Earworms from three anglesVictoria Williamson & Daniel MüllensiefenA British Academy funded project run by the Music, Mind andBrain Group at Goldsmiths in collaboration with BBC 6Music
Points of contact for our studies Earwormery.com BBC 6Music site (Short reports, emails and texts)
What is left unanswered…1. What triggers earworms in everyday life? Do they have a purpose?2. Are some personalities more vulnerable than others?3. What makes a tune sticky?
Project 1: Everyday triggersWhat triggers earworms? Method: Qualitative analysis (grounded theory) of earworm episodes Result: Identification of high-risk situations Do they have a use?
Williamson et al., (2012) Psychology of Music Earworm reports coded using grounded theory analysis techniques (2 independent raters) 6 Music corpus: 333 reports = 942 codes Survey (.com) corpus: 271 reports = 657 codes Two models of codes show everyday earworm triggers and their relations Emphasise importance of musical exposure but also memory function, and cognitive and affective state.
Some examples of memorable reports... Stress - My ear worm is ‘Nathan Jones by Bananarama. I first caught it in 1989 during my GCSE chemistry exam and have been plagued by it in moments of extreme stress since, e.g. wedding, childbirth etc” (6Music Text). Person Association- My earworm today is ‘This Charming Man by The Smiths because every time I see David Cameron, that song just appears in my head, for some particular reason” (6Music Emails)
Musical media1. Live Music (e.g. concerts or gigs)1.Video Media (e.g. TV, film, internet site)3. Radio4. Private Music (e.g. in the home or the car)5. Contagion (e.g. another individual singing or humming)6. Learning (e.g. practising for performance or a lesson)7. Public Music (e.g. restaurant, shop or gym)8. Ringtones
Discussion Musical exposure – ubiquity (Sacks, 2007; Beaman & Williams, 2010; Liikkanen, 2012) But also non musical association triggers in (involuntary) memory Heightened emotional states (including Media): Levels of encoding = ‘resurfacing’ potential?
Project 2: Individual differencesAre some people more vulnerable than others? Method: Statistical analysis of personality inventory (OCI-R) and factors of musical behaviour questionnaire (MuBQ) in relation to INMI factors (earwormery.com)
Müllensiefen et al. (in review) Why are we interested in OC trait? “people with obsessive compulsive disorder are more likely to report being troubled by earworms – in some cases medications for OCD can minimise the effects” (Levitin, 2006, p.151) Let’s find out …
HypothesesIndividuals who measure highly on sub-clinicalOC will experience more INMI that is moredisturbing (Garcia-Soriano, Belloch, Morillo, & Clark, 2011)People who are more ‘musical’ will experiencemore frequent earworms (INMI) that arelonger and more troubling (Beaman & Williams, 2011;Liikkanen, 2012)
Method 1536 participants (58.1% women). M Age = 34.2, SD = 12.6, range: 12-75 Exploratory analysis (n=512): ◦ Factor analysis of musical behaviour and INMI questionnaire Confirmatory analysis (n=1024): ◦ Structural equation modelling to test hypotheses between OC, musical behaviour, and INMI.
Testing HypothesesStructural Equation Modelling:◦ Only some hypotheses confirmed◦ Good fit of final model: adjusted goodness-of-fit = 0.929 RMSEA index = 0.06
Results:Only Singing is linked(positively) to INMIBut: Singing makes INMI morepleasantOC traits = INMIFrequency andDisturbanceMediated evaluativeresponse between OC &INMI Length:High OC => INMI disturbing =>longer INMIsSimilar paradoxical relationshipsfound in OCD (Wegner et al., 1987)
To follow up Should we be medicating earworms with OCD drugs?... Should we prescribe singing to OCD patients? …
Posters on individual differences G. A. Floridou, V. J. Williamson, D. Müllensiefen “Contracting Earworms: The Roles of Personality and Musicality” (Friday 3.30pm) M. Wammes, D. Müllensiefen,V.J. Williamson: “Schizotypal Influences on Musical Imagery Experience” (Wednesday 11am)
Project 3: Stickiness of tunesWhat is it that makes a tune sticky? Method: Computational analysis of tunes from frequently reported earworms Tools: FANTASTIC software package 2 ∑ (∆p i i − ∆p ) = 2.83 i.abs.std = N −1 Result: Classification model predicting stickiness
Step 1: Gathering earworms• ~2000 participants (.com survey)• 1960 different earworm tunes (Artist, song title, exact part)• Top earworm list: 5.5% of songs identifiable and named at least 3 times
Method1. Control for popularity and recency and find ‘sticky tunes’: => tunes with a positive residual after poisson regression (using popularity data as predictors)2. Find tunes most similar to INMI tunes (match by genre and chart success etc.)3. Use melodic features (Müllensiefen, 2009) of tunes to predict INMI vs non-INMI tunes (logistic regression)
Data Most frequent earworm tunes: artist song incs hi.entry weeks entry.date exit.date genrelady gaga bad romance 13 1 38 281 15 poplady gaga alejandro 11 7 10 253 183 pop dont stopjourney believing 11 6 47 477 149 rockkaty perry california gurls 10 1 6 43 1 pop bohemianqueen rhapsody 7 1 17 12699 12580 rock Similarly successful but never mentioned as earworms: artist song incs hi.entry weeks entry.date exit.date genregorillaz feel good inc. 0 2 39 1940 1667 pop these boots arejessica simpson made for walkin 0 4 10 1800 1730 pop handbags andstereophonics gladrags 0 4 15 3164 3059 rocknelly my place 0 1 11 2164 2087 popelvis presley way down 0 1 13 12054 11963 rock
Earworm classification model 1p (earworm = 1) = −(1.079+ 0.064 ⋅ d.median -0.723 ⋅ i.leaps) 1+ e= Longer durations and smaller intervals make tunes sticky (maybe because they are easier to sing?)BUT results only preliminary, because: • Melody only one aspect of INMI • Small sample (58 songs) • Interactions of features • Different types of earworms => different structural models? Latest analysis on 214 tunes: Sebastian Finkel (Friday 3.30pm poster session)
FINAL conclusionsMusical exposure important (Sacks, 2007; Bailes, 2012) that is recentand repeated (Beaman & Williams, 2010); but so is the activity of non-musical, involuntary memoriesState of mental arousal (wakefulness, excitement and stress)and ‘mind wandering’ – a possible function?(Leverhulme Grant)Singing behaviour predicts features of INMI plus ease ofsinging may predict stickiness: activity of brain areas?Melodic structure alone is a powerful predictor of inherentstickinessMulti-method approach for generating future hypotheses
icmpc12earworms.com Special thanks to Sagar Jilka, Sebastian Finkel, Josh Fry, Alex Handler, Mandi Goldberg, Andre Lira & all at the BBC
THANK YOU! YOU! MUSICPSYCHOLOGY.CO.UK (LIVE- (LIVE-ISH BLOG OF ICMPC/ESCOM) QUESTIONS??This project was kindly supported by: