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Erwan Normand
2024-10-18 15:09:57 +02:00
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2-perception/vhar-textures/3-results.tex
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@@ -83,11 +83,12 @@ It seems that the second dimension opposed textures that were perceived as hard
Stiffness is indeed an important perceptual dimension of a material (\secref[related_work]{hardness}).% \cite{okamoto2013psychophysical,culbertson2014modeling}.
\fig[0.6]{results/matching_correspondence_analysis}{
Correspondence analysis of the \level{Matching} task confusion matrix (\figref{results/matching_confusion_matrix}).
Correspondence analysis of the confusion matrix of the \level{Matching} task.
}[
%The haptic textures are represented as green squares, the haptic textures as red circles. %
The closer the haptic and visual textures are, the more similar they were judged. %
The first dimension (horizontal axis) explains \percent{60} of the variance, the second dimension (vertical axis) explains \percent{30} of the variance.
The confusion matrix is \figref{results/matching_confusion_matrix}.
]
\paragraph{Hierarchical Clustering}

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2-perception/xr-perception/4-results.tex
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@@ -73,7 +73,7 @@ This means that within the same time window on the same surface, participants ex
%\figref{results/question_heatmaps} shows the median and interquartile range (IQR) ratings to the questions in \tabref{questions} and to the NASA-TLX questionnaire.
%
Friedman tests were employed to compare the ratings to the questions (\tabref{questions}), with post-hoc Wilcoxon signed-rank tests and Holm-Bonferroni adjustment, except for the questions regarding the virtual hand that were directly compared with Wilcoxon signed-rank tests.
Friedman tests were employed to compare the ratings to the questions (\tabref{questions1} and \tabref{questions2}), with post-hoc Wilcoxon signed-rank tests and Holm-Bonferroni adjustment, except for the questions regarding the virtual hand that were directly compared with Wilcoxon signed-rank tests.
\figref{results_questions} shows these ratings for questions where statistically significant differences were found (results are shown as mean $\pm$ standard deviation):
\begin{itemize}