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\title{DOH assessment for the Uncanny Valley Hypothesis}
\author{Name}
\date{\today}

\begin{document}
\maketitle

\begin{abstract}
The Uncanny Valley Hypothesis (UVH, proposed in 1970s) suggests that looking at or interacting with almost human-like artificial characters would trigger eeriness or discomfort. We studied how well subjects can assess degrees of human-likeness for computer-generated characters. We conducted two studies, where subjects were asked to assess human-likeness of given computer-generated models (Study 1) and to point the most typical model for a given category (Study 2). The results suggest that evaluation of the way human-likeness is assessed should be an internal part of UVH research.


\vspace{6pt}

\noindent \textbf{Keywords:} Uncanny Valley Hypothesis, human-likeness, categorisation, computer-generated models
\end{abstract}




\section{Introduction}

The Ucanny Valley Hypothesis (hereafter UVH) has been formulated by Masahiro Mori (see \citealt{mori}). Mori hypothesises that when we will present a subject with a series of different human-like models (including robots) certain models would trigger negative reactions (uneasiness, eeriness). As he claims these will be almost human-like characters. We may imagine models presented in order, from least human-like (like e.g. robotic arm) to the most human-like ones on the $X$ axis. On the $Y$ axis we would present affinity level. According to Mori's suggestion we would observe a growing level of affinity as we move towards human-like models, but at a certain level of human-likeness the level of affinity will rapidly get lower, this is the `valley' (or as MacDorman and Norri Kageki call it `descent into eeriness'---\citealt{mori}). See Figure \ref{fig:valley}.

\begin{figure}
\centering
\includegraphics[width=\textwidth]{uvh}
\caption{Uncanny valley}
\label{fig:valley}
\end{figure}

As such, UVH gained much attention in both, research. As \cite{review} note the reason for this is the wide range of applicability and importance of this hypothesis for a wide range of disciplines, from social robotics to computer animation and game design. 

\begin{quote}
Considering the questions that must be covered, we tried to choose a not very large number of characters to avoid a too long form. Criteria for choosing the characters were set in order to obtain a group that allowed us to cover all the questions above. The first criterion was the human likeness of each character. This feature is necessary because it is present in the uncanny valley graph (the horizontal axis). [...] To cover the human likeness feature, we chosen characters intended to "represent" accurately a human [...] 
\end{quote}

What we want to verify in our studies is whether such a choice is so simple and may be done authoritatively. We want to check how difficult is DOH assessment for a given set of models and also how difficult is to evaluate a given model as typical for a certain category, like e.g. as a robot or an android.

\section{Results}\label{results}


Table \ref{tab:first} presents results of DOH.

\begin{table}
\centering
\caption{Human-likeness assessment for the Study 1}
\begin{tabular}{lllllll}\toprule
\textbf{Model} & \textbf{Score}  & \textbf{Min}  & \textbf{Max} & \textbf{Range} & \textbf{SD} & \textbf{IQR} \\ \midrule
M2 (robot)       & 3   & 1 & 4 & 3 & 0.76 & 1 \\
M13 (robot)      & 1   & 1 & 3 & 2 & 0.73 & 1 \\
M14 (robot)      & 2.5 & 1 & 4 & 3 & 0.68 & 1 \\ \bottomrule
\end{tabular}
\label{tab:first}
\end{table}

IQR analysis for the study sample allows for identifying three models which differ from others: M8, M9 and M10. 

\section{Summary and discussion}\label{discussion}

	
The results presented in the previous section do not support our hypothesis for this research. Especially negative results for (H1) and (H2) are important in our opinion. We started with arbitrary set of computer rendered models and grouped them into 5 categories which (in our view) reflect the growing human-likeness. Expectation was that the set could be used in the UVH research (constituting $X$ axis). What the results suggest, our models were differently assessed by subjects. This supports claims presented by \cite{review}, that the human-likeness assessment may be biased by many external factors. This leads to a conclusion that such analysis of the uniformity of human-likeness assessment should be an important element for UVH research.



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