Fix citations [skip ci]

Author: mvdoc

tutorials/notebooks/shortclips/03_compute_explainable_variance.ipynb

@@ -39,7 +39,7 @@
         "In the literature, the explainable variance is also known as the *signal\n",
         "power*.  \n",
         "\n",
-        "For more information, see {cite:t}`Sahani2002`, {cite:t}`Hsu2004`, and {cite:t}`Schoppe2016`."
+        "For more information, see {cite}`Sahani2002,Hsu2004,Schoppe2016`."
       ]
     },
     {

tutorials/notebooks/shortclips/05_fit_wordnet_model.ipynb

@@ -109,7 +109,7 @@
       "metadata": {},
       "source": [
         "Before fitting an encoding model, the fMRI responses are typically z-scored over time. This normalization step is performed for two reasons.\n",
-        "First, the regularized regression methods used to estimate encoding models generally assume the data to be normalized {cite:t}`Hastie2009`. \n",
+        "First, the regularized regression methods used to estimate encoding models generally assume the data to be normalized {cite}`Hastie2009`. \n",
         "Second, the temporal mean and standard deviation of a voxel are typically considered uninformative in fMRI because they can vary due to factors unrelated to the task, such as differences in signal-to-noise ratio (SNR).\n",
         "\n",
         "To preserve each run independent from the others, we z-score each run separately."
@@ -254,7 +254,7 @@
         "Now, let's define the model pipeline.\n",
         "\n",
         "With regularized linear regression models, it is generally recommended to normalize \n",
-        "(z-score) both the responses and the features before fitting the model {cite:t}`Hastie2009`. \n",
+        "(z-score) both the responses and the features before fitting the model {cite}`Hastie2009`. \n",
         "Z-scoring corresponds to removing the temporal mean and dividing by the temporal standard deviation.\n",
         "We already z-scored the fMRI responses after loading them, so now we need to specify\n",
         "in the model how to deal with the features. \n",

tutorials/notebooks/shortclips/vem_tutorials_merged_for_colab.ipynb

@@ -302,7 +302,7 @@
     "In the literature, the explainable variance is also known as the *signal\n",
     "power*.  \n",
     "\n",
-    "For more information, see {cite:t}`Sahani2002`, {cite:t}`Hsu2004`, and {cite:t}`Schoppe2016`."
+    "For more information, see {cite}`Sahani2002,Hsu2004,Schoppe2016`."
    ]
   },
   {
@@ -1427,7 +1427,7 @@
    "metadata": {},
    "source": [
     "Before fitting an encoding model, the fMRI responses are typically z-scored over time. This normalization step is performed for two reasons.\n",
-    "First, the regularized regression methods used to estimate encoding models generally assume the data to be normalized {cite:t}`Hastie2009`. \n",
+    "First, the regularized regression methods used to estimate encoding models generally assume the data to be normalized {cite}`Hastie2009`. \n",
     "Second, the temporal mean and standard deviation of a voxel are typically considered uninformative in fMRI because they can vary due to factors unrelated to the task, such as differences in signal-to-noise ratio (SNR).\n",
     "\n",
     "To preserve each run independent from the others, we z-score each run separately."
@@ -1572,7 +1572,7 @@
     "Now, let's define the model pipeline.\n",
     "\n",
     "With regularized linear regression models, it is generally recommended to normalize \n",
-    "(z-score) both the responses and the features before fitting the model {cite:t}`Hastie2009`. \n",
+    "(z-score) both the responses and the features before fitting the model {cite}`Hastie2009`. \n",
     "Z-scoring corresponds to removing the temporal mean and dividing by the temporal standard deviation.\n",
     "We already z-scored the fMRI responses after loading them, so now we need to specify\n",
     "in the model how to deal with the features. \n",

tutorials/notebooks/shortclips/vem_tutorials_merged_for_colab_model_fitting.ipynb

@@ -302,7 +302,7 @@
     "In the literature, the explainable variance is also known as the *signal\n",
     "power*.  \n",
     "\n",
-    "For more information, see {cite:t}`Sahani2002`, {cite:t}`Hsu2004`, and {cite:t}`Schoppe2016`."
+    "For more information, see {cite}`Sahani2002,Hsu2004,Schoppe2016`."
    ]
   },
   {
@@ -830,7 +830,7 @@
    "metadata": {},
    "source": [
     "Before fitting an encoding model, the fMRI responses are typically z-scored over time. This normalization step is performed for two reasons.\n",
-    "First, the regularized regression methods used to estimate encoding models generally assume the data to be normalized {cite:t}`Hastie2009`. \n",
+    "First, the regularized regression methods used to estimate encoding models generally assume the data to be normalized {cite}`Hastie2009`. \n",
     "Second, the temporal mean and standard deviation of a voxel are typically considered uninformative in fMRI because they can vary due to factors unrelated to the task, such as differences in signal-to-noise ratio (SNR).\n",
     "\n",
     "To preserve each run independent from the others, we z-score each run separately."
@@ -975,7 +975,7 @@
     "Now, let's define the model pipeline.\n",
     "\n",
     "With regularized linear regression models, it is generally recommended to normalize \n",
-    "(z-score) both the responses and the features before fitting the model {cite:t}`Hastie2009`. \n",
+    "(z-score) both the responses and the features before fitting the model {cite}`Hastie2009`. \n",
     "Z-scoring corresponds to removing the temporal mean and dividing by the temporal standard deviation.\n",
     "We already z-scored the fMRI responses after loading them, so now we need to specify\n",
     "in the model how to deal with the features. \n",