diff --git a/07_final_assignment/paper/main.tex b/07_final_assignment/paper/main.tex
index 4701a09..f16255e 100644
--- a/07_final_assignment/paper/main.tex
+++ b/07_final_assignment/paper/main.tex
@@ -3,16 +3,19 @@
 \usepackage[utf8]{inputenc}
 \usepackage[american]{babel}
 \usepackage[style=apa,sortcites=true,sorting=nyt,backend=biber]{biblatex}
-\usepackage{color} 
 \DeclareLanguageMapping{american}{american-apa}
 \addbibresource{references.bib}
+
 \usepackage{listings}
+\usepackage{color} 
+
+\usepackage{todonotes}
 \title{Simulation of Grainger et al. (2012) with Rescorla Wagner equations}
 \shorttitle{Grainger et al. (2012) simulation with RW equations}
 \author{Robert Geirhos (3827808), Klara Grethen (3899962), \\David-Elias Künstle (3822829), Felicia Saar (3818590)}
 \affiliation{Linguistics for Cognitive Science Course, University of Tübingen}
 
-\abstract{our abstract goes here!}
+\abstract{TODO TODO TODO our abstract goes here TODO TODO TODO}
 
 \lstset{ %
   basicstyle=\footnotesize,        % the size of the fonts that are used for the code
@@ -37,11 +40,11 @@
 
 %\cite{}
 \section{Introduction}
-statement of the problem
+\todo{statement of the problem}
 
 \section{Simulations}
 \subsection{Experimental Code}
-why we didn't use the given code, what we improved, how the result is structured - Goal: modular and comprehensive experiment. Problems with paper and given code. What's a block in our experiment.\\
+\todo{why we didn't use the given code, what we improved, how the result is structured - Goal: modular and comprehensive experiment. Problems with paper and given code. What's a block in our experiment.}
 Since preliminary experiments showed that the monkeys performed with very high accuracies (>90\%), we decided to introduce a random parameter $ r $ in the experiment, defined as the fraction of times the monkey would make a random guess instead of an experience-based prediction.
 
 \subsection{Choice of Parameters}
@@ -52,14 +55,14 @@
 In other words, the maximum possible performance is no longer 1.0 (for a very intelligent monkey) but rather restricted by $ r $. If a monkey's performance is slightly better than $ p_{max} $, this is assured to be due to chance.
 \subsubsection{Alpha and Beta} Both $ \alpha $ and $ \beta $ were our independent variables which we manipulated over the course of the experiments. We gathered data for every possible combination of $ \alpha $ and $ \beta $ values within an equally spaced range from 0.0 to 0.3. A total of 15 values for each $ \alpha $ and $ \beta $ were combined to $ 15*15 = 225 $ possible combinations. Since $ \alpha $ and $ \beta $ were internally multiplied to a single value, we expected the outcome to be symmetrical due to the commutativity of the multiplication operation and therefore calculated each combination of $ \alpha $ and $ \beta $ only once, which we used as a trick to improve the overall runtime. Therefore, $\sum_{i=1}^{15}i = 120$ combinations  remained to be explored.
 
-\subsubsection{Lambda} Choice of $ \lambda $
+\subsubsection{Lambda} \todo{Choice of $ \lambda $}
 %TODO explain choice of lambda=1, saliency
 
 \subsection{Running Parallelized Experiments}
-Running an experiment with a single combination of $ \alpha $ and $ \beta $ on a normal desktop computer took about 75 minutes. Therefore, the parameter space one could explore within a reasonable amount of time was quite restricted. We decided to write a parallelized version of the code to reduce the overall runtime. Using the R packages foreach, parallel and doParallel (TODO: Cite them properly), we restructured the experiment. Since conflicts can easily occur when more than one core is trying to access a shared data structure at the same time, we implemented a parallelized version that is able to run without even containing critical sections. Instead, each thread has its own data structure, a .txt file, and in the end the results are harvested and combined. This version of the experiment ran on a cluster with 15 cores, each performing a total amount of eight experiments. Altogether, 120 combinations of $ \alpha $ and $ \beta $ were explored overnight, which would have taken about 150 hours in a non-parallelized version.
+Running an experiment with a single combination of $ \alpha $ and $ \beta $ on a normal desktop computer took about 75 minutes. Therefore, the parameter space one could explore within a reasonable amount of time was quite restricted. We decided to write a parallelized version of the code to reduce the overall runtime. Using the R packages foreach, parallel and doParallel \toco{(TODO: Cite them properly)}, we restructured the experiment. Since conflicts can easily occur when more than one core is trying to access a shared data structure at the same time, we implemented a parallelized version that is able to run without even containing critical sections. Instead, each thread has its own data structure, a .txt file, and in the end the results are harvested and combined. This version of the experiment ran on a cluster with 15 cores, each performing a total amount of eight experiments. Altogether, 120 combinations of $ \alpha $ and $ \beta $ were explored overnight, which would have taken about 150 hours in a non-parallelized version.
 
 \section{Results}
-results
+\todo{results}
 
 \begin{figure*}
 \includegraphics[width=0.9\textwidth]{plots/plot_accuracy}
@@ -71,8 +74,7 @@
 
 
 \section{Discussion}
-"your conclusions about what is most likely to underlie the different success rates of the baboons"
-
+\todo{"your conclusions about what is most likely to underlie the different success rates of the baboons"}
 
 \appendix
 \section{Complete Results}