Rename voxelwise modeling to voxelwise encoding model framework

Author: mvdoc

README.rst

@@ -1,18 +1,18 @@
-============================
-Voxelwise modeling tutorials
-============================
+==================================
+Voxelwise Encoding Model tutorials
+==================================
 
 |Github| |Python| |License| |Build| |Build Tutorials| |Downloads|
 
-Welcome to the voxelwise modeling tutorial from the
-`GallantLab <https://gallantlab.org>`_.
+Welcome to the Voxelwise Encoding Model tutorials, brought to you by the
+`Gallant Lab <https://gallantlab.org>`_.
 
 Paper
 =====
 
 If you use these tutorials for your work, consider citing the corresponding paper:
 
-Dupré La Tour, T., Visconti di Oleggio Castello, M., & Gallant, J. L. (2024). The Voxelwise Modeling framework: a tutorial introduction to fitting encoding models to fMRI data. https://doi.org/10.31234/osf.io/t975e
+   Dupré La Tour, T., Visconti di Oleggio Castello, M., & Gallant, J. L. (2024). The Voxelwise Encoding Model framework: a tutorial introduction to fitting encoding models to fMRI data. https://doi.org/10.31234/osf.io/t975e
 
 You can find a copy of the paper `here <paper/voxelwise_tutorials_paper.pdf>`_.
 

tutorials/notebooks/shortclips/02_download_shortclips.ipynb

@@ -51,9 +51,8 @@
         "download more subjects.\n",
         "\n",
         "We also skip the stimuli files, since the dataset provides two preprocessed\n",
-        "feature spaces to perform voxelwise modeling without requiring the original\n",
-        "stimuli.\n",
-        "\n"
+        "feature spaces to fit voxelwise encoding models without requiring the original\n",
+        "stimuli."
       ]
     },
     {

tutorials/notebooks/shortclips/03_compute_explainable_variance.ipynb

@@ -15,9 +15,10 @@
         "stimulus-dependent signal and noise. If we present the same stimulus multiple\n",
         "times and we record brain activity for each repetition, the stimulus-dependent\n",
         "signal will be the same across repetitions while the noise will vary across\n",
-        "repetitions. In voxelwise modeling, the features used to model brain activity\n",
-        "are the same for each repetition of the stimulus. Thus, encoding models will\n",
-        "predict only the repeatable stimulus-dependent signal.\n",
+        "repetitions. In the Voxelwise Encoding Model framework, \n",
+        "the features used to model brain activity are the same for each repetition of the \n",
+        "stimulus. Thus, encoding models will predict only the repeatable stimulus-dependent \n",
+        "signal.\n",
         "\n",
         "The stimulus-dependent signal can be estimated by taking the mean of brain\n",
         "responses over repeats of the same stimulus or experiment. The variance of the\n",

tutorials/notebooks/shortclips/README.md

@@ -1,6 +1,6 @@
 # Shortclips tutorial
 
-This tutorial describes how to use the Voxelwise Encoding Model framework on a visual
+This tutorial describes how to use the Voxelwise Encoding Model framework in a visual
 imaging experiment.
 
 ## Dataset

tutorials/notebooks/shortclips/merged_for_colab.ipynb

@@ -209,9 +209,10 @@
     "stimulus-dependent signal and noise. If we present the same stimulus multiple\n",
     "times and we record brain activity for each repetition, the stimulus-dependent\n",
     "signal will be the same across repetitions while the noise will vary across\n",
-    "repetitions. In voxelwise modeling, the features used to model brain activity\n",
-    "are the same for each repetition of the stimulus. Thus, encoding models will\n",
-    "predict only the repeatable stimulus-dependent signal.\n",
+    "repetitions. In the Voxelwise Encoding Model framework, \n",
+    "the features used to model brain activity are the same for each repetition of the \n",
+    "stimulus. Thus, encoding models will predict only the repeatable stimulus-dependent \n",
+    "signal.\n",
     "\n",
     "The stimulus-dependent signal can be estimated by taking the mean of brain\n",
     "responses over repeats of the same stimulus or experiment. The variance of the\n",

tutorials/notebooks/shortclips/merged_for_colab_model_fitting.ipynb

@@ -209,9 +209,10 @@
     "stimulus-dependent signal and noise. If we present the same stimulus multiple\n",
     "times and we record brain activity for each repetition, the stimulus-dependent\n",
     "signal will be the same across repetitions while the noise will vary across\n",
-    "repetitions. In voxelwise modeling, the features used to model brain activity\n",
-    "are the same for each repetition of the stimulus. Thus, encoding models will\n",
-    "predict only the repeatable stimulus-dependent signal.\n",
+    "repetitions. In the Voxelwise Encoding Model framework, \n",
+    "the features used to model brain activity are the same for each repetition of the \n",
+    "stimulus. Thus, encoding models will predict only the repeatable stimulus-dependent \n",
+    "signal.\n",
     "\n",
     "The stimulus-dependent signal can be estimated by taking the mean of brain\n",
     "responses over repeats of the same stimulus or experiment. The variance of the\n",

tutorials/notebooks/vim2/README.md

@@ -7,7 +7,7 @@ lobe, and with no mappers to plot the data on flatmaps.
 Using the "Shortclips tutorial" with full brain responses is recommended.
 :::
 
-This tutorial describes how to perform voxelwise modeling on a visual
+This tutorial describes how to use the Voxelwise Encoding Model framework in a visual
 imaging experiment.
 
 ## Data set

tutorials/pages/index.md

@@ -5,7 +5,7 @@ Welcome to the tutorials on the Voxelwise Encoding Model framework from the
 
 If you use these tutorials for your work, consider citing the corresponding paper:
 
-> T. Dupré La Tour, M. Visconti di Oleggio Castello, and J. L. Gallant. The voxelwise modeling framework: a tutorial introduction to fitting encoding models to fMRI data. PsyArXiv, 2024. [doi:10.31234/osf.io/t975e.](https://doi.org/10.31234/osf.io/t975e)
+> T. Dupré La Tour, M. Visconti di Oleggio Castello, and J. L. Gallant. The Voxelwise Encoding Model framework: a tutorial introduction to fitting encoding models to fMRI data. PsyArXiv, 2024. [doi:10.31234/osf.io/t975e.](https://doi.org/10.31234/osf.io/t975e)
 
 You can find a copy of the paper [here](https://github.com/gallantlab/voxelwise_tutorials/blob/main/paper/voxelwise_tutorials_paper.pdf).
 

tutorials/static/references.bib

@@ -165,7 +165,7 @@ @misc{huth2022data
 
 @article{dupre2023,
     author = {Dupré La Tour, T. and Visconti di Oleggio Castello, M. and Gallant, J. L.},
-    title = {The Voxelwise Modeling framework: a tutorial introduction to fitting encoding models to {fMRI} data},
+    title = {The Voxelwise Encoding Model framework: a tutorial introduction to fitting encoding models to {fMRI} data},
     journal = {PsyArXiv},
     year = {2024},
     doi = {10.31234/osf.io/t975e}