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# STiMCON This repository describes the scripts from the paper <i>Ten Oever & Martin (2021), An oscillating computational model can track pseudo-rhythmic speech by using linguistic predictions, Elife, 10:e68066. DOI: https://doi.org/10.7554/eLife.68066 </i> ## Explanation The repository consists of scripts belonging to the Corpus Gesproken Nederlands (CGN), simulations and fitting with STiMCON ### CGN related files: <b>CGN_Fig2.py:</b>\ This script extracts and plot the basic temporal variation in the syllables and words of the CGN related to Figure 2 of the main manuscript. <b>CGN_Tab1_Fig3_Fig7.py:</b>\ The ordinary least square and related figures. <b>RNN_Model.py:</b>\ The RNN model <b>RNN_subFun.py:</b>\ Subfunctions to use the RNN_Model ### STiMCON related files: <b>STiMCON_Fig4_Fig5_Fig8A.py</b>\ Shows the basic behavior of STiMCON (Figure 4), the threshold/timing of activation (Figure 5) and ambiguous daga overall simulations (Figure 8A) <b>STiMCON_Fig6.py</b>\ Shows how acoustic time and model time is not the same in STiMCON (Figure 6) <b>STiMCON_Fig8C.py</b>\ Fitting of the da/ga data using the first active node as output (Figure 8C) <b>STiMCON_Fig8D.py</b>\ Fitting of the da/ga data using the relative node activation as output (Figure 8D) <b>STiMCON_core.py</b>\ Core script for the STiMCON model which has all the low-level code <b>STiMCON_plot.py</b>\ Plotting output of the STiMCON <b>STiMCON_sen.py</b>\ Creating sensory input going into the STiMCON