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<p><strong>Emotionotopy</strong></p> <p>Please refer to: <a href="https://doi.org/10.1038/s41467-019-13599-z" rel="nofollow">https://doi.org/10.1038/s41467-019-13599-z</a></p> <p><strong>Brief description of the directories</strong></p> <p><em>behavioral_ratings/</em> Emotion ratings and the scripts to process and analyze them. The directory contains also scripts related to the processing of other models taken from Thornton, M. A., & Tamir, D. I. (2017) (10.1073/pnas.1616056114) and from Labs and colleagues, 2015 (10.12688/f1000research.6230.1)</p> <p><em>lowlevel_models/</em> low-level acoustic (i.e., volume energy - RMS of the signal, power spectral features) and visual (i.e., Gabor contrast energy for 0.5 and 8 cyc/deg spatial frequencies for each frame, GIST descriptors) models to clean the fMRI timeseries</p> <p><em>emotion_models/</em> From behavioral ratings we obtained two models: one based on the six basic emotions (happiness, surprise, fear, sadness, anger, disgust), and one obtained from a principal compontent analysis (polarity, complexity, intensity, PC#4, PC#5, PC#6). The directory contains also portrayed emotions alternative models taken form Labs and colleagues, 2015 (10.12688/f1000research.6230.1). The directory contains the scripts for the surrogate-based (IAAFT) analysis</p> <p><em>fMRI_data/</em> Directory contains preprocessed fMRI timeseries. The raw data is available at <a href="http://studyforrest.org/" rel="nofollow">http://studyforrest.org/</a></p> <p><em>fMRI_encoding/</em> Encoding results and matlab scripts (Figure 3 in the manuscript)</p> <p><em>fMRI_gradient/</em> Testing a gradient-like organization in right (and left) TPJ (Figure 4 & 5 in the manuscript)</p> <p><em>pRF/</em> Data and results for the population receptive field analysis (Figure 6 in the manuscript)</p> <p><em>matlab_functions/</em> & <em>external_matlab_functions/</em> Matlab functions called by scripts and needed to perform the analyses</p>
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