2. Background
• Memory for emotional stimuli is enhanced (e.g. Bradley &
Lang, 2000)
• Emotional stimuli: more arousing than neutral stimuli, of
strong valence (positive or negative)
H1: If we have a better memory for positive and negative
arousing music than for neutral music…
… then likely to make its way into our conscious
experience of involuntary musical imagery (INMI)
3. Emotional musical imagery?
Voluntary musical imagery
Experiment participants able to indicate the emotion
expressed in imagined music (Lucas, Schubert, & Halpern,
2010)
Q. transfer to INMI?
Involuntary musical imagery (INMI)
INMI during ‘affective states’ (Williamson et al., 2011)
• Themes of ‘Mood’, ‘Emotion’, ‘Stress’, ‘Surprise’
4. Valence and Arousal
Valence
• Positive tone of earworm music and words, experience
described as ‘pleasant’ (Halpern & Bartlett, 2011)
• Association between INMI frequency and its valence (Liikkanen,
2011)
• Positive emotional engagement with music (Beaman & Williams,
2009) and musical preference (Hemming, 2009; Halpern & Bartlett,
2011) associated with subsequent INMI (level of processing?)
• Music students sometimes attributed INMI to liking the
particular tune (Bailes, 2007)
Arousal
• ‘Entertainment’ factor of INMI (Wammes & Baruss, 2009)
• Mental relaxation and increased physical activity associated
with INMI (Hemming, 2009)
• INMI in ‘low attention states’ (Williamson et al., 2011)
5. Aims
Explore the relationship between involuntary musical
imagery and emotion
• Using findings from a follow-up of Bailes (2006, 2007)
Caveat. Study not designed to test this relationship
6. Method
Experience sampling methods to observe the musical
experiences of respondents from the general population
(Bailes, 2006)
Participants
• N = 47 (21 male)
• Volunteers from greater Western Sydney & undergraduate
psychology students from University of Western Sydney
• aged 18 to 53 years
• Ollen Musical Sophistication Index range 39 – 944
7. Experience Sampling Form (ESF)
• 2 sides of (A4) sheet of paper to be completed when
messaged (Bailes, 2006)
• Introductory section (date, time contacted, time filled out)
8. ESF ctd..
Part B
Completed if hearing music at time of contact
• Up-dated from Bailes (2006) to include laptops and mp3
players as possible sources of music
• Stylistic categories updated to include trance/house/techno,
country, blues, urban (rap, R&B, hip hop) and gospel
Part C
Completed if imagining music at time of contact
• Up-dated with style categories as in Part B
• Questions adapted to accommodate respondents without
musical training
• Tempo/Rhythm added as a potentially important element of
imagined music
9. Procedure
• Briefing session: informed consent sought, distribution of
background questionnaire & revised transliminality
questionnaire
• Participants received pack of 42 ESFs: 1 ESF to be filled
out each time they receive an SMS
• Bulk SMS provider scheduled sending of message
“Please fill out your form” to participants 6 times a day,
over 7 days, between 9am and 9pm
• Quasi-random schedule, with one signal scheduled within
each two-hour time period
• On receipt of SMS, participants to fill out a blank ESF as
soon as possible
10. Results
1,415 ESFs returned (out of a possible 1,974)
Imagining Music
13%
No Music
52% Hearing Music
31%
Both
4%
11. Musical State and Mood
Multinomial logistic regression analysis
DV: musical state at time of contact (hearing, imagining, neither
hearing nor imagining music)
Predictor variables: ratings along Part A mood pairs
915 cases analysed, omnibus chi-square = 129.86, df = 68,
p < .005
Model accounted for 13.2% - 15.2% of variance
Only Alert/Drowsy (p = .01) and Lonely/Connected (p = .05)
reliably predicted musical state
12. INMI and Mood
Alert/Drowsy
Being ‘drowsy’ or ‘neither alert nor drowsy’ significantly negative
predictor of imagining music
Lonely/Connected
‘Quite connected’ ratings significant predictor of imagining music
Energetic/Tired
Being ‘neither energetic nor tired’ significantly negative predictor
of imagining music
Happy/Sad
Ratings DO NOT predict imagining music
NB. Similar model coefficients for odds of hearing music
(‘drowsy’ and ‘neither energetic nor tired’ as negative predictors)
13. Imagining music from heard episodes
Degree of choice in heard music
• No correlation with the times subsequently imagined
(rho(164) = .106, p = .17)
• No difference between the reported degree of personal
choice when hearing pieces that were imagined versus
those that were not (U = 866.5, N1 = 152, N2 = 14, p =
.226, two-tailed)
Heard and imagined music mood congruency
All mood pair ratings significantly correlated when
participants hear and imagine the same piece (except for
Alert/Drowsy)
14. Reasons for imagining particular music
Node References % of references
Recently heard 61 37.7
Don’t know why 19 11.7
Stickiness 11 6.8
TV 7 4.3
Spontaneity 7 4.3
Recently imagined 6 3.7
Value judgement 5 3.1
Musical features 5 3.1
Favourite music 5 3.1
Visual cue 4 2.5
Recently sung/played 3 1.9
Imagery on waking 3 1.9
Intentional imaging 3 1.9
Sentimental/nostalgia 3 1.9
Other 20 12.3
15. Arousal and Valence
When I exercise I usually listen to
music on an ipod shuffle. I didn’t
It’s annoyingly cheesy have it with me today, so I usually
just hear the same songs in my
Like the song head
I just love it
Fun song
The other girls at work love it, I
dislike it very much
Maybe because it’s a
favourite song of mine
As cleaning is boring –
it is much easier to
It was sentimental value Good song
imagine some thing
Bored in class. When
Was the dance/music @ my I’m bored I imagine
wedding music. Also at work.
16. Conclusions
• Mood pairs that vary in arousal (Alert/Drowsy,
Energetic/Tired) predict the likelihood of imagining music
• Drowsy respondents, or respondents who are at neither end of the
alert/drowsy and energetic/tired scales are not likely to have been
imagining music
• Mood pairs that vary in valence (e.g. Happy/Sad) do not
predict the likelihood of imagining music
• BUT mood congruence at level of specific piece, includes
Happy/Sad
• Relatively small percentage of affective reasons given for
imagining music, comparable to percentage of codes for
‘affective state’ in Williamson et al. (2011)
17. Discussion
• ‘Use’ of imagery as emotional self-regulation (comparable
mood as when hearing)?
• Low attention states & INMI in Williamson et al. (2011),
but current respondents ‘quite connected’
• Diffuse attention? Herbert (2011) – absorption and everyday
listening experience, trance and earworm characteristics
(repetition)
Q. Do affective associations with ‘real’ life influence our
mental jukebox?
18. Research Directions
Need to distinguish between…
• Emotion/mood
• Combine experience sampling methods with state-trait measures to
explore interactions between mood, personality, and INMI
• Perceived vs. induced emotion
• Emotion of imagined music vs. self during episode
• Emotion at encoding of music, of musical content, and at
retrieval of music
• Develop experiments to compare the induction of affective with
neutral music
19. Acknowledgements
Many thanks for the symposium organization.
Thanks particularly to my stoic respondents, as well as to
postgraduate diploma students Sarah Allen, Vicky Busuttil,
Samar Dawidar and Asma Payara for data collection.
