diff --git a/07_final_assignment/paper/main.tex b/07_final_assignment/paper/main.tex
index 858ffc6..c5ad7e7 100644
--- a/07_final_assignment/paper/main.tex
+++ b/07_final_assignment/paper/main.tex
@@ -106,29 +106,27 @@
 \section{Results}
 The number of words learned by the actual monkeys ranged between 87 and 308. With the chosen range for $\alpha$ and $\beta$, we obtained between 275 and 307 learned words, however, it is important to note that we only presented 307 words, so the model reached maximum learning potential. The general accuracy for the real monkeys lay between 71.14\% and 79.81\%, while our accuracies moved between 0.60 and 0.68. Accuracies for word and non-word decisions are similar in both cases. The complete result data is attached in the appendix of this paper. 
 
-%TODO we need a section explaining the results of the plots. What does that mean? -> small influence of parameters as a major finding, however there ARE effects -> perhaps explain that along with the GAM. I think it is crucial that we explain our findings (= our contribution): We explored the whole parameter space (which others probably couldn't), and we found this and that influence.
+%TODO we need a section explaining the results of the plots. What does that mean? -> small influence of parameters as a major finding, however there ARE effects -> perhaps explain that along with the GAM, where we definitely have to explain what the predictors included in the model are: Otherwise it's black magic. I think it is crucial that we explain our findings (= our contribution): We explored the whole parameter space (which others probably couldn't), and we found this and that influence.
 
 \begin{figure*}[ht]
   \centering
   \includegraphics[width=0.9\textwidth]{../plots/plot_accuracy}
   \caption{
-    Top row shows model output  accuracies in dependence of modulated $ \alpha $ and $ \beta $.
+    Top row shows model output accuracies in dependence of modulated $ \alpha $ and $ \beta $.
     Second row visualizes corresponding nonlinear regressions (GAM).
-    Accuracy seem to approximate a maximal accuracy with growing alpha, beta parameter.
+    Accuracy seems to approximate a maximal accuracy with growing $ \alpha $ and $ \beta $ parameters.
     Visible in the GAM plot is the small influence of one of the parameters.
     This indicates that the results might probably be approximated with just one nonlinear parameter.
   }
   \label{fig:accuracy}
 \end{figure*}
-%TODO we have to insert a small block explaining how we got the GAM model (parameter choice). Otherwise it's black magic.
 
 \begin{figure*}[ht]
   \centering
   \includegraphics[width=0.9\textwidth]{../plots/plot_numwords}
   \caption{
-    The left plot shows the raw num words learned of the model with modulated parameter (alpha, beta).
-    The model performs always quite well, just several parameter value result in lower perfomance. The corresponding nonlinear regression plot (middle) doesn't mirror a first hipothesis of growing words learned with growing parameter values.
-    This is not necessarily a consequense of a wrong hypothesis but of a wrong regression model because of the weak data with very high frequency of around 305 learned words but almost no other number of learned words (right plot).
+    The left plot shows the raw number of words learned by the model with modulated parameters $ \alpha $ and $ \beta $. The corresponding nonlinear regression plot (middle) doesn't mirror a first hypothesis of more words learned with increasing parameter values.
+    This is not necessarily a consequence of a wrong hypothesis but may rather be caused by the weak data with one very high frequency of around 305 learned words and many very low frequencies (right plot).
 }
   \label{fig:numwords}
 \end{figure*}