<p>This project investigates whether the way the brain encodes perceived actions allows it to simultaneously make automatic predictions about future actions.</p>
<p>fMRI data consist of 17 participants who watched the first half of the first episode of the TV show <em>Sherlock</em>. These data are available <a href="https://dataspace.princeton.edu/jspui/handle/88435/dsp01nz8062179" rel="nofollow">here</a>. </p>
<p>Actions in the Sherlock video were automatically annotated using a pre-trained temporal relation network, available <a href="https://github.com/metalbubble/TRN-pytorch" rel="nofollow">here</a>. Due to copyright restrictions, we do not post the video file publicly. If you wish to use this file for scientific purposes, please contact us or the authors of the original study in which the fMRI data were collected.</p>
<p>This study builds on the ACT-FAST action concept taxonomy. The OSF repository for the derviation and validation of that taxonomy is linked to this project. We apply other analyses to the Sherlock fMRI dataset in that study. However, video annotation and ratings are shared across the two studies. Thus see the linked repository ('fMRI Study' folder) for the corresponding code and data. For details on the code and data specific to this study, see the dictionary below.</p>
<p>Data and code dictionary:</p>
<em> predictive_model_betweensub.m - trains and tests ACT-FAST decoding and prediction models.
</em> predictive_model_betweensub_sbatch - slurm script for submitting predictive_model_betweensub.m to cluster.
<em> feature_selection.R - performs clustering to identify action sensitive voxels
</em> make_alphamaps_doublecv.m - creates reliabilty maps for use in feature_selection.R
<em> predictive_decoding.mat - results of predictive_model_betweensub.m used in predictive_decoding.R
</em> predictive_decoding.R - analyzes the accuracy of predictions made in predictive_model_betweensub.m
<em> TR_regressors_annotation_332.csv - contains regressors encoding the probabilities of the 332 action classes across the 1976 TRs of the fMRI time course
</em> TR_regressors_anoid_332.csv - contains numerical labels indicating the single most likely action at each TR in the fMRI timecourse. Vales correspond to the columns labels in TR_regressors_annotation_332.csv
* TR_regressors_ratings.csv - contains regressors for the 6 ACT-FAST dimensions across the 1976 TRs of the fMRI time course.</p>