DOI: 10.5281/zenodo.7102065
title: Data and models for "Modelling human behaviour in cognitive tasks with latent dynamical systems"

description: Ebb and Flow gameplay data and trained model parameters for:

Jaffe, P.I., Poldrack, R.A., Schafer, R.J. & Bissett, P.G. Modelling human behaviour in cognitive tasks with latent dynamical systems. Nat Hum Behav (2023). https://doi.org/10.1038/s41562-022-01510-8 

Ebb and Flow is a task-switching game offered as a part of the Lumosity cognitive training platform (Lumos Labs, Inc.). The data and model parameters are organized by participant/model in individual archived directories (140 participants; 245 models). Within each model directory, “data_pre_split.pickle” contains the raw Ebb and Flow data. The processed model inputs for the training, validation, and holdout/test splits are contained in the files "train_model_inputs.pt", "val_model_inputs.pt", and "test_model_inputs.pt", respectively. Other metadata associated with each split is contained in "train_other_data.pkl", "val_other_data.pkl", and "test_other_data.pkl". The parameters from the trained model are stored in “model_params.pth”. Some intermediate analysis products are contained in the subfolder “model_analysis”.

Metadata for all models can be found in “model_metadata.csv”. The metadata field “switch_cost_type” identifies models that were trained on data with (sc+) or without (sc-) a switch cost (note that models marked “NA”, except for the optimal models, were also trained on data with a switch cost but were not included in the paired comparison of the sc+ and sc- models; see manuscript for details). The "exgauss" field identifies models that were trained with an exGaussian response template (coded as "exgauss+"); models identified as "exgauss-" were trained with a Gaussian kernel and were used in paired comparisons with the exgauss+ models. The "early" field identifies models that were trained with early-stage practice data if set to TRUE. The "optimal" field identifies models that were trained to perform the task optimally if set to TRUE. The other metadata fields are self-explanatory.

Fast command line download instructions (macOS/linux) 

For help downloading on Windows, see https://github.com/dvolgyes/zenodo_get.

1) Copy and save the complete list of files below to a text file, e.g. "files.txt". Save it to the same directory you would like to save the data to. 

2) Install parallel if it's not already installed:

sudo apt-get install parallel

3) Run the following from the directory with files.txt (all data will be saved here). The flag -jN will create N parallel wget instances to download the files, e.g.:

cat files_test.txt | parallel -j8 wget {}

4) Unzip the files and cleanup:

unzip "*.zip"
rm *.zip files.txt

List of files

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description: For general inquiries about the data, please contact Paul Jaffe (pauljaffe7@gmail.com). If you wish to use this data for research or other purposes, please first contact Bob Schafer (bschafer@lumoslabs.com).