Multimodal dynamics of explaining the mechanisms of global warming

This repo contains the Jupyter notebooks for the analyses underlying Multimodal dynamics of explaining the mechanisms of global warming (Paxton, Abney, Castellanos, & Sepulveda, 2016), a poster presented at the 2016 Annual Conference of the Cognitive Science Society.

Due to ethical considerations for participant privacy, we cannot publicly share files that include participant data.

Overview of experiment procedures

In the current experiment, we asked participants to watch a 5-minute video explaining the mechanisms behind global warming ("How Global Warming Works in Under 5 Minutes"; Ranney, Lamprey, Reinholz, Le, Ranney, & Goldwasser, 2013). After watching the video (and a 30-second waiting period), participants were asked to explain the mechanisms aloud in less than 2 minutes so that a future participant could learn the information.

Participants' gaze and attention throughout the experiment were captured using a remote eye-tracker (SMI Red-m).

Comprehension was derived as the similarity between each participant's explanation and the video's transcript using latent semantic analysis (LSA) implemented in the LSAfun package in R (Gunther, Dudschig, & Kaup, 2015).

Overview of repo contents

This repo contains several Jupyter notebooks, supplementary analysis files, figures, and a copy of the poster.

• dyn_exp-step2-data_cleaning.ipynb: A Jupyter notebook running a Python kernel to clean up participants' transcripts.
• dyn_exp-step3-data_preparation.ipynb: A Jupyter notebook running an R kernel to prepare data for later analysis.
• dyn_exp-step4-data_analysis.ipynb: A Jupyter notebook running an R kernel to execute the data analysis plan for the current project.
• dyn_exp-poster-cogsci-2016.pdf: A PDF of the poster.
• figures/: A directory containing all images produced by the Jupyter notebooks.
• supplementary_code: A directory containing additional files to help clean the data (e.g., defining new functions, importing libraries).
• global-warming-transcript.txt: A transcript of the video watched by participants ("How Global Warming Works in Under 5 Minutes"; Ranney, Lamprey, Reinholz, Le, Ranney, & Goldwasser, 2013).