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<p>Data and scripts necessary to reproduce figures reported in Sprague, Ester & Serences, 2016, Neuron <a href="http://www.cell.com/neuron/fulltext/S0896-6273%2816%2930352-X" rel="nofollow" title="Sprague, Ester & Serences, 2016">(available here)</a>. Modeled after Foster et al, 2016, J Neurophys (<a href="https://osf.io/bwzfj/" rel="nofollow">https://osf.io/bwzfj/</a>). Data include behavioral data measured inside the scanner (both raw outputs from stimulus presentation scripts, as well as concatenated data across runs), and extracted activation patterns on each TR from each trial from each ROI (V1, V2, V3, V3A, IPS0, IPS1, IPS2, IPS3, sPCS, All ROIs combined, iPCS, DLPFC+, SMA+). Scripts include stimulus presentation code, code used to compute behavioral results, IEM, reconstructions, fits, representational fidelity, etc. Should you need access to raw or pre-processed whole-slice-stack functional data, please contact the authors.</p> <h2>Data description</h2> <p>Each participant (n = 6) completed 3 sessions of scanning. Each participant is defined by a unique identifier (AI, AL, AP, AR, AS, BC), a study number (always 8), and a session number (1-3). For example, particpant AI is AI81, AI82, and AI83. </p> <p>Within all scripts plotting results, data is averaged within a participant before averaging; for all scripts implementing resampling analyses, trials are concatenated across all participants. </p> <p>We analyzed the data in MATLAB, so the data are .mat files and the analysis scripts are .m files. To run the scripts, you will need access to MATLAB. We include only trial-wise data from each ROI reported (measurements from each voxel of each ROI on each time point on each trial). This constitutes ~5 GB. Each kind of reconstruction (those aligned to relative target positions, those aligned to the exact target position, and 1-d reconstructions aligned to the target polar angle) takes up ~50 GB or more of disk space. These analyses should only be run on sufficiently powerful and capable hardware.</p> <p>For those interested primarily in comparing best-fit parameters to reconstructions (e.g., Figs 7-8), we also include a set of resampled fits in the <code>/wmDrop_fits</code> folder. These can be computed using scripts found in mFiles (see <code>wmDrop_allAnalyses1.m</code> for a detailed description of each step of the analysis pipeline)</p> <h2>Instructions for use</h2> <p><strong>Paper citation:</strong> Sprague, T.C., Ester, E.F., Serences, J.T., (In Press). Restoring latent visual working memory representations. Neuron.</p> <p>You can find a pdf of our paper <a href="http://www.cell.com/neuron/fulltext/S0896-6273%2816%2930352-X" rel="nofollow">here</a></p> <p><strong>Data citation:</strong> See citation list in the right hand corner of the main project page.</p> <p><strong>Usage:</strong> This OSF project contains the data and analysis scripts for the experiment reported in our <em>Neuron</em> paper. If you would like to use the data in published work, please cite both the paper and data set, and email me.</p> <p>For any further questions or comments, please email me.</p> <p>You can reach me at tsprague@nyu.edu or tommy.sprague@gmail.com</p>
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