2013. Vol.4, No.7, 566-568
Published Online July 2013 in SciRes (http://www.scirp.org/journal/psych) http://dx.doi.org/10.4236/psych.2013.47081
Copyright © 2013 SciRes.
Music Modulates the Strength of Vection
1Faculty of Design, Kyushu Unive rsity, Fukuoka, Japan
2Institute for Advanced Study, Kyushu University, F ukuoka, Japan
3Research Center for Appli ed Perceptual Science, Kyushu University, Fukuoka, Ja p a n
Received April 20th, 2013; revised May 23rd, 2013; accepted June 20th, 2013
Copyright © 2013 Takeharu Seno. This is an open access article distributed under the Creative Commons Attri-
bution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the
original work is properly cited.
We presented four types of music (two fast tempo and two slow tempo types) during illusory self-motion
perception (vection). Vection was induced by expansional dots (optic flow), and participants estimated it s
strength via magnitude estimation and by pressing a button. Our purpose of this study is to examine
whether music alter vection strength. Results showed that vection was facilitated by two fast tempo types
and one slow tempo type of music in lieu of a no-music condition. We speculated that fast tempo, active
music might induce higher arousal levels in participants compared with a no-music condition, and that
higher arousal levels might induce stronger vection. We speculated that this auditory method of modulat-
ing vection strength can be utilized in the virtual reality environment.
Keywords: Vection; Music; Sound; Facilitation
Music is known to influence human behavior (Review in
Miell et al., 2005). For instance, when listening to music with a
fast tempo, supermarket shoppers have been found to move
more quickly around a store, restaurant patrons eat more
quickly, and drinks in pubs are consumed more quickly, than
when listening to music with a slow tempo (Milliman, 1982,
1986; Herrington & Capella, 1996). Additionally, listening to
music with a fast tempo increases the rate and precision of
mathematical computations in stock-market environments
(Mayfield & Moss, 1989), and of self-paced line drawings
(Nittono et al., 2000).
Recent studies have revealed a relationship between music
and self-motion, i.e. driving. Spinney (1997) found that music
exposure during driving facilitated avoidance of driving haz-
ards and increased performance, such as reaction times. In ad-
dition, drivers characterized scenic countryside landscapes as
having more power when listening to music with a fast tempo
(Iwamiya, 1997). Another study found that music tempo con-
sistently affected simulated driving speed, perceived speed, and
the frequency of virtual traffic violations (Brodsky, 2002).
In this study, we focused on the effect of music on illusory
self-motion perception, vection. Vection refers to the pheno-
menon in which a stationary observer experiences compelling
illusory self-motion when exposed to a large visual field of
optic flow (e.g. Seno et al., 2013).
Music has been shown to have the ability to alter human
arousal levels (Gabrielsson, 2001). Specifically, fast tempo
music has been found to induce higher arousal levels while
slow tempo music has been found to induce lower arousal lev-
els (Husain et al., 2002). In our recent study, we found that
vection could also alter human arousal levels; stronger vection
induced higher arousal levels (Ihaya et al., submitted). We hy-
pothesized that the opposite was also possible, i.e., that higher
arousal levels could induce stronger vection. We further hy-
pothesized that when arousal levels were increased by fast
tempo music, these higher arousal levels might induce stronger
vection. In this way, we predicted that fast tempo music could
Stimuli were generated and controlled by a computer (MB
543J/A, Apple) and presented on a plasma display (3D Viera,
50-inch, Panasonic, with 1024 × 768 pixel resolution at a 60 Hz
refresh rate). The experiment was conducted in a dark cham-
Fourteen naïve volunteers participated in this experiment.
Participants were graduate or undergraduate students. All par-
ticipants reported normal vision and no history of vestibular
system diseases. None of them was aware of the purpose of the
Vection was induced in fourteen stationary observers by
pre se nt ing expanding optic flo w. Optic flow displays (72˚ × 57˚;
presented for 30 s) consisted of 16,000 randomly positioned
dots (Seno et al., 2010) with global dot motion to simulate for-
ward self-motion (16 m/s).
Participants were asked to press a button when they per-
ceived forward self-motion, and keep the button depressed for
the duration of self-motion. After each trial, the participants
rated subjective vection strength using a 101-point rating scale
ranging from 0 (no vection) to 100 (very strong vection).
There were five music conditions: two fast, two slow, and a
no-music condition. These conditions were conducted in sepa-
rate sessions. Three trials were conducted for the with-music
conditions and four trials for the without-music condition. For
the two fast music conditions, we used “Bakushou-sengen” (the
theme of a Japanese Pro-wrestler) and the theme music from
the movie “Back to the Future”, and for two slow music condi-
tions, we used Pachelbel’s “Kanon-D-dur” and Debussy’s
“Moon Light”. The three trials were conducted successively
within the same session. After each session, participants rated
the subjective activity of the music using a 101-point rating
scale ranging from 0 (not active) to 100 (very active). The rat-
ing scale was created by the experimenter. We asked the par-
ticipants to subjectively estimate how active they thought the
four types of music were. This value indicated the degree to
which the participants were stimulated by the four types of
music. We hypothesized that the more stimulating and active
the music was rated, the higher the arousal level of the partici-
pants would be, and that the perceived activity of the four types
of music and the strength of their respective degrees of vection
would be correlated. The sound level of the music ranged from
0 dB to +20 dB for each condition. The average loudness of the
four-minute piece of music was consistent. The sounds were
emitted by a stereo-speaker (Computer MusicMonitor, BOUSE)
(Figure 1). All participants participated in all five conditions
(four music and one no music conditions). The order of con-
ducting the five conditions was randomized.
Result and Discussion
The average latencies and durations were shortest and long-
est respectively in the two fast music conditions, shorter in the
two slow music conditions, and slowest in the no-music condi-
tion (Figure 2). A one-way ANOVA revealed a significant
main effect of the five conditions (latency, F(4,52) = 6.01, p
< .01; duration, F(4,52) = 6.89, p < .01)). Multiple comparisons
revealed significant differences between the fast and slow mu-
sic conditions, between the no-music and the two fast condi-
tions, and between the no-music and Kanon-D-dur conditions,
both in terms of latency and duration (Tukey’s HSD, p < .05).
A schematic illustrat ion of the experimental environment.
Results of vection strength (latency, duration and magni-
tude) and subjectiv e ratings of Music activity.
The average magnitudes were largest in the two fast music
conditions and the Kanon-D-dur condition, and smallest in the
Moon Light and no-music conditions. A one-way ANOVA
rev eale d a significant main effect of the five conditions ( F(4,52)
Copyright © 2013 SciRes. 567
Copyright © 2013 SciRes.
= 11.60, p < .01). Multiple comparisons revealed significant
differences between the fast and slow music conditions, be-
tween the no-music and the two fast conditions, and between
the no-music and Kanon-D-dur conditions (Tukey’s HSD, p
< .05). The difference between the Kanon-D-dur and Moon
Light conditions trended towards significance (Tukey’s HSD, p
The average estimated subjective activity of the music was
largest in the two fast music conditions and the Kanon-D-dur
condition, and smallest in the Moon Light condition. A one-
way ANOVA revealed a significant main effect of four condi-
tions (F(3,39) = 56.35, p < .01). Multiple comparisons revealed
significant differences between all combinations of these four
conditions except between the two fast music conditions
(Tukey’s HSD, p < .05).
Here, we could speculate that the fast tempo music types
stimulated the participants and that those music types induced
higher arousal levels in the participants. The correlation be-
tween the subjective activity ratings of the music and the vec-
tion strength values implies that the music modulated arousal
levels,which then modulated vection strength.
When participants listened to more active music, they per-
ceived stronger vection. This result supports our hypothesis.
The estimated subjective activity ratings of the music might
indicate the depth of modulation of the arousal levels in the
The presence of background fast tempo music had a facili-
tating effect on vection. Thus, we are able to present new em-
pirical data related to how music can alter human perception.
Our results suggest that music can alter human visual percep-
tion. Thus, visual illusions combined with musical stimuli, and
the relationship between visual acuity and musical stimuli,
should be examined in future work. The effects of combinations
of visual and musical stimuli on human perception clearly con-
stitute a new and promising topic for vision research.
We speculated that our results indicated that the induced
higher arousal could induce stronger vection. Thus in the future
by modulating participants’ arousal level by presenting not only
music but also some tactile stimuli and visual stimuli, vection
strength can be m o dulated easil y as intended directions.
In our study propose practical use of music to enhance visual
effect, i.e. vection. In some virtual reality environment, vection
should be enhanced by fast tempo music and also it can be
modulated by the tempo of presence music. This can be a great
advantage for the virtual reality contents developers.
I thank Tomomi Ogura for her beautiful figural design. The
author was supported by Funds for the Development of Human
Resources in Science and Technology (Japan Science and
Technology Agency).This work is supported by Program to
Disseminate Tenure Trac king System, MEXT, Japan.
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