  This paper describes the development and validation of a continuous
pictographic scale for self-reported assessment of affective states in virtual
environments. The developed tool, called Morph A Mood (MAM), consists of a 3D
character whose facial expression can be adjusted with simple controller
gestures according to the perceived affective state to capture valence and
arousal scores. It was tested against the questionnaires Pick-A-Mood (PAM) and
Self-Assessment Manikin (SAM) in an experiment in which the participants (N =
32) watched several one-minute excerpts from music videos of the DEAP database
within a virtual environment and assessed their mood after each clip. The
experiment showed a high correlation with regard to valence, but only a
moderate one with regard to arousal. No statistically significant differences
were found between the SAM ratings of this experiment and MAM, but between the
valence values of MAM and the DEAP database and between the arousal values of
MAM and PAM. In terms of user experience, MAM and PAM hardly differ.
Furthermore, the experiment showed that assessments inside virtual environments
are significantly faster than with paper-pencil methods, where media devices
such as headphones and display goggles must be put on and taken off.
