DOI: 10.5281/zenodo.10775514
title: Data and models for "An image-computable model of speeded decision-making"

description: Lost in Migration gameplay data and trained models for:

Jaffe, P. I., Gustavo, X. S. R., Schafer, R. J., Bissett, P. G., Poldrack, R. A. An image-computable model of speeded decision-making. eLife 13, RP98351 (2024).

 

This dataset can be used to reproduce all of the results of the manuscript, following the instructions in the code repository for the paper: https://github.com/pauljaffe/vam.

The dataset includes the following components:

gameplay_data.zip: Trial-level gameplay metadata for Lost in Migration. Lost in Migration is a variant of the flanker task offered as a part of the Lumosity cognitive training platform (Lumos Labs, Inc.). The .zip file includes a separate .csv file for each of the 75 Lumosity users (participants) that we trained models on. Each .csv file has one row per trial with the following fields/columns: "anon_id", numerical identifier for the Lumosity user; "nth_play", the nth gameplay of Lost in Migration for this user; "trial", the nth trial for the current gameplay; "xpos", the signed horizontal distance from the center of the target bird to the left edge of the game window (pixels, non-negative); "ypos", the signed vertical distance from the center of the target bird to the bottom edge of the game window (pixels, non-negative); "flanker_direction", (L/R/U/D); "response_direction", (L/R/U/D); "target_direction", (L/R/U/D); "response_time", (ms); "stimulus_layout", numerical code for the layout of the bird flock for the current trial (0: horizontal line, 1: vertical line, 2: cross, 3: <, 4: >, 5: v, 6: ^).

vam_models.zip: Parameters for the 75 visual accumulator models (VAMs) analyzed in the manuscript.

task_opt_models.zip: Parameters for the 75 task-optimized models analyzed in the manuscript.

binned_rt_models.zip: Parameters for the VAMs trained with the modified binned RT training paradigm (supplemental material; one VAM for each of 10 Lumosity users in each of 5 RT bins, 50 models total).

metadata.csv: Metadata for each Lumosity user that a VAM/task-optimized model was trained on. The .csv file has one row per user with the following fields/columns: "user_id", numerical identifier for the Lumosity user (same as "anon_id" in gameplay_data.zip); "gender", self-reported gender ('m', 'f', or null, indicating no response was given); "binned_age", age bucketed into decade-long bins (20-29, 30-39... 80-89).

binned_rt_metadata.csv: Metadata for the 10 Lumosity users that we trained a VAM model on using the modified binned RT training paradigm. This has the same organization as metadata.csv.

derivatives.zip: The RTs/choices generated by the trained models, organized into separate folders by model type (vam/task_opt/binned_rt) and user ID.  Also includes a "summary_stats" folder with analysis products of the model activations and outputs.

graphics.zip: Image files used to create the visual stimuli from the gameplay metadata.

example_model_inputs.zip: The processed visual stimuli and gameplay data used as inputs to train one model (user ID 182). Note we provide instructions to recreate the stimuli and other model inputs for all models in the code repository.
description: For general inquiries about the data, please contact Paul Jaffe (pauljaffe7@gmail.com).