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Frontiers in Psychology 01 frontiersin.org
Elements of musical and dance
sophistication predict musical
groove perception
Samantha R. O’Connell
1
*, Jessica E. Nave-Blodgett
2, Grace E.
Wilson
2, Erin E. Hannon
2 and Joel S. Snyder
2*
1 Caruso Department of Otolaryngology, Head and Neck Surgery, Keck School of Medicine of USC,
University of Southern California, Los Angeles, CA, United States, 2 Department of Psychology,
University of Nevada, Las Vegas, NV, United States
Listening to groovy music is an enjoyable experience and a common human
behavior in some cultures. Specifically, many listeners agree that songs
they find to bemore familiar and pleasurable are more likely to induce the
experience of musical groove. While the pleasurable and dance-inducing
eects of musical groove are omnipresent, we know less about how
subjective feelings toward music, individual musical or dance experiences,
or more objective musical perception abilities are correlated with the way
weexperience groove. Therefore, the present study aimed to evaluate how
musical and dance sophistication relates to musical groove perception. One-
hundred 24 participants completed an online study during which they rated
20 songs, considered high- or low-groove, and completed the Goldsmiths
Musical Sophistication Index, the Goldsmiths Dance Sophistication Index, the
Beat and Meter Sensitivity Task, and a modified short version of the Profile for
Music Perception Skills. Our results reveal that measures of perceptual abilities,
musical training, and social dancing predicted the dierence in groove rating
between high- and low-groove music. Overall, these findings support the
notion that listeners’ individual experiences and predispositions may shape
their perception of musical groove, although other causal directions are also
possible. This research helps elucidate the correlates and possible causes of
musical groove perception in a wide range of listeners.
KEYWORDS
auditory perception, musical sophistication, dance sophistication, groove, online
studies
Introduction
Moving to music is a common and pleasurable human behavior. Certain songs groove
in that they encourage spontaneous movement and feelings of enjoyment (Madison, 2006;
Madison etal., 2011; Janata etal., 2012; Matthews etal., 2020). Musical groove is recognized
as a characteristic of songs encompassing genres such as jazz, pop, rock, hip hop, R&B, soul,
and funk, made popular by artists like Stevie Wonder, Michael Jackson, and James Brown
(Danielsen, 2006). e origins of groove are thought to berooted in West African rhythms
TYPE Original Research
PUBLISHED 17 November 2022
DOI 10.3389/fpsyg.2022.998321
OPEN ACCESS
EDITED BY
Evangelos Himonides,
University College London,
UnitedKingdom
REVIEWED BY
Olivier Senn,
Lucerne University of Applied Sciences and
Arts, Switzerland
Chen-Gia Tsai,
National Taiwan University, Taiwan
*CORRESPONDENCE
Samantha R. O’Connell
sroconne@usc.edu
Joel S. Snyder
joel.snyder@unlv.edu
SPECIALTY SECTION
This article was submitted to
Performance Science,
a section of the journal
Frontiers in Psychology
RECEIVED 26 July 2022
ACCEPTED 21 October 2022
PUBLISHED 17 November 2022
CITATION
O’Connell SR, Nave-Blodgett JE,
Wilson GE, Hannon EE and
Snyder JS (2022) Elements of musical and
dance sophistication predict musical
groove perception.
Front. Psychol. 13:998321.
doi: 10.3389/fpsyg.2022.998321
COPYRIGHT
© 2022 O’Connell, Nave-Blodgett, Wilson,
Hannon and Snyder. This is an open-access
article distributed under the terms of the
Creative Commons Attribution License (CC
BY). The use, distribution or reproduction in
other forums is permitted, provided the
original author(s) and the copyright
owner(s) are credited and that the original
publication in this journal is cited, in
accordance with accepted academic
practice. No use, distribution or
reproduction is permitted which does not
comply with these terms.
O’Connell et al. 10.3389/fpsyg.2022.998321
Frontiers in Psychology 02 frontiersin.org
(Pressing, 2002). Early songs with groove are oen associated with
swing, a type of jazz music composed of “swinging” rhythms in
which the beat is unevenly subdivided to sound like a lilt
(Buttereld, 2010b). As music evolved, groove became an
umbrella term describing a phenomenon in which musical
rhythms invoke movement (Iyer, 2002). Songs with musical
groove have become popular as naturalistic stimuli to study
interactions between auditory and motor brain regions (Zatorre
et al., 2007; Patel and Iversen, 2014). Listening to songs with
groove can enhance performance on a range of physical tasks
(Karageorghis and Terry, 1997; Styns etal., 2007; Buhmann etal.,
2016) by eliciting longer strides and faster steps while walking
(Leow etal., 2014), running (Edworthy and Waring, 2006), and
rowing (Rendi et al., 2016). Even without accompanying
movement, just listening to music with groove may have the
power to excite neurons in the motor system (Wilson and Davey,
2002; Stupacher etal., 2013; Ross etal., 2016; Matthews etal.,
2020; Martín-Fernández etal., 2021). As a result, musical groove
listening is gaining traction as an enjoyable and therapeutic gait
treatment for movement-related disorders such as Parkinson’s
disease (Nombela etal., 2013; Leow etal., 2014).
To understand this musical phenomenon, researchers have
studied the specic auditory components that may contribute to
the sensation of groove (Stupacher et al., 2016a). Converging
empirical evidence indicates that timing-based auditory properties
such as a salient, low-pitched beat (Drake etal., 2000; Madison
etal., 2011; Burger etal., 2012; Janata etal., 2012; Stupacher etal.,
2016a; Hove et al., 2019), moderate rhythmic complexity
(Temperley, 1999; Danielsen etal., 2014; Madison and Sioros,
2014; Sioros et al., 2014; Witek etal., 2014; Wesolowski and
Hofmann, 2016; Witek, 2017; Matthews et al., 2019), and a
medium tempo of about 120 beats per minute (MacDougall and
Moore, 2005; Styns etal., 2007; Kornysheva etal., 2010; Janata
etal., 2012; Leow et al., 2014; Michaelis etal., 2014; Stupacher
etal., 2016a; Etani etal., 2018; Liu etal., 2018) have all been
described as dening characteristics of musical groove. Beat-based
musical elements may also activate neural motor networks.
Listening to beat-based rhythms related to groove, without
accompanying physical movement, engages auditory (Snyder and
Large, 2005; Fujioka etal., 2009), prefrontal (Fukuie etal., 2022),
and sensorimotor brain regions (Grahn and Brett, 2007; Grahn
and Rowe, 2009, 2013; Fujioka etal., 2012). Additionally, listening
to beats and rhythms can encourage kinesthetic movement by
providing a temporal anchor to synchronize our bodies to the
music (Iyer, 2002; Leman, 2012; Leow etal., 2021) and with one
another (Kokal etal., 2011; Cirelli etal., 2014; Stupacher etal.,
2017a,b). Performing synchronized movements can lead to
arousal (Bowling et al., 2019), activation of reward networks
(Menon and Levitin, 2005; Kokal etal., 2011; Zatorre, 2015;
Matthews et al., 2020), and the release of feel-good
neurotransmitters such as endorphins and oxytocin (Tarr etal.,
2014, 2015; Josef etal., 2019), likely contributing to the overall
enjoyable experience of being “in the groove” (Madison, 2006; De
Bruyn etal., 2009; Janata etal., 2012).
ere is a consensus that those with formal music training may
have enhanced auditory perception (Kraus and Chandrasekaran,
2010; Strait etal., 2012, 2014, 2015; Kraus etal., 2014; Slater etal.,
2015; Habibi etal., 2016) and emotional responses to music (Blood
and Zatorre, 2001; Liu etal., 2018); however, there is a lack of
consensus regarding how musical expertise may shape perception of
musical groove. On one hand, research indicates that musicians’
perception of groove may beenhanced compared to non-musicians
(Stupacher etal., 2013; Ross et al., 2016; Matthews etal., 2019).
Musicians’ responsiveness to musical groove may beattributed to
their ability to hear minute changes in acoustic elements better than
non-musicians (Stupacher etal., 2016b). Musicians, compared to
non-musicians, potentially have more awareness of musical elements
important to musical groove such as harmonic complexity
(Matthews etal., 2019), rhythmic complexity (Grahn and Rowe,
2009; Stupacher etal., 2017c; Matthews etal., 2019), tempo (Etani
etal., 2018), syncopation (Madison and Sioros, 2014; Witek etal.,
2014; Senn et al., 2018; Matthews et al., 2019), micro-timing
deviations (Davies etal., 2013; Kilchenmann and Senn, 2015; Senn
etal., 2016), and beat perception (Grahn and Rowe, 2009; Stupacher
et al., 2017c; Nguyen et al., 2022). Additionally, relative to
non-musicians, musicians’ motor systems may react more robustly
to music with groove (Stupacher etal., 2013), possibly allowing for
better balance control (Ross etal., 2016). is could arise from
extensive training involving the synchronization of movements to
the beat when producing musical sounds (Stupacher etal., 2013),
resulting in stronger integration between perceptual and motor
brain networks (Zatorre etal., 2007; Luo et al., 2012; Patel and
Iversen, 2014; Martín-Fernández etal., 2021).
On the other hand, movement to music with groove may bea
phenomenon experienced by a wide range of listeners (Madison,
2006; Madison etal., 2011; Janata etal., 2012), and thus formal
expertise may beunnecessary for musical groove perception. For
example, multiple studies have found no dierences between
musicians and non-musicians in their susceptibility to groove
(Buttereld, 2010a; Frühauf etal., 2013; Hofmann etal., 2017).
Most recently, Senn et al. (2019b) showed only marginal main
eects of musical expertise on groove ratings when comparing
musicians, amateur musicians, and non-musicians. In another
study, non-musicians perceived music as groovier than musicians
(Witek et al., 2014). Across these studies, musicians and
non-musicians tend to agree on which songs are more or less
“groovy”; however, their musical experiences may drive their
preference for groove genres with higher or lower levels of musical
complexity. For example, while musicians may rate more complex
music, like jazz and funk, to be “groovier” (Pressing, 2002;
Matthews etal., 2019), non-musicians may beinclined to rate pop
and rock higher in groove because it is less complex and more
familiar (Senn etal., 2021a). Taken together, factors such as innate
biological traits, musical preferences, and musical exposure, rather
than musical skills gained from playing an instrument, may have
equal or greater eects on how weperceive the groove.
While previous research has revealed brain and behavior
enhancements due to music training (Kraus and Chandrasekaran,
O’Connell et al. 10.3389/fpsyg.2022.998321
Frontiers in Psychology 03 frontiersin.org
2010; Skoe and Kraus, 2010; Strait and Kraus, 2011; Slater and
Kraus, 2016), musicality varies within populations of those with
and without musical expertise (Zatorre, 2013; Nave-Blodgett etal.,
2021a,b). is may be because biological and environmental
benets may contribute to heightened musicality in both
musicians and non-musicians. In some instances, musicality may
becultivated due to an availability of resources (Corrigall etal.,
2013). In other instances, one’s musicality may bea predisposed
trait (Peretz etal., 2007; Tan etal., 2014; Mankel and Bidelman,
2018) that remains somewhat hidden due to a lack of nancial or
familial support (Schellenberg, 2015) or a lack of interest in learning
to play music; however, some of these untrained individuals may
become avid music appreciators and develop similar skills to
musicians through hours of listening or other activities such as
playing music video games (Pasinski etal., 2016). Furthermore, in
both musicians and non-musicians, musical ability (Swaminathan
and Schellenberg, 2018) and appreciation for certain types of
music may bedictated by one’s personality (McCrae, 2007; Luck
etal., 2010; Nusbaum etal., 2014; Colver and El-Alayli, 2015;
Swaminathan and Schellenberg, 2018; Kuckelkorn etal., 2021)
and music preferences (Madison, 2006; Salimpoor etal., 2013;
Wesolowski and Hofmann, 2016; Madison and Schiölde, 2017;
Senn etal., 2019b, 2021a; Kowalewski et al., 2020). erefore,
there is a growing need to understand individual dierences in
music perception that are not based on formal music training.
Groove has oen been studied in the context of music
performance: playing the music of a particular genre (e.g., jazz and
funk), how the music is performed, or the enjoyable sensation of
being “in the pocket” when musicians synchronize with the music
and with one another (Berliner, 1994; Hosken, 2021). Historically,
however, much of music was written for the purposes of dancing to
music. For instance, songs from music genres known for groove
rhythms, such as jazz or Afro-Cuban music, were rst composed to
accompany dance forms such as tap dance, swing dance (Madison,
2006), and Latin dances (Hughes, 2001). Oentimes, it is hard to
explain the feeling of groove without mentioning “movement” or
“dancing.” While there is an undeniable connection between
musical groove and dance (Merker, 2014; Fitch, 2016), there is a
surprising dearth of empirical studies investigating the inuence of
dance experience on musical groove perception (Bernardi etal.,
2017), or even general music perception.
As is the case with musical listening skills, dance-related skills
may behard to predict. Some dancers, like ballerinas, may possess
years of professional training with a dance company while others
may have years of self-taught experience dancing socially in a club.
With the rise of social media, dance access has also become more
widespread. Today, anyone with access to phone applications like
TikTok can create, share, and learn dance choreography without
having any prior experience. Dance experience or expertise may
also behard to assess because it can bedicult to disassociate from
musical experience. For instance, tap dance straddles the ne line
of being both music and dance because the art form equally values
the importance of rhythmic sounds and movement. For this
reason, many tap dancers identify as both musicians and dancers
(Hill, 2010). In fact, the division of music and dance seems bea
Western-focused mindset (Trehub etal., 2015). For example, in
Nigeria and in India the very same term (nkwa and sangeet,
respectively) is used for musical performance and dance (Balkwill
and ompson, 1999; Clayton, 2000). As the term groove itself is
at the intersection of dance and music, it is important to study the
inuence of dance experience on musical groove perception
regardless of one’s dance experience or how dance is identied.
Trained dancers, compared to non-dancers or non-trained
dancers, may possess heightened functioning of sensorineural
networks that may enhance their perception of musical groove.
For instance, those with dance training show increased cortical
thickness in superior temporal brain regions compared to
non-experts (Karpati etal., 2017): these regions are vital to the
auditory-motor integration network used during music listening
and production (Bangert etal., 2006; Zatorre etal., 2007; Gordon
etal., 2018). Additionally, trained dancers reveal enhancements in
sensorimotor integration (Karpati et al., 2016) and appear to
outperform non-trained dancers and non-musicians in
audiovisual beat perception and production tasks (Nguyen etal.,
2022). Furthermore, trained dancers, like trained musicians, show
cortical phase synchrony in beta and gamma frequency bands
during passive viewing of dance with music (Poikonen et al.,
2018). ese frequency bands have been implicated in musical
beat encoding and auditory-motor brain interactions (Fujioka
etal., 2009). Together, these studies suggest that dancers may
exhibit training-induced neuroplasticity in sensorimotor regions
that may engender heightened perception of the musical beat- a
crucial component of musical groove.
Although dance expertise may hone music perception, feeling
the groove may not bedependent on having superior perceptual
or motor skills. Instead, the pleasure wefeel from listening to
music with groove may depend on our physical movement with
music. For instance, those without formal dance training felt the
most pleasure and arousal when moving spontaneously to high-
groove music compared to low-groove music or when listening to
music without movement (Bernardi et al., 2017). is may
bebecause moving to music helps us understand the beat and
meter through embodiment (Phillips-Silver and Trainor, 2006,
2008; Leman, 2012; Chemin et al., 2014; Lee etal., 2015). e habit
of moving to music may also facilitate enjoyment of music with
groove. Head movements to the beat of the music produce
vestibular self-stimulated responses that may play an integral role
in the understanding of musical beat (Phillips-Silver and Trainor,
2007; Todd and Lee, 2015), and meter (Phillips-Silver and Trainor,
2008; Trainor etal., 2009), and may activate brain circuits involved
in reward (Todd and Lee, 2015; Reybrouck etal., 2019).
Additionally, high-groove music can strengthen the link
between beat and movement because it tends to besyncopated
(Janata etal., 2012; Witek, 2017; Witek etal., 2017), and the
experience of syncopation depends on a strong, internally
maintained beat (Pressing, 2002; Keller and Schubert, 2011; Sioros
etal., 2014; Witek and Clarke, 2014). Knowing the locations of
beats in time can help us synchronize our movements with the
O’Connell et al. 10.3389/fpsyg.2022.998321
Frontiers in Psychology 04 frontiersin.org
music and with others (De Bruyn etal., 2009). Past experiences
moving to the music may also facilitate meter awareness. ose
without formal dance training, but with experience dancing
specic choreography, were better at tapping along to the music’s
beat than those who did not learn the choreography (Lee etal.,
2015). Furthermore, dance familiarity can beacquired through
observation. Frequent spectators of dance, compared to novice
dance spectators, showed increased corticospinal excitability as
they viewed the form of dance with which they were most familiar
(Jola etal., 2012). What is unclear, however, is whether these
increases in meter perception and motor activation due to
repeated dance observation translate to a heightened perception
of musical groove. erefore, there is a great need for investigations
that directly study dierences in music perception in those with
varying degrees of dance experience.
In the present study, weinvestigated how musical and dance
sophistication may inuence musical groove perception in adult
listeners with a wide range of artistic experiences. e rst aim of
this investigation was to understand how variations in musical
sophistication predict musical groove perception. Specically,
wemeasured how both objective and subjective (self-reported)
components of musical sophistication predict musical groove
ratings. Musical sophistication is the possession of heightened
music skills and engagement, and contains attributes such as
musical understanding, appreciation, evaluation, and
communication alongside skills such as playing an instrument,
improvisation, and possessing a sense of rhythm and pitch (Hallam
and Prince, 2003; Hallam, 2010; Müllensiefen et al., 2014).
Objective components were perceptual musical skills measured
using e Prole for Music Perception Skills (Law and Zentner,
2012; Zentner and Strauss, 2017) and the Beat and Meter Sensitivity
Task (Nave-Blodgett etal., 2021a,b). Subjective components were
measured using the Goldsmiths Musical Sophistication Index
(Müllensiefen et al., 2014). Wepredicted that musical training, beat
sensitivity, and measure sensitivity would bethe most reliable
predictors of musical groove perception, though other possible
predictors could include active engagement, accent perception, or
rhythm perception. It is vital to understand these subtleties in
musicality across a wide range of listeners because musical groove’s
likeability and eects on movement seem omnipresent (Madison,
2006; Madison etal., 2011; Janata etal., 2012), and thus potentially
independent of skills that are only honed via formal music training
(Leow etal., 2014).
e second aim of this study was to investigate the impact of
dance sophistication on musical groove perception. Dance
sophistication is the possession of heightened dance enjoyment,
knowledge, or skills without necessarily undergoing formal dance
training (Rose et al., 2020). Weanalyzed responses from the
Goldsmiths Dance Sophistication Index (Rose etal., 2020), a new
dance self-report assessment that distinguishes experience in
dance participation from experience in dance observation to
measure one’s overall dance comprehension. e present study
marks one of the rst investigations studying dance experience
and musical groove perception. While there is little published
work on how dance appreciation or experience may shape the way
weperceive music with groove, wehypothesized dance training to
bea strong predictor of musical groove perception in this model.
Because weinvestigated listeners with varying degrees of dance
experience, perception of musical groove in individuals with less
dance experience may bemore dependent on personal traits that
make them more open to dancing in social settings.
Materials and methods
Participants
One hundred seventy-one adults completed the study. A priori
power analyses using G*Power (Faul etal., 2009) determined that
for a multiple regression model with seven predictors, data from
153 participants was eective in achieving a power (1–β) of 0.95
to detect a medium eect size (f
2
= 0.15) at a statistical signicance
level of α = 0.05. Most participants were UNLV undergraduates
enrolled in a psychology course (n = 146). e remaining
participants were recruited by word of mouth, email, or by
announcements posted on social media platforms (e.g., Facebook,
Instagram, and Twitter). Twenty-three participants were excluded
due to poor performance on the initial headphone check (see
“Headphone check” for details); eight participants were excluded
due to incorrect answers on compliance checks (see “Compliance
check” for details); one participant was excluded due to an
excessively noisy environment while completing the study; and 15
participants were excluded due to issues loading the stimuli. e
nal 124 participants were between the ages of 18–44 years old
(M = 22.6 years, SD = 5.77 years, females = 80) and had no history
of learning, neurological, or motor disorders. Power analyses
using G*Power (Faul etal., 2009) determined this sample size was
eective in achieving a power (1–β) of 0.885 to detect a medium
eect size (f2 = 0.15) at a statistical signicance level of α = 0.05.
While musicians and dancers were not actively recruited for the
present study, participants reported varying degrees of music and
dance experience (see Table 1 for detailed music and
dance experience).
Procedure
All testing was implemented online using Qualtrics (Qualtrics,
Provo, UT, United States) and LimeSurvey (LimeSurvey,
Hamburg, Germany). Participants followed an internet link to
access the experiment (link can befound on the project’s Open
Science Foundation Repository).1 Participants were required to
sign a consent form before beginning the study. Participants were
asked to complete the experiment on a computer over headphones
in a quiet environment. Participants proceeded through the study
1 https://osf.io/g3y7c/
O’Connell et al. 10.3389/fpsyg.2022.998321
Frontiers in Psychology 05 frontiersin.org
beginning with the most dicult and attentionally taxing
measures, described below in order of administration. Participants
were oered opportunities to take short breaks aer each test and
subtest. Total test time was 60–90 min.
Headphone check
To ensure that participants were using headphones as
requested, and could hear the auditory stimuli clearly, they
completed a short assessment prior to beginning the experiment.
In each trial, participants were presented with three tones and
asked to indicate which was the quietest: the correct answer could
only bediscerned if the individual was wearing headphones rather
than listening in free-eld (Woods etal., 2017). Weexcluded data
from participants who did not correctly answer at least ve out of
the six trials.
Profile for music perception skills
First, participants completed the short version of the Prole
for Music Perception Skills (PROMS; Zentner and Strauss, 2017).
is music aptitude battery objectively measures perceptual
musical skills across multiple modalities in both musically trained
and untrained individuals (Law and Zentner, 2012).
Weadministered the Rhythm, Embedded Rhythm (rhythm-to-
melody), Tempo, and Accent subtests because of their theorized
importance to the feeling of musical groove (Witek etal., 2014;
Etani etal., 2018; Matthews etal., 2019; Fukuie etal., 2022) and
their robustness against noisy testing environments (Zentner and
Strauss, 2017). We also chose to use the Melody subtest as an
exploratory measure as previous research has yet to report that
melody inuences musical groove. Each subtest consists of eight
to ten trials with a total testing time of 25 min. In each subtest, a
trial consisted of a standard auditory stimulus (played twice)
followed by one comparison auditory stimulus. Participants
indicated (1) if the comparison stimulus was the same as the
standard stimulus and (2) how condent they were in their answer.
e Melody subtest assessed the ability to recognize either
tonal (easy) or atonal (dicult) melodies. Two-bar, eighth-note
melodies were played by a MIDI harpsicord monophonically in
4/4 time. In dierent trials, one note of the comparison melody
would dier from the reference melody by one semitone. e
Rhythm subtest assessed the ability to recognize percussive
rhythmic motifs. Two-bar phrases played equally accented in 4/4
time were composed of quarter, eighth, and sixteenth notes. Trials
varied in diculty by where the rhythmic deviant was located
between the reference and comparison stimuli (i.e., easy trials had
rhythmic deviants presented on downbeats and hard trials had
rhythmic deviants presented on up-beats). e Embedded
Rhythm subtest assesses the ability to recognize a percussive
rhythmic motif when it is presented as part of a melody. Two-bar
monophonically played and equally accented phrases in 4/4 time
were composed of eighth and quarter notes. e reference
stimulus was presented as a simple rhythm while the comparison
stimulus was presented as a tonal melody. Participants were asked
to identify whether the rhythm of the melody in the comparison
stimulus matched the rhythm of the reference stimulus. e
Tempo subtest assessed the ability to discriminate the speed at
which music is played. Reference and comparison stimuli were
polyphonically played in 4/4 time. e comparison stimuli ranged
in diculty by being 1 BPM (dicult) to 7 BPM (easy) dierent
from the reference stimulus. e Accent subtest assessed the
ability to discriminate the relative emphasis given to certain notes
in a rhythmic pattern. Across two identical rhythmic motifs
presented in 4/4 time monophonically by a MIDI drum sound,
accented notes were presented as 3 dB louder than non-accented
notes. Easy trials had more accent variations between reference
and comparison stimuli compared to moderate and dicult trials.
More detailed information on these subtests can befound in Law
and Zentner (2012).
Beat and meter sensitivity task
In the next task, participants completed the Short BMS, a brief
version of the Nave-Blodgett et al. (2021a,b) Beat and Meter
Sensitivity Task (BMS), presented via Qualtrics. e BMS uses
naturalistic music stimuli to assess auditory beat and meter
sensitivity in individuals with varying levels of musical expertise,
and does not require familiarity with musical terms, theory, or
notation. In the Short BMS, participants listened to brief excerpts
of commercially-recorded ballroom dance music overlaid with a
custom click track that either matched or mismatched the music
at the beat and measure levels (four possible alignment
TABLE1 Participants’ musical and dance experience: self-reported category of expertise.
n (%)
Musical experience
NE OM RM SAM PM To tal
Dance experience NE 37 (29.8%) 31 (25%) 7 (5.6%) 3 (2.4%) 2 (1.6%) 80 (64.5%)
OD 12 (9.6%) 7 (5.6%) 3 (2.4%) 2 (1.6%) 0 (0%) 8 (6.4%)
RD 2 (1.6%) 5 (4%) 1 (0.8%) 0 (0%) 0 (0%) 8 (6.4%)
SAD 4 (3.2%) 3 (2.4%) 2 (1.6%) 1 (0.8%) 0 (0%) 10 (8.1%)
PD 0 (0%) 1 (0.8%) 0 (0%) 1 (0.8%) 0 (0%) 2 (1.6%)
Tot a l 55 (44%) 47 (37.9%) 13 (10.4%) 7 (5.6%) 2 (1.6%) 124 (100%)
No experience (NE) = have no experience playing/participating. Occasional musician (OM)/Occasional dancer (OD) = less than weekly practice/participation. Recreational musician (RM)/
Recreational dancer (RD) = weekly practice or recreational playing/performance. Serious Amateur Musician (SAM)/Serious Amateur Dancer (SAD) = extensive commitment to practice and/or
recreational music or dance activity. Professional musician (PM)/Professional dancer (PM) = paid to perform and/or teach music or dance. Va lues are expressed as n (% of reported sample).
O’Connell et al. 10.3389/fpsyg.2022.998321
Frontiers in Psychology 06 frontiersin.org
conditions). e musical excerpts were taken from six ballroom
dance pieces, three of which were scored in 3/4 time (triple meter)
and three of which were scored in 4/4 time (duple meter). e
click track could fully match the beat and measure of the musical
excerpt (beat matching/measure matching; e.g., a click track in 4/4
paired with a musical excerpt in 4/4), match the beat but not the
measure (beat matching/measure mismatching; e.g., a click track
in 3/4 paired with a musical excerpt in 4/4 where the beat of the
click track and music aligns), match the measure of the music but
not the beat (beat mismatching/measure matching; e.g., a click
track in 3/4 paired with a musical excerpt in 4/4 where the
measure-level downbeat matches but the beat does not), or not
match either the beat or measure of the music (beat mismatching/
measure mismatching; e.g., a click-track in 3/4 with a beat-level
tempo 15% faster or slower than the musical excerpt in 4/4).
Please consult the Open Science Foundation Repository2 for
methods and stimulus information specic to the Short BMS, and
Nave-Blodgett etal. (2021a,b) for general methods.
Aer listening to each musical excerpt and click track pairing,
participants rated the t of the click track to the music using a
four-point Likert-type scale ranging from 1 (“Not Well at All”) to
4 (“Very Well”). Participants were given four practice trials to
experience the stimuli and the rating scale prior to starting the
experimental portion of the Short BMS. e Short BMS consists
of 30 pairs of musical excerpts/click tracks. e musical excerpt/
click track pairings were three musical measures long, which
translated to approximately 6–8 s per trial. e task took
approximately 10 min for participants to complete. e Short BMS
results in two scores per participant, a beat sensitivity score that
indicates participants’ ability to distinguish between beat-
matching and beat-mismatching metronomes, and a meter
sensitivity score that indicates participants’ ability to distinguish
between metronomes that fully match the beat and measure of the
2 https://osf.io/8nfvq/
music and those metronomes that match the beat of the music but
not the measure.
Musical groove judgment task
Following the Short BMS, participants completed the Musical
Groove Judgment Task (MGJT). Participants listened to 15-s clips
of 10 high-groove (HG) and 10 low-groove (LG) songs and made
judgments on what they heard. e ten songs rated highest in
groove and the ten songs rated lowest in groove were chosen for
this study from the Janata etal. (2012) music library (see Table2
for complete song list). In this task, groovy was dened as how
much a song makes youwant to dance. On a seven-point Likert
scale, they answered the following questions: (1) “Is this song
groovy? (i.e., does it make youwant to dance?),” (2) “Did youenjoy
this song?,” and (3) “Are youfamiliar with this song?.” Likert scale
choices ranged from Not groovy at all, I do not like it at all, and
is song is not familiar at all to Very groovy, I like it very much,
and is song is very familiar, respectively. Stimuli were truncated
to 15-s segments using Audacity 2.1.2 (Audacity Team, 2021) and
normalized to bethe same volume. As in Janata etal. (2012), song
stimuli were segmented starting at ~ 45 s into the song. is task
took about 5 min to complete (Table3).
Goldsmiths musical sophistication index
Upon completion of the MGJT, participants completed the
Goldsmiths Musical Sophistication Index Self-Report Inventory
(Gold-MSI), a 39-item psychometric instrument used to quantify
the amount of musical engagement, skill, and behavior of an
individual (Müllensiefen et al., 2013). e questions on this
assessment are grouped into ve subscales: Active Engagement,
Perceptual Abilities, Musical Training, Singing Abilities, and
Emotions (see Müllensiefen etal., 2013, 2014 for each subscale’s
detailed question information). e Active Engagement subscale
comprised questions that described a range of active musical
engagement behaviors (e.g., “I keep track of new music that
Icome across (e.g., new artists or recordings)” or “I do not spend
much of my disposable income on music”). e Perceptual
Abilities subscale comprised questions that each represented the
self-assessment of cognitive musical ability and music listening
skills (e.g., “I can tell when people sing or play out of tune”). e
Musical Training subscale combined questions involving the
extent of self-reported musical training and practice (e.g., “I
engaged in regular daily practice of a musical instrument
including voice for __ years”), and the degree of self-assessed
musicianship (“I would not consider myself a musician”). e
Singing Abilities subscale comprised questions that reected upon
dierent self-reported skills and activities related to singing (e.g.,
“I amnot able to sing in harmony when somebody is singing a
familiar tune”). e Emotions subscale comprised questions
describing self-reported behaviors that happen frequency in
response to an external music source. ese questions were not
assessing planned behaviors or those that could change based on
increased musical experience (e.g., “I hardly ever hum or sing
along to music”). All items, except those assessing Musical
TABLE2 Participants’ musical and dance experience: self-reported
characteristics.
Characteristic n M SD Range
Age started music lessons 37 9.7 3.7 4–16
Years of music lessons 37 6.5 4.2 1–20
Age started music ensemble 50 11.7 2.2 5–16
Years of music ensemble 50 5.8 4.7 1–30
Average hours of daily playing 37 2.7 2.5 0.5–11
Age started dance lessons 39 9.4 6.8 2–35
Years of dance lessons 39 8.6 7.6 0.5–27
Average hours of daily dancing 23 3.0 2.0 0.25–8
Hours of music listening per week 124 15.0 14.4 0–70
Years musical and dance training, age started musical and dance training, and hours
daily playing only include participants with relevant experience. Hours of music
listening per week include all participants. ose with both music and dance experience
are not included in the separate totals for musical and dance experience.
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Frontiers in Psychology 07 frontiersin.org
Training, are scored on a seven-point Likert scale with choices that
range from Completely disagree to Completely agree. e composite
score of these ve subscales makes up an individual’s General
Musical Sophistication score (Müllensiefen et al., 2013). More
details about the Gold-MSI can be found in Müllensiefen
etal. (2013).
Goldsmiths dance sophistication index
Aer the Gold-MSI, participants completed the Goldsmiths
Dance Sophistication Index (Gold-DSI), a 26-item standardized
self-report instrument used to quantify individual dierences in
doing dance (i.e., participatory dance experience), watching dance
(i.e., observational dance experience), and one’s knowledge about
dance (Rose etal., 2020). Like the Gold-MSI, the Gold-DSI is
designed to measure a wide range of dance skills, behaviors, and
engagement in a general population (Rose et al., 2020). e
Gold-DSI is comprised of two separate inventories: Participatory
Dance Experience and Observational Dance Experience. e
composite score of four subtests (Body Awareness, Social Dancing,
Urge To Dance, and Dance Training) contribute to the
Participatory Dance Experience score while the composite score
on six separate questions comprises the Observational Dance
Experience score (see Rose etal., 2020 for each subscale’s detailed
question information). e questions were randomized per
participant. e Body Awareness subscale consisted of items that
ask about the degree of self-assessed movement and coordination
(e.g., “I nd it easy to learn new movements”). e Social Dancing
subscale consisted of items describing self-reported behaviors
about one’s time spent dancing with others and the emotions felt
around dancing in public places (e.g., “If someone asks me to
dance, Iusually say yes”). e Urge To Dance subscale consisted
of items describing self-reported physical and emotional responses
to music related to dance and how much time spent dancing (e.g.,
“When I dance, I feel better”). e Dance Training subscale
consisted of questions describing the extent of one’s formal dance
experience and their self-assessed level of dance ability (e.g., “I
have taken regular dance classes at least once a week for __ years”).
e Observational Dance Experience subscale consisted of items
that ask the extent to which one self-reports watching dance
in-person or on TV/online and the emotions felt when watching
dance (e.g., “I like watching people dance”). All items, except those
assessing Dance Training, are scored on a seven-point Likert scale
with choices that range from Completely disagree to Completely
agree. More details about the Gold-DSI can befound in Rose
etal. (2020).
Demographics
e nal task participants completed was a demographics
questionnaire that asked questions about health history, music
experience, dance experience, exercise, and engagement with
music listening.
Compliance check
roughout the study, we utilized a set of previously
published questions to ensure participants were adequately
attending to the experimental task (Mehr etal., 2018). Within
each experimental block, participants were asked one time to
answer the following question: “What color is the sky? Please
answer this incorrectly, on purpose, by choosing red instead of
blue.” e possible response options were “Green,” “Blue,” “Red,”
or “Yellow”. e correct response was only presented in each
answer slot once. is question was presented a total of ve
times. Participant who did not select “Red” to all ve questions
were excluded from analysis.
Aer the completion of the experiment, participants were
asked to answer compliance questions to ensure that the
experiment was completed with eort in an environment with
minimal distraction. e rst question stated, “People are working
on this task in many dierent places. Please tell us about the place
you were at when working on this task. Please answer honestly.”
Response options were (1) “I worked on this study in a very noisy
place,” (2) “I worked on this study in a somewhat noisy place,” (3)
“I worked on this study in a somewhat quiet place,” or (4) “I
worked on this study in a very quiet place”. ose who answered
the question with “I worked on this study in a very noisy place” or
“I worked on this study in a somewhat noisy place” were excluded
from analysis. e second question asked, “Please tell us if youhad
TABLE3 Songs used in the musical groove judgment task.
Song name Artist Groove Genre Groove
rating
Superstition Stevie Wonder High Soul 108.7
It’s a Wrap FHI (Funky Hobo #1) High Soul 105.9
Flash Light Parliament High Soul 105.1
Lady Marmalade Patti LaBelle High Soul 102.5
Up for the
Downstroke
e Clinton
Administration
High Soul 102.4
Mama Cita Funk Squad High Soul 101.6
Music Leela James High Soul 101.1
If IAin’t Got You Alicia Keys High Soul 98.7
Sing, Sing, Sing Benny Goodman High Jazz 97.4
In the Mood Glenn Miller High Jazz 96.9
Space Oddity David Bowie Low Rock 38.7
Ray Dawn Balloon Trey Anastasio Low Rock 38.5
Druid Fluid Yo-Yo Ma, Mark
O’Connor, and Edgar
Meyer
Low Folk 38.1
Flandyke Shore e Albion Band Low Folk 36.5
Citi Na GCumman William Coulter and
Friends
Low Folk 35.2
Dawn Star Dean Magraw Low Folk 34.8
Fortuna Kaki King Low Folk 32.6
Beauty of the Sea e Gabe Dixon Band Low Rock 32.1
Sweet ing Alison Brown Low Folk 30.9
Hymn for Jaco Adrian Legg Low Folk 29.3
Groove = groove category (i.e., low or high). Groove rating values are derived from
Janata etal. (2012).
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diculty loading the sounds. Please answer honestly.” From “Yes”
or “No” response choices, any participant who responded with
“Yes” was excluded from analysis. e nal question asked, “How
carefully did you complete this experiment? Please answer
honestly.” From the response options of (1) “Not at all carefully,”
(2) “Slightly carefully,” (3) “Moderately carefully,” (4) “Quite
carefully,” and (5) “Very carefully,” those who answered with “Not
at all carefully,” “Slightly carefully,” or “Moderately carefully” were
excluded from analysis.
Statistical design
All data for this study can befound here: https://osf.io/g3y7c/.
e main analysis was a stepwise multiple linear regression that
predicted musical groove sensitivity score, which is the dierence
between mean high-groove music ratings and mean low-groove
music ratings (Mhigh-groove music– Mlow-groove music). Musical groove
sensitivity scores can range from –6 to + 6. ose with higher
musical groove sensitivity scores (score = 4–6) perceive greater
dierences between high- and low-groove songs. ose with lower
musical groove sensitivity scores (score = 1–3) either perceive less
dierences between high-and low-groove songs or no dierences
(score = 0) between the two groove types. A score evaluating the
dierence between high- and low-groove music was employed
rather than separately regressing ratings on high- and low-groove
songs for two reasons. First, a dierence score uses all the groove
rating data in a single outcome measure. Second, a dierence score
controls for response bias or how individual participants use the
subjective rating scale for “grooviness.” For example, two
participants might provide dierent numbers for the same
subjective amount of groove for a low-groove song, but it is
assumed that the increased rating they would each provide for a
high-groove song would reect an accurate measure of their
sensitivity for the dierence between low-groove and high-groove
songs. Overall, this allows for a more nuanced measurement of
musical groove that captures how individuals dierently rate high-
and low-groove music.
A bidirectional stepwise linear regression analysis was
performed in the R statistical soware environment (R Core
Team, 2022) to assess how subtests of the Gold-MSI, the Gold-
DSI, the PROMS, and the Short BMS may predict musical groove
sensitivity. First, weused the stepAIC() function from the MASS
package to choose predictors for a best-t model based on the
Akaike Information Criterion (AIC): a measure of t that
estimates the quality of each model. is automatic function
evaluates models in parallel to avoid overtting of data and
cherry-picking of predictors (Venables and Ripley, 2002). en,
wechose the best-t model based on the lowest AIC value and ran
a multiple linear regression analysis using the lm() function on
the resulting automatically chosen predictors.
Analysis of variance (ANOVA) evaluating musical groove,
familiarity, and likeability ratings and Pearson’s r correlation
coecients between the predictor and criterion variables for the
resulting stepwise multiple regression analysis were calculated in
SPSS 28 (IBM, Chicago, IL, United States). e ANOVA for
musical groove, likeability, and familiarity ratings was replicated
from Janata etal. (2012) and was conducted with the musical
excerpt as a case (i.e., data averaged across participants for each
excerpt) rather than the participant. is was intentional to
validate this dataset against the original ndings of Janata
etal. (2012).
Results
Relation of musical groove, likeability,
and familiarity
Replicating prior work (Janata etal., 2012), a one-way analysis
of variance (ANOVA) with a two-tailed alpha level of 0.05 was
conducted with musical excerpt as a case (i.e., data averaged across
participants for each excerpt). e results conrmed that listeners
in the Musical Groove Judgment Task gave higher groove ratings
to high-groove (M = 5.42, CI = 5.05, 5.79) than to low-groove
excerpts (M = 2.14, CI = 1.81, 2.47), F1,18 = 226.02, p < 0.001,
η2 = 0.926 (see Figure1A). is conrms that the songs identied
in this study, borrowed from Janata etal. (2012), were categorized
correctly as high-groove or low-groove by the researchers and
conrmed by listener ratings. ere were also statistically
signicant positive correlations between mean musical groove
ratings and likeability ratings, r (18) = 0.79, p < 0.001; groove and
familiarity ratings, r (18) = 0.70, p < 0.001; and familiarity and
likeability ratings, r (18) = 0.88, p < 0.001 (see Figure1B).
Stepwise multiple linear regression
analysis
We rst entered a total of 17 predictors into the stepwise linear
regression model. e predictors were the total scores of each of
the Gold-MSI subscales (i.e., Active Engagement, Perceptual
Abilities, Emotions, Singing Abilities, and Musical Training), the
total scores of each of the Gold-DSI subscales (i.e., Body
Awareness, Social Dancing, Urge To Dance, and Dance Training,
and Observational Dance Experience), the total scores of each
PROMS subtest (i.e., Melody, Tempo, Accent, Rhythm, and
Embedded Rhythm), and the total scores on each of the Short
BMS measures (i.e., beat sensitivity and measure sensitivity). Aer
running the stepAIC() function, wechose the best tting model
based on the lowest AIC value (AIC = −22.612). e selected
model for analysis contained seven predictors: three predictors
from the Gold-MSI (Perceptual Abilities, Musical Training, and
Emotions), two predictors from the Gold-DSI (Social Dancing
and Dance Experience), one predictor from the PROMS (Accent),
and one predictor from the Short BMS (beat sensitivity).
Weregressed these predictors using the lm() function to nd that
Perceptual Abilities, Musical Training, and Social Dancing scores
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Frontiers in Psychology 09 frontiersin.org
signicantly predicted musical groove sensitivity, F (7,
116) = 5.091, p < 0.001, R2 = 0.24, adj. R2 = 0.19 (see Table 4).
Emotions, Dance Training, Accent and beat sensitivity scores were
not statistically signicant predictors of groove sensitivity,
ps > 0.05. Multicollinearity was assessed: VIF were below 2.50
suggesting no presence of multicollinearity (Kutner et al., 2005).
e accompanying correlations between predictor and criterion
variables can befound in Table5.
Discussion
e present study investigated individual dierences in music
and dance characteristics that may contribute to musical groove
perception. Specically, this online experiment examined 17
potential predictors and assessed how facets of musical
sophistication, dance sophistication, and performance on
music-based perceptual tasks inuenced individuals’ sensitivity to
musical groove. Although previous studies focused on the acoustic
components of music (Witek etal., 2014; Stupacher etal., 2016a;
Senn et al., 2017, 2018) and the way music is performed that
makes the music itself “groovy” (Hurley etal., 2014; Witek and
Clarke, 2014; Kilchenmann and Senn, 2015; Senn etal., 2016),
here we chose to ask how individual dierences in listeners’
experiences, training, and perceptual skills might shape the way
they experience musical groove. Our study is novel in that weuse
a new measure, the musical groove sensitivity score, which can
beused in a regression framework to examine the relationship
between an individual’s groove perception and other individual
dierence measures.
In general, our participants agreed on ratings of musical
groove, familiarity, and likeability. Songs previously rated as high
and low in musical groove by listeners in Janata etal. (2012) were
rated similarly by the listeners in the present study. Specically,
AB
FIGURE1
Mean musical groove ratings and correlations for musical groove, likeability, and familiarity. (A) Bar graphs of mean musical groove ratings (N = 20;
high-groove = 10, low-groove = 10) based on musical excerpt as a case (i.e., data averaged across participants for each excerpt). Error bars indicate
95% confidence intervals. Results reveal statistically significant dierences between high-groove (black) and low-groove (grey) mean song ratings
F1,18 = 226.02, p < 0.001. (B) Relationships between mean musical groove ratings, mean likeability ratings, and mean familiarity ratings (N = 20; high-
groove = 10, low-groove = 10). Results show statistically significant positive correlations between musical groove and likeability ratings, musical
groove and familiarity ratings, and likeability and familiarity ratings.
TABLE4 Stepwise multiple linear regression results.
Vari a b le B97.5% CI for βSEBβt p
LL UL
Gold-MSI Perceptual Abilities 0.03 0.24 0.29 0.01 0.27 2.46 0.015*
Gold-MSI Musical Training −0.02 −0.24 −0.20 0.01 −0.22 −2.15 0.034*
Gold-MSI Emotions 0.03 0.14 0.21 0.02 0.17 1.78 0.077
Gold-DSI Social Dancing 0.02 0.18 0.23 0.01 0.21 2.17 0.032*
Gold-DSI Dance Training −0.03 −0.21 −0.15 0.02 −0.18 −1.73 0.087
BMS Beat Sensitivity 0.20 −0.15 0.42 0.15 0.13 1.38 0.171
PROMS Accent 0.09 0.03 0.26 0.06 0.14 1.51 0.133
Criterion variable, musical groove sensitivity score. AIC = −22.612, F(7, 116) = 5.091, p < 0.001, R2 = 0.24, adj. R2 = 0.19. B, unstandardized regression coecients. CI, condence interval. LL,
lower limit. UL, upper limit. β, standardized regression coecients. *p < 0.05 and **p < 0.01. Bolded values emphasize the signicant predictors found in the stepwise regression model.
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Frontiers in Psychology 10 frontiersin.org
our listeners rated high-groove music as being signicantly more
“groovy” than low-groove music. As in Janata etal. (2012), our
participants also rated high-groove songs as more familiar and
more likeable than low-groove songs. Musical groove ratings,
familiarity ratings, and likeability ratings all had strong, positive
relationships with one another.
Using an AIC-based stepwise model selection, seven out of 17
possible predictors from subtest scores of the Gold-MSI, Gold-
DSI, PROMS, and Short BMS were chosen to predict the musical
groove sensitivity score. e seven selected predictors (Gold-MSI
Perceptual Abilities, Gold-MSI Musical Training, Gold-MSI
Emotions, Gold-DSI Dance Training, Gold-DSI Social Dancing,
PROMS Accent, and Short BMS beat sensitivity) together
accounted for 24% of the variance in musical groove sensitivity
score. Of these predictors, self-reported Perceptual Abilities,
Musical Training, and Social Dancing scores separately predicted
musical groove dierence ratings compared to the other predictors
in the model. Emotions, Dance Training, Accent, and beat
sensitivity scores did not signicantly predict the dierence
between high-and low-groove music ratings.
Perceptual abilities and musical training
e Perceptual Abilities subtest of the Gold-MSI is comprised
of self-reported views of song recognition, tonal perception, genre
identication, and how well one can judge others’ musical abilities
(Müllensiefen et al., 2014). Pearson r correlations reected a
positive relationship between Perceptual Abilities and musical
groove sensitivity scores indicating that those who think they are
good at judging others’ musical abilities, identifying musical
genres, recognizing familiar music, and spotting mistakes in
performances tend to rate high-and low-groove music more
distinctly. Perceptual Abilities also had signicant positive
correlations with Gold-MSI Musical Training, Gold-MSI
Emotions, Gold-DSI Dance Training, Gold-DSI Social Dancing,
PROMS Accent, and Short BMS beat sensitivity.
e Musical Training subtest of the Gold-MSI is comprised of
self-reported views of musicianship as well as quantitative
measurements of practice time, formal training, and instrument
type (Müllensiefen etal., 2014). Pearson r correlations reected a
barely positive relationship between Musical Training and musical
groove sensitivity scores indicating those that consider themselves
to be musicians, those that are complimented more oen on
performance quality, and those who report more hours of daily
practice, greater years of formal music training, and increased
numbers of instruments played rated high-and low-groove music
more distinctly. Musical Training also had signicant positive
correlations with Gold-MSI Perceptual Abilities, Gold-MSI
Emotions, Gold-DSI Dance Training, PROMS Accent, and Short
BMS beat sensitivity, but was not signicantly correlated with
Gold-DSI Social Dancing.
In the stepwise regression model, the Perceptual Abilities
score was a signicant positive predictor of musical groove
sensitivity score. Interestingly, Musical Training score was a
signicant negative predictor of musical groove sensitivity score.
is regression result was surprising considering that when
correlated on its own, Music Training score has a barely positive
association with musical groove sensitivity score. It is only when
other predictors are considered in the regression model, however,
that Musical Training has a negative regression coecient.
ese unpredicted results may beconnected to the positive
association found between Gold-MSI Perceptual Abilities and
Musical Training subtest scores. Individuals who possess more
honed perceptual abilities may have more musical training. Five
out of seven questions on the Gold-MSI Musical Training subtest
ask about quantitative hours of practice and years of training.
erefore, it seems that music training quantity is highly weighted
in the nal subscale score and is designed to identify individuals
with formal music training. Questions that comprise the
Gold-MSI Perceptual Abilities subtest, such as judging musical
abilities and spotting mistakes during performances, are also some
of the many skills that are taught and honed when formally
learning to sing or play an instrument at a high level.
Additionally, those with formal music training may rate songs
with groove dierently from those without formal training.
Previous research has shown that musicians have rated more
complex music, like jazz and funk, to be“groovier” (Pressing,
2002; Matthews etal., 2019) while non-musicians have rated less
complex music, such as pop and rock, higher in groove (Senn
etal., 2021a). is may bedue to musicians understanding and
appreciating more complex music and how familiar musicians are
TABLE5 Correlations between variables in stepwise multiple linear regression analysis.
Vari a b le 1 2 3 4 5 6 7 8
1. Musical Groove Sensitivity Score –
2. Gold-MSI Perceptual Abilities 0.31** --
3. Gold-MSI Musical Training 0.02 0.55*** –
4. Gold-MSI Emotions 0.31** 0.53*** 0.33*** –
5. Gold-DSI Social Dancing 0.21*0.19*0.05 0.15 –
6. Gold-DSI Dance Training 0.05 0.37*** 0.35*** 0.27** 0.52*** –
7. BMS Beat Sensitivity 0.29** 0.36*** 0.28** 0.37*** 0.16 0.22*–
8. PROMS Accent 0.20*0.28** 0.37*** 0.19* −0.003 0.17 0.43*** –
*p < 0.05; **p < 0.01; ***p < 0.001.
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with their genre of expertise. For instance, a jazz musician may
bebetter at dierentiating between high- and low-groove music if
they were making ratings across only jazz music as opposed to
rating only pop music. What is missing from the Gold-MSI,
however, is an assessment about the type of music these musicians
play. While this specic examination did not collect sucient
demographic data about the type of genre musicians claimed
expertise in, future research should consider comparing musical
groove sensitivity scores across a variety of musicians with
dierent types of expertise to see if familiarity with a certain genre
can drive musical groove ratings.
Conversely, possessing greater perceptual abilities may belinked
to more experience with music (e.g., avid listening or attending
concerts), or associated with self-taught, informal training (e.g.,
funk players with 20+ years of band experience), but may not
beindicative of formal music training (e.g., conservatory-trained
classical musicians). Müllensiefen etal. (2014) found that both the
Gold-MSI Perceptual Abilities and Music Training subscale scores
had strong positive associations with perceptual musical skills tasks
such as the Gold-MSI Beat Perception and Melody Memory tests.
Our data also supports this notion with positive, signicant
relationships between the Gold-MSI Perceptual Abilities, Gold-MSI
Musical Training, Short BMS beat sensitivity, and PROMS Accent—
two perceptual tasks that measure performance on music-related
skills designed to potentially identify musical ability not necessarily
honed through training.
Considering the positive and negative associations between
Gold-MSI Perceptual Abilities and Musical Training in both the
correlation and regression analyses, respectively, the regression
result seems to imply that among those who score higher on
Perceptual Abilities, avid music appreciators and those with
informal music training may make greater distinctions between
high- and low-groove music compared those with formal music
training. For instance, the questions that make up the Gold-MSI
Perceptual Abilities subtest ask about genre identication and
recognition of familiar and novel songs. While these abilities can
belearned through formal music training, they can also berened
through frequent music listening or informal music training. e
high-and low-groove songs that were selected for this study
belonged to diering genres: high-groove songs were previously
categorized as belonging to soul and jazz genres while low-groove
songs were previously identied as belonging to rock and folk
genres (Janata etal., 2012). Avid music appreciators who have
experience listening to a wide variety of music, or informally
trained musicians with ample experience playing in funk and soul
bands, may beable to easily identify high-and low-groove music
genres as “dance” or “non-dance” songs, respectively, based on
genre, but not necessarily on how much they make them want to
dance. Frequent music listeners are also potentially better than
music novices at recognizing familiar and unfamiliar music.
Because songs higher in groove in this study and others (Janata
etal., 2012; Senn etal., 2021a) were also rated as more familiar,
these individuals may rate high- and low-groove music more
distinctly based on familiarity rather than how much it makes
them want to dance. Future research should consider matching
high- and low-groove songs on genre and familiarity to further
disentangle groove from familiarity and its associations with
pleasurable movement.
It is also possible that the discrepancy found between the
Gold-MSI Musical Training score and the musical groove sensitivity
score in the correlation and regression analyses may beaected by
suppressor variables in the stepwise regression. Suppressed variables
are sometimes identied by being highly positively correlated with
other signicant predictors within the regression model but are not
signicantly positively correlated with the criterion variable. ese
types of predictors may therefore have dierent relationships with a
criterion variable when doing simple correlation versus multiple
regression (Pandey and Elliott, 2010). e Gold-MSI Musical
Training predictor seemed to t this mold: it is signicantly positively
correlated with Gold-MSI Perceptual Abilities but not with musical
groove sensitivity and has a signicant negative regression coecient
in the regression model. While this study was more exploratory in
identifying potential variables that may predict musical groove
sensitivity score, it is possible that removing the Musical Training
score from the model may increase the magnitude in the relationship
between other signicant predictors and the criterion variable
(Mackinnon etal., 2000).
Social dancing
Social Dancing is a Gold-DSI subtest comprised of self-
reported views of social dance enjoyment and engagement
(Müllensiefen etal., 2014). Pearson r correlations reect a positive
relationship between Social Dancing and musical groove
sensitivity scores, indicating those who have more engagement in
social dancing, greater experience dancing with others, and
heightened enjoyment participating in social dance rated high-
and low-groove music more distinctly. Social Dancing also had
signicant positive correlations with Gold-MSI Perceptual
Abilities and Gold-DSI Dance Training but was not signicantly
correlated with Gold-MSI Musical Training, Gold-MSI Emotions,
PROMS Accent, and Short BMS beat sensitivity.
e stepwise regression model revealed the Gold-DSI Social
Dancing score as a signicant positive predictor of musical groove
sensitivity score. Unexpectedly, Dance Training score was not a
signicant predictor of musical groove sensitivity score and when
correlated on its own, Dance Training did not have a signicant
association with musical groove sensitivity score.
e Gold-DSI Social Dancing score may be a signicant
predictor of musical groove sensitivity scores because it assesses
dance in the context of socialization and enjoyment: all previously
reported descriptors of how people feel and act when hearing songs
with groove (Janata etal., 2012). Fitch (2016) argues that “…core
aspects of musical rhythm, especially ‘groove’ and syncopation, can
only be fully understood in the context of their origins in the
participatory social experience of dance” (p.1). Considering the
positive association between Gold-DSI Social Dancing and Dance
O’Connell et al. 10.3389/fpsyg.2022.998321
Frontiers in Psychology 12 frontiersin.org
Training scores, those who scored higher on Social Dancing may
beindividuals with extensive dance training. Oentimes, classical
dance forms such as ballet, modern, or lyrical are choreographed
and performed to low-groove songs while social, contemporary, and
percussive dance forms like jazz, tap, and hip-hop are performed to
high-groove songs. erefore, these individuals would be well-
trained in evaluating what is considered “groovy” music based on
the dance form with which the music is associated. It is also possible
that those with more formal dance training also are more likely to go
social dancing compared to those with less formal training. e
Gold-DSI Dance Training subtest does gather quantitative
information about formal dance training (e.g., years of involvement
in formal dance classes); however, the Social Dancing subtest does
not assess quantitative social dance experience (e.g., how many
hours per week spent social dancing at a party or club), but rather
the qualitative experience of dancing (e.g., “Dancing with other
people is a great night out as far as I’m concerned”). While the
Gold-DSI does not gather this information, future studies should
investigate whether those with more formal dance training also
spend more time social dancing.
Taking together the correlation and regression analyses between
Dance Training, Social Dancing, and musical groove sensitivity,
however, this regression analysis seems to indicate that among
frequent social dancers, those with less formal dance training (e.g.,
those who attend clubs and parties to dance with friends) tend to
hear greater dierences between high- and low-groove music than
those with more formal dance training (e.g., professional classical
ballerinas). is seems to contradict our original prediction that
Dance Training would bea signicant predictor of musical groove
sensitivity score. ose who scored higher on Social Dancing may
not be formal dancers, but experienced non-trained dancers or
dance appreciators who enjoy dancing with others as a form of
bonding and socialization. Because songs with groove are oen
danced to in social settings, those who feel more comfortable
dancing socially may have more familiarity with musical groove and
as a result, are better at identifying dierences between high- and
low-groove music. ose who enjoy social dancing may also
bepeople who have greater openness to experience or are more
extraverted. Previous research has found that those who report more
openness to experience also have more episodes of pleasurable
esthetic chills to music (Colver and El-Alayli, 2015), which may
suggest greater emotional connection to music. ose who self-
report as being more extraverted also have greater local and global
body movements, faster head speeds, and greater hand ux and
hand distance when moving to music belonging to high-groove
genres such as rock, jazz, Latin, techno, funk, and pop (Luck etal.,
2010). is may indicate that those who enjoy dancing to music
from high-groove genres may also engage in more movement while
dancing, and as a result, have a more embodied representation of the
music itself. rough movement, these individuals may develop a
better sense of the beat and facilitate more enjoyment of groove
through head movements that stimulate the vestibular system and
reward networks (Phillips-Silver and Trainor, 2007, 2008; Reybrouck
etal., 2019).
Limitations and future directions
A limitation to the current study was the online format, which
was chosen due to social-distancing restrictions during the
COVID-19 pandemic which made in-person testing not feasible.
For this reason weused subjective groove ratings, which may
depend on individual participants’ interpretation of the word
“groovy.” Although wedened groove for participants as “does it
make youwant to dance?,” it is nevertheless possible that to some
extent their ratings reect their associations between certain
musical genres and the word “groovy.” Similarly, our measures of
sensitivity to musical beat were based on subjective ratings of t
between a metronome and music. Collecting accurate temporal
information or nger tapping data in online tasks is unreliable due
to potential timing lags and lack of necessary equipment in
everyday households. A future extension of this work could
incorporate production tasks, such as a beat synchronization test
in which participants tap along to music. It is possible that the
ability to produce a beat accurately in time to music may bea
more reliable predictor of hearing dierences between high-and
low-groove music than purely perceptual beat sensitivity.
is study explored sensitivity ratings of 10 high- and 10
low-groove songs. We selected a subset of songs that were
exemplars of high and low-groove music based on previous work
(Janata etal., 2012; Stupacher etal., 2013) while also considering
the time needed to obtain good data in an online study without
participant fatigue. is design did not allow us for time to include
songs that have been previously rated as “mid-groove.” Future
research should consider using a wider range of songs that capture
high-, mid-, and low-groove music to obtain a more inclusive
landscape of dierent musical genres and preferences to see how
personal experiences and predilections can inuence perceptions
of songs with moderate groove.
Conclusion
e present study investigated the inuence of musical
sophistication, dance sophistication, and musical perceptual
abilities on musical groove perception. Wefound that perceptual
abilities, musical training, and social dancing are signicant
predictors of rating dierences between high-and low-groove
music. Overall, our results indicate that the experience of groove
may not besolely dependent on the way the music is written or
performed but also shaped by listeners’ individual experiences and
predispositions. Results from this investigation may help develop
more objective assessments of dance skills that can measure dance
ability in a wide array of individuals. Clinical implications of this
research may help with the development of musical therapeutic
tools for those diagnosed with movement impairments (e.g.,
Parkinson’s disease; Nombela etal., 2013; Krotinger and Loui,
2021) or developmental disorders (e.g., ADHD; Puyjarinet etal.,
2017), who have a harder time moving to the beat compared to
healthy and typically developing individuals, respectively.
O’Connell et al. 10.3389/fpsyg.2022.998321
Frontiers in Psychology 13 frontiersin.org
Data availability statement
e datasets presented in this study can befound in online
repositories. e names of the repository/repositories and
accession number(s) can befound at: https://osf.io/g3y7c/.
Ethics statement
e studies involving human participants were reviewed and
approved by the University of Nevada, Las Vegas Institutional
Review Board. e participants provided their written informed
consent to participate in this study.
Author contributions
SO’C, EH, and JS: conceptualization. SO’C, JN-B, and JS:
methodology. SO’C: formal analysis and data collection. SO’C and
GW: investigation setup. SO’C and JN-B: writing—original dra
preparation. SO’C, JN-B, EH, and JS: writing—review and editing. All
authors contributed to the article and approved the submitted version.
Funding
is research was supported in part by the University of
Nevada, Las Vegas Foundation Board of Trustees Fellowship
awarded to SO’C. e publication fees for this article were
supported by the UNLV MSI Open Article Fund.
Acknowledgments
The authors wish to thank Jennifer Rennels and
Diego Vega for their expertise and feedback on writing
and methods. They also wish to thank Kris Gunawan,
William Blake Ridgway, and Kindy Insouvanh for their
expertise and guidance in the data analytic plan. Additionally,
the authors wish to thank Hannah Strauss for her assistance
in setting up the online version of the Profile for
Music Perception Skills. Some of the content of this manuscript
has previously appeared online in SO’C dissertation
(O’Connell, 2021).
Conflict of interest
e authors declare that the research was conducted in the
absence of any commercial or nancial relationships that could
beconstrued as a potential conict of interest.
Publisher’s note
All claims expressed in this article are solely those
of the authors and do not necessarily represent those
of their affiliated organizations, or those of the publisher,
the editors and the reviewers. Any product that may be
evaluated in this article, or claim that may be made by
its manufacturer, is not guaranteed or endorsed by
the publisher.
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