\section{Divergence}
Lacking inter-trial fixation crosses for monkeys let us expect a higher divergence, especially in the beginning of trials, when compared to humans (cf. \cite{Tatler2006}). 

%Figure \ref{divergencemeasures} confirms this expectation. We see ...

\begin{singlespacing}  
\begin{figure}[p!]
\label{divergence}
%\begin{rotate}{1} 
\fbox{ \begin{minipage}{\textwidth}
\begin{tabular}{ccc}
\input{intersubeucliddist.tex} & \input{intersubeucliddisteyemovements.tex} & \input{intersubeucliddistfeatureeffects.tex} \\
\end{tabular}
Divergence of fixation locations, eye movements, and effect sizes of features. Metric was median inter-subject Euclidean distance. Eye movements were fixation duration and saccade length. Effect sizes refer to redefined controls. The second and third figure only represents experiments 1 and 2. In the first figure, humans from Osnabr\"uck are blue, Montana monkeys are red. As for Bochum small condition, humans are cyan, monkeys magenta. In the big big screen condition, humans are green and monkeys yellow. Cf. table. \\
\vfill
\scriptsize
\begin{tabular}{lcccccc}
\multicolumn{7}{c}{ divergence of fixation locations } \\
 {} & monkeys & humans & monkeysSS & humansSS & monkeysBS & monkeysBS \\
median  & 273.3697 &  240.4071 &  339.7094 &  329.4333 &  228.7352 &  230.3894  \\ 
std  & 20.0616 &  66.2950 &  106.2013 &  51.3139 &  43.9180 &  39.2206  \\ 
correlations(t) &  0.2309  &  0.9020  &  -0.4952  &  0.3485  &  0.4357  &  0.6995  \\ 
{   } p-values &  0.4271  &  0.0000  &  0.0101  &  0.1216  &  0.0622  &  0.0037  \\ 
correlations & \multicolumn{2}{c}{ 0.3412 } & \multicolumn{2}{c}{ 0.1218 } & \multicolumn{2}{c}{ 0.0920 } \\ 
{   } p-values & \multicolumn{2}{c}{ 0.2325 } & \multicolumn{2}{c}{ 0.5988 } & \multicolumn{2}{c}{ 0.7443 } \\ 
signed-test & \multicolumn{2}{c}{ 1 , p= 0.0017 } & \multicolumn{2}{c}{ 1 , p= 0.0325 } & \multicolumn{2}{c}{ 0 , p= 0.8469 } \\ 
\\
\end{tabular}
\vfill
\begin{tabular}{lr}
	\begin{tabular}{lcc}
		\multicolumn{3}{c}{ divergence of eye movements } \\
		 {} & monkeys & humans \\
		median  & 163.9469 &  181.0036 \\ 
		std & 13.4151 &  19.4164 \\ 
		correlations(t) &  0.1054  &  -0.1586  \\ 
		{   } p-values &  0.7444  &  0.6224  \\ 
		correlations & \multicolumn{2}{c}{ 0.1662 } \\ 
		{   } p-values & \multicolumn{2}{c}{ 0.6057 } \\ 
		signed-test & \multicolumn{2}{c}{ 1 , p= 0.0161 } \\ 
		\\
	\end{tabular}
	\begin{tabular}{lcc}
		\multicolumn{3}{c}{ divergence of features } \\
		 {} & monkeys & humans \\
		median & 1.0725 &  0.6956  \\ 
		std & 0.1034 &  0.1720  \\ 
		correlations(t) &  -0.0182  &  0.9306  \\ 
		{   } p-values &  0.9507  &  0.0000  \\ 
		correlations & \multicolumn{2}{c}{ 0.0612 } \\ {   } p-values & \multicolumn{2}{c}{ 0.8354 } \\ signed-test & \multicolumn{2}{c}{ 1 , p= 0.0001 } \\ 
		\\
	\end{tabular}
\end{tabular}
\normalsize
\vfill
Statistics corresponding to the figures. \textit{Correlations(t)} refers to correlation coefficients when looking at each line as a time series and the line below gives the matching p-values. \textit{Correlations} below compares humans and monkeys, respectively for the two through maximally four cases. 
\caption{Divergence of subjects over time}
\end{minipage} }
%\end{rotate}
\end{figure}
\end{singlespacing}  

% discussion: w�lfel, ekenel, feature weighted mahalanobis distance: improved robustness for gaussian classifiers

%mutual information is symmetric and non-negative
