/WALD-EM

Wald Accumulation for Locations and Durations of Eye Movements

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WALD-EM: Wald Accumulation for Locations and Durations of Eye Movements

This repository provides code and additional materials associated with the article

Kucharský, Š., van Renswoude, D. R., Raijmakers, M. E. J., & Visser, I. (2020, July 17). WALD-EM: Wald Accumulation for Locations and Durations of Eye Movements. https://doi.org/10.31234/osf.io/2s48r

Getting started

Running the model requires two crucial dependencies:

  1. R (version 3.6.3 was used to create the original output). To download R, visit https://www.r-project.org/. Optionally, use R Studio (https://rstudio.com/).

  2. Stan and R package rstan. Visit https://mc-stan.org/ for more information, installation instruction, manual, and tutorials.

To reproduce all code and output, there are additional dependencies, mostly in the form of additional R packages. The list of R packages (and their versions) is available in the packages.csv file, and the full R environment listed in sessionInfo file.

To install all necessary R packages, simply run

packages <- read.csv("packages.csv", stringsAsFactors = FALSE)
install.packages(packages$package)

Or to get the packages in their specified versions

# install.package("devtools")
library(devtools)
packages <- read.csv("packages.csv", stringsAsFactors = FALSE)
devtools::install(pkg = packages$package, version = packages$version)

Additionally, we calculated saliency maps of images created by Xu et al. (2014) stored at https://github.com/NUS-VIP/predicting-human-gaze-beyond-pixels using Python (version 3.7.7, https://www.python.org/). We used functions that compute saliency of images that were originally created by Mayo Yamasaki for Python 2 and rewritten by Kohki Yamagiwa for Python 3. Using these resources is not strictly necessary to produce the main output as the saliency maps were saved in folder data/saliency.

Structure of the repository

  1. stan/ folder contains all necessary .stan files used for fitting the model, generating posterior predictives, and cross-validation.

    1. helpers/ folder contains definitions of functions that are used in the stan files. These functions are copied into the .stan scripts using the directive #include "path_to_file.stan" to be used as user defined functions. The file load_functions.stan makes it easier as including only that file includes all files present in the helpers/ folder.
    2. examples/ folder contains definitions of toy models that are used in the documents/ folder to demonstrate how to build a model like the one we present in the article.
    3. objects_central_distance_saliency.stan is the main .stan file used to fitting the data with the initial four factor model.
    4. objects_central_distance_saliency_horizontal.stan is the main .stan file used to fitting the data with the extended five factor model.
    5. log_lik_objects_central_distance_saliency.stan is a .stan file to compute the log-likelihood of the data under the initial four factor model.
    6. log_lik_objects_central_distance_saliency_horizontal.stan is a .stan file to compute the log-likelihood of the data under the extended five factor model.
    7. gqs_objects_central_distance_saliency.stan is a .stan file for generating posterior predictives from the initial four factor model.
    8. gqs_objects_central_distance_saliency.stan is a .stan file for generating posterior predictives from the extended five factor model.
    9. try_this.stan is a simple .stan file that can be used to debug possible linking errors (i.e., in case some helper functions were not loaded in Stan.

  2. data/ folder contains data that are used in this project. The eye-tracking data are associated with the article by Renswoude et al. (n.d.) who used the stimli materials from Xu et al. (2014).

    1. saliency/ folder contains all 700 stimuli from the repository by xu2014beyond (https://github.com/NUS-VIP/predicting-human-gaze-beyond-pixels) converted to saliency maps and saved as .jpg files. The folder contains get_saliency.py script that should (provided necessary dependencies are included, see above) reproduce the output.
    2. cleaned_data.Rdata collects a work space with data objects that are otherwise shared in form of .csv files in the data/ folder.

  3. documents/ folder contains files that provide additional background information about the model, including explanations of helper functions, implementation of the model in the Stan modeling language, etc. The aim of this folder is to collect all possible information that is useful for adapting the model to your own needs rather than simply reruning the code in this repository.

  4. R/ folder contains function definitions that are used in the project, or additional scripts that are used to maintain this repository.

  5. scripts/ folder contains the R scripts that were used to produce the output: data preparation, fitting model(s), model diagnostics and comparison, plotting, etc. The scripts should be relatively modular/standalone, and so each script should be able to produce its own output regardless of which script ran first. It is advised to run each script in a fresh R session. For completeness, the project output was created in this order:

    1. prepare_data.md is an output of an rmarkdown script and processes the data. This script is responsible for creating all contents of the data/ folder.
    2. fit_model.R is a script that fits and saves the ‘initial model’ using Stan.
    3. diag_model.R is a script that produces ‘initial model’ diagnostics (including diagnostic plots, and parameter estimates.
    4. posterior_predictives_in_sample.R and posterior_predictives_out_sample.R are scripts that produce the posterior predictive checks of the ‘initial model’, or more formally retrodictive and predictive checks.
    5. fit_model_horizontal.R is a script that fits and saves the ‘extended model’ using Stan.
    6. diag_model_horizontal.R is a script that produces ‘initial model’ diagnostics (including diagnostic plots, and parameter estimates.
    7. posterior_predictives_in_sample_horizontal.R and posterior_predictives_out_sample_horizontal.R are scripts that produce the posterior predictive checks of the ‘extended model’, or more formally retrodictive and predictive checks.
    8. model_comparison.R is a script that compares the two models to each other.

  6. saves/ folder contains some R objects that were saved to make some output reusable (e.g., so that R scripts that depend on other’s scripts output can be run independently).

  7. figures/ folder contains figures that we generated for the article, or as part of the model diagnostics. The folder itself has some .R scripts which generate figures that are used in the article for illustration. There are 2 additional subfolders:

    1. fit_model/ contains plots associated with the initial model containing four factors.
    2. fit_model_horizontal/ contains plots associated with the extended model containing five factors.

    Each of these two subfolders have the following structure:

    1. in_sample contains the posterior predictives on the data that were used to fit the model (aka posterior “retrodictives”).
    2. out_sample contains the posterior predictives on the data that were used for cross-validation.
    3. par_diagnostics contains diagnostic plots of the MCMC chains.

NB. The paths to files and folders are defined using the package here (Müller, 2017), and rely on the presence of the file DMEM.Rproj or .here in the root of this repository (i.e., the paths are relative to these to files, and their location is assumed to be in the root of the repository). Provided at least one of these files is present, and the working directory is anywhere inside of this repository, the paths should “just work”.

References

Müller, K. (2017). Here: A simpler way to find your files. https://CRAN.R-project.org/package=here

Renswoude, D. R. van, Voorvaart, R. E., Berg, L. van den, Raijmakers, M. E. J., & Visser, I. (n.d.). Object familiarity influences infant gaze control during free scene viewing. Manuscript in Preparation.

Xu, J., Jiang, M., Wang, S., Kankanhalli, M. S., & Zhao, Q. (2014). Predicting human gaze beyond pixels. Journal of Vision, 14(1), 28–28. https://doi.org/10.1167/14.1.28