment.

https://github.com/contextlab/mental-time-travel-paper

Science Score: 13.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
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    DOI references
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    Academic publication links
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    JOSS paper metadata
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    Scientific vocabulary similarity
    Low similarity (11.4%) to scientific vocabulary

Keywords

analysis computational-psychology episodic-memory experimental-data experimental-data-analysis preprint theory
Last synced: 5 months ago · JSON representation

Repository

Code for exploring how we distribute our thoughts over time when we remember, using data from a naturalistic memory experiment.

Basic Info
  • Host: GitHub
  • Owner: ContextLab
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: master
  • Homepage: http://www.context-lab.com
  • Size: 287 MB
Statistics
  • Stars: 6
  • Watchers: 3
  • Forks: 1
  • Open Issues: 0
  • Releases: 3
Topics
analysis computational-psychology episodic-memory experimental-data experimental-data-analysis preprint theory
Created over 7 years ago · Last pushed about 5 years ago
Metadata Files
Readme License

README.md

Episodic memory: mental time travel or quantum 'memory wave' function?

This repository contains data and code used to produce the paper "Episodic memory: mental time travel or quantum 'memory wave' function?" by Jeremy R. Manning. The repository is organized as follows:

root └── code : all code used in the paper └── notebooks : jupyter notebooks for paper analyses └── data : all data used in the paper ├── raw : raw data before processing └── processed : all processed data └── paper : all files to generate paper └── figs : pdf copies of each figure

I also include a Dockerfile to reproduce our computational environment. Instruction for use are below (copied and modified from this project):

One time setup

  1. Install Docker on your computer using the appropriate guide below:
  2. Launch Docker and adjust the preferences to allocate sufficient resources (e.g. > 4GB RAM)
  3. Build the docker image by opening a terminal in this repo folder and enter docker build -t qwave .
  4. Use the image to create a new container for the workshop
    • The command below will create a new container that will map your local copy of the repository to /mnt within the container, so that all the files in the repo are shared between your host OS and the container. The command will also share port 9999 with your host computer so any jupyter notebooks launched from within the container will be accessible at localhost:9999 in your web browser
    • docker run -it -p 9999:9999 --name qwave -v $PWD:/mnt qwave
    • You should now see the root@ prefix in your terminal, if so you've successfully created a container and are running a shell from inside!
  5. To launch any of the notebooks: jupyter notebook --port=9999 --no-browser --ip=0.0.0.0 --allow-root

Using the container after setup

  1. You can always fire up the container by typing the following into a terminal
    • docker start --attach qwave
    • When you see the root@ prefix, letting you know you're inside the container
  2. Close a running container with ctrl + d from the same terminal you used to launch the container, or docker stop qwave from any other terminal

Owner

  • Name: Contextual Dynamics Laboratory
  • Login: ContextLab
  • Kind: organization
  • Email: contextualdynamics@gmail.com
  • Location: Hanover, NH

Contextual Dynamics Laboratory at Dartmouth College

GitHub Events

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Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 9
  • Total pull requests: 2
  • Average time to close issues: 28 days
  • Average time to close pull requests: 2 minutes
  • Total issue authors: 1
  • Total pull request authors: 2
  • Average comments per issue: 1.33
  • Average comments per pull request: 0.0
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Top Authors
Issue Authors
  • jeremymanning (9)
Pull Request Authors
  • paxtonfitzpatrick (1)
  • jeremymanning (1)
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