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Saari, Puttonen, & Keltikangas-Järvinen, 2008;
Ryan, Rigby, & Przybylski, 2006). Gaming is a
joyful and affective activity that provides emo-
tional experiences and these experiences may
guide how we process information.
Regarding emotions, Norman's (2004) defini-
tion is that emotion works through neurochemical
transmitters which influence areas of our brain
and successively guide our behaviour and modify
how we perceive information and make decisions.
While Norman makes a fine distinction between
affect and cognition , he also suggests that both
are information-processing systems with dif-
ferent functionality. Cognition refers to making
sense of the information that we are presented
with, whereas affect refers to the immediate “gut
reaction” or feeling that is triggered by an object,
a situation, or even a thought. Humans strive to
maximize their knowledge by accumulating novel,
but also interpretative information. Experiencing
novel information and being able to interpret it may
be a cause of neurophysiological pleasure (Bie-
dermann & Vessel, 2006). Cognitive processing
of novel information activates endorphins in the
brain, which moderate the sensation of pleasure.
Thus, presenting novel cues in a game environ-
ment will affect and mediate player experience
and in-game learning. This is an excellent example
of how cognition and affect mutually influence
each other, which is in line with modern emotion
theories (Damasio, 1994; LeDoux, 1998; Norman,
2004). Norman (2004) proceeds to define emotion
as consciously experienced affect, which allows
us to identify, who (or what) caused our affective
response and why. The problem of not making a
clear distinction between emotion and affect is
further addressed by Bentley, Johnston, & von
Baggo (2005), who recall Plutchik's (2001) view
on emotion as an accumulated feeling which is
influenced by context, experience, personality,
affective state, and cognitive interpretation. They
also explain that user experience for desktop soft-
ware or office-based systems is more dependent
on performance factors while, for digital games,
user experience depends much more on affective
factors. Affect is defined as a discrete, conscious,
subjective feeling that contributes to, and influ-
ences, an individual's emotion (Bentley, et al.,
2005; Damasio, 1994; Russell, 1980). We will
revisit this notion later in the text.
In addition, Moffat (1980) introduced an in-
teresting notion about the relationships between
personality and emotion, which are distinguished
along the two dimensions: duration (brief and
permanent) and focus (focused and global). For
example, an emotion might develop from brief
affection into a long-term sentiment or a mood
that occurs steadily might become a personal-
ity trait. The two dimensions can be plausibly
identified at a cognitive level, making a strong
case for the relation between emotion, cognition,
and personality both at the surface and at a deep,
structural level.
Psychophysiological research shows that
affective psychophysiological responses elicit
more activity (on facial muscles such as cor-
rugator supercilii, indicating negative appraisal)
and higher arousal when people have to process
unpleasant sound cues (e.g., bomb sounds), which
shows that sound cues can be used in games to
influence players' emotional reactions (Bradley
& Lang, 2000). Sound and music are generally
known to enhance the immersion in a gaming
situation (Grimshaw, 2008a). To music has been
attributed also a facilitation of absorption in an
activity (Rhodes, David, & Combs, 1988), and
it is generally know to trigger the mesolimbic
reward system in the human brain (Menon &
Levitin, 2005), allowing for music to function as
a reward mechanism in game design and possibly
allowing for reinforcement learning (Quilitch &
Risley, 1973). The recent explosion of interac-
tive music games is a testament to the pleasure-
enhancing function of music in games. Examples
for interactive music games are Audiosurf (2008),
the Guitar Hero series (2005-2009), SingStar
(2004), or WiiMusic (2008). They make heavy
use of reinforcement learning, as both positive
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