Content uploaded by Sara F. Abalde
Author content
All content in this area was uploaded by Sara F. Abalde on Nov 06, 2024
Content may be subject to copyright.
A framework for joint music making: Behavioral ndings, neural processes,
and computational models
Sara F. Abalde
a,b,*
, Alison Rigby
c
, Peter E. Keller
d,e,f
, Giacomo Novembre
a
a
Neuroscience of Perception and Action Lab, Italian Institute of Technology, Rome, Italy
b
The Open University Afliated Research Centre at the Istituto Italiano di Tecnologia, Italy
c
Neurosciences Graduate Program, University of California, San Diego, USA
d
Center for Music in the Brain, Aarhus University, Denmark
e
Department of Clinical Medicine, Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Denmark
f
The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Australia
ARTICLE INFO
Keywords:
Music
Joint action
Interpersonal coordination
Familiarity
Strategies
Social cognition
ABSTRACT
Across different epochs and societies, humans occasionally gather to jointly make music. This universal form of
collective behavior is as fascinating as it is fragmentedly understood. As the interest in joint music making (JMM)
rapidly grows, we review the state-of-the-art of this emerging science, blending behavioral, neural, and
computational contributions. We present a conceptual framework synthesizing research on JMM within four
components. The framework is centered upon interpersonal coordination, a crucial requirement for JMM. The
other components imply the inuence of individuals’ (past) experience, (current) social factors, and (future)
goals on real-time coordination. Our aim is to promote the development of JMM research by organizing existing
work, inspiring new questions, and fostering accessibility for researchers belonging to other research
communities.
1. Introduction
1.1. A framework for JMM
Throughout the world, from ancient times to the present day, people
gather to jointly make music (Mehr et al., 2019; Savage et al., 2015).
This universal form of collective behavior entails the production of
combined sounds, having rhythmic or acoustic relationships, all
blending into a patterned output that we call ensemble music. As a
human behavior, joint music making (JMM) is as fascinating as it is
mysterious. Covering diverse and complex social functions, and
accompanying rituals, courtships, or even battles, JMM has often been
labeled a microcosm of social interaction (Keller et al., 2014; D’Ausilio
et al., 2015; Izen et al., 2023) and a driving force for the evolution of
music (Savage et al., 2021; Keller et al., 2017; Mehr et al., 2021; Rav-
ignani et al., 2014). Yet, our current understanding of its underlying
cognitive, neural, and computational mechanisms remains fragmented.
The scientic interest in JMM – dened as the action of two or more
people coordinating to make music together – has grown substantially
over the past decade (Figs. 1C, 1D). Different research domains have
approached JMM with divergent yet complementary perspectives. Here
we review research from three existing elds orbiting around this topic,
namely behavioral, neural, and computational sciences, which are
grounded in social, life, and formal sciences, respectively (as illustrated
in Fig. 1A). We propose a framework for JMM intending to arrange the
existing studies in this eld in a structured manner, facilitating access to
the relevant literature and highlighting consistencies between them.
Additionally, we aim to drive future research and inspire researchers
from different backgrounds to engage with this developing eld of
research.
Our proposed framework is composed of four main components – or
topics – that have been extensively explored so far. As JMM crucially
requires interpersonal coordination, this is the central pillar, as shown in
Fig. 1B. Interpersonal coordination calls for mechanisms that permit
individuals to synchronize musical actions with one another. Next, we
describe three modulators that have been shown to impact interpersonal
coordination. These are: (i) knowledge, such as sensory or motor fa-
miliarity with musical material, (ii) the goals and strategies that are used
by musicians to achieve interpersonal coordination, and (iii) social
factors, such as personality differences, as illustrated in Fig. 1B. Notably,
* Corresponding author at: Neuroscience of Perception and Action Lab, Italian Institute of Technology, Rome, Italy.
E-mail address: sara.abalde@iit.it (S.F. Abalde).
Contents lists available at ScienceDirect
Neuroscience and Biobehavioral Reviews
journal homepage: www.elsevier.com/locate/neubiorev
https://doi.org/10.1016/j.neubiorev.2024.105816
Received 20 October 2023; Received in revised form 15 July 2024; Accepted 16 July 2024
Neuroscience and Biobehavioral Reviews 167 (2024) 105816
Available online 19 July 2024
0149-7634/© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-
nc-nd/4.0/ ).
the components of such a framework are consistent with previous
theoretical work by Keller (2008), (2014), except that, for the sake of
simplicity, we do not address hierarchical relationships between distinct
components. Instead, we propose to simply organize them along a
temporal dimension, stressing that individuals’ experiences, social fac-
tors, and goals rely mostly on the past, the present and the future,
respectively (see Fig. 1B). This suggested organization is intended to
highlight the distinction between different components and to provide a
simple way to organize this extensive literature. Our manuscript is
structured according to the proposed framework, with one section
dedicated to each component of JMM (Fig. 1B), and subsections
addressing behavioral, neural, and computational perspectives on the
topic (Fig. 1A).
1.2. Methodological approach and scope
Here we clarify methodological considerations about how we
structured our framework, why we adopted this approach, and what
goals we do (and do not) aim to achieve.
First, we center our object of study on interpersonal coordination
(central pillar), and accordingly dene JMM as the action of two or more
people coordinating to make music together (see above). It follows that
the most pertinent methodological approaches to review here comprise
behavioral, neural, and computational perspectives (Fig. 1C), which we
frame adopting traditional psychological and cognitive neuroscience
perspectives, seeking to explain JMM mostly at the level of individual
brains (the lowest common denominator). Behavioral approaches are
indispensable to capture action and coordination. Neural approaches are
crucial to address mechanisms that might not be captured by the study
of behavior alone. Computational approaches formalize processes
revealed by the former two. We feel that integrating this range of ap-
proaches – notably combining disciplines spanning social, life, and
formal sciences – is sufciently ambitious (Fig. 1A), but we welcome
recommendations to integrate additional elds or approaches in the
future (Delius and Müller, 2023; Lange et al., 2022; Vickhoff et al., 2013;
Dell’Anna et al., 2021; van der Schyff et al., 2018; Gesbert et al., 2022).
Second, precisely because we are integrating heterogeneous disci-
plines, often relying on distinct labels or measures to address qualita-
tively similar concepts, we reasoned that a systematic review of JMM
would be premature. The eld of JMM seems to us to be too broad, and
simultaneously insufciently consolidated, to carry out a systematic
review according to standard guidelines like e.g. PRISMA (Page et al.,
2021). Hence, our contribution should be considered a narrative or a
scoping review (Munn et al., 2018), focused on highlighting the emer-
gence of concepts and denitions in the literature, according to the
proposed framework (Fig. 1B). Despite the lack of a systematic literature
review method with detailed inclusion and exclusion criteria, we have
opted to be all-inclusive and to accommodate as much relevant research
pertaining to the components of the framework as possible. Hopefully,
this will serve as a precursor to a systematic review, perhaps emerging
once the eld is more mature.
Third, we wish to emphasize that we are putting forward a concep-
tual framework, not a theory. A framework serves as a conceptual or
theoretical structure that organizes research efforts within a particular
eld. A theory might also serve these functions while, crucially, making
predictions or generating testable hypotheses. Certainly, we hope that
systematizing the existing research on JMM will help researchers to
develop novel hypotheses. However, our contribution per se does not
Fig. 1. Research on joint music making (JMM) (A) Diagram illustrating the three main methodological approaches to JMM (behavioral, neural, and computational),
and their respective scientic branches. (B) Diagram illustrating the proposed framework for JMM, comprising four components: interpersonal coordination (central
pillar), knowledge, goals and strategies, and social factors. These components are organized according to a temporal dimension: coordination and social factors most
strongly relate to the present, knowledge relates to the past, and goals and strategies address the future. (C) Number of relevant articles (cited in the present article)
displayed as a function of time (publication year) and grouped by methodological approach (behavioral, neural, and computational). (D) Number of relevant articles
(cited in the present article) displayed as a function of time (year of publication) and grouped by the four different components of the JMM framework.
S.F. Abalde et al.
Neuroscience and Biobehavioral Reviews 167 (2024) 105816
2
aim to achieve that end.
2. Interpersonal coordination
2.1. Behavioral ndings
When people make music collectively, they coordinate their actions
interpersonally and along multiple musical dimensions, such as rhythm
and pitch. Such coordination builds upon several mechanisms, some of
which are as basic as receiving auditory feedback, while others are more
complex and might require cognitive processes, such as the capacity to
distinguish between different musical parts (i.e., those played by oneself
vs. others). Behavioral research on JMM coordination has investigated
these mechanisms in different musical contexts, namely scripted perfor-
mances (such as string quartets or orchestras), or improvisations. In this
section, we rst review studies addressing JMM coordination in
different musical dimensions and musical contexts. We then zoom into
these studies to shed light upon the cognitive mechanisms involved.
2.1.1. Coordination in the temporal dimension
Joint music making crucially requires establishment and mainte-
nance of temporal coordination, a form of behavior that has been
extensively studied in non-musical contexts (see reviews Coey et al.,
2011; Repp, 2005; Repp and Su, 2013), and to a more limited extent in
musical ensembles. Although interpersonal coordination takes place at
the levels of body movements and sounds during JMM (Davidson and
Broughton, 2016; Keller, 2008), auditory information is conventionally
considered to be primary in musical communication. The degree of co-
ordination of sound onsets achieved by ensemble co-performers is in the
range of tens of milliseconds (i.e., the standard deviation of asyn-
chronies is typically 10–50 ms), with precise values depending on
combination of performer skill, musical style, and structural aspects of
the music (e.g. Clayton et al., 2020; Keller, 2014; Keller and Appel,
2010; Rasch, 1988; Sabharwal et al., 2022; Bishop and Goebl, 2015,
2018a; Shaffer, 1984).
Temporal coordination during JMM depends on both endogenous
properties of individual musicians (Loehr et al., 2011), as well as
exogenous sources of sensory information, such as auditory feedback.
Loehr and Palmer (2011) demonstrated that an important factor pre-
dicting the quality of interpersonal synchronization between performing
musicians is the similarity of their endogenous rhythms when playing
solo (see also Roman et al., 2023). These endogenous rhythms are
spontaneous rates that are intrinsically different for each individual, i.e.,
idiosyncratic. Accordingly, pairs of performers with similar endogenous
rhythms can achieve higher interpersonal synchronization in compari-
son with dissimilar duet colleagues (Zamm et al., 2016), a nding that
has been also generalized to musically untrained individuals (Tranchant
et al., 2022; see also B´
egel et al., 2022).
Besides the inuence of endogenous rhythms on JMM, auditory
feedback of the musical outcome is another important factor for tem-
poral coordination amongst musicians. Interpersonal synchronization
does not only rely on hearing the music played by a partner, but also on
hearing self-produced music (Goebl and Palmer, 2009; Demos et al.,
2017). This is not trivial because information about self-produced music
might also be available through other modalities, such as motor, visual,
or proprioceptive inputs. Yet, listening plays a crucial role during JMM,
perhaps due to its high temporal precision. Zamm et al. (2015) studied
duets of pianists either playing at the same time with their partner – in
unison – or playing rounds – each person plays the same melody but
starts at different times. Pianists coordinated better when receiving
auditory feedback of the joint outcome in comparison to when they
could only hear their partner’s musical outcome, a nding aligned with
Goebl and Palmer (2009). Furthermore, coordination was generally
more accurate when the pianists performed in unison, and self-related
auditory feedback was particularly important when playing rounds
(Zamm et al., 2015). Together, these ndings indicate that self-related
auditory feedback is a requirement for JMM, especially during chal-
lenging conditions such as when the musical parts performed by each
person are different. This also implies that interpersonal synchroniza-
tion does not rely solely on external stimuli, but also on higher level
processes aimed at integrating self- and other- related musical outcomes.
2.1.2. Coordination beyond the temporal dimension: dynamics, intonation,
and timbre
JMM not only requires coordination in the temporal dimension, but
also in other musical dimensions (in addition to temporal ones) such as
dynamics, intonation and timbre, which refer roughly to variations in
the volume, pitch, and spectral content of the music, respectively. Here
we review studies demonstrating that ensemble musicians indeed
transfer and share information through these non-temporal musical di-
mensions, both while playing a score as well as during free or structured
improvisation. Papiotis et al. (2014) used computational methods to
study whether actual JMM – as opposed to solo performances – could be
detected by analyzing the interdependence between time series
featuring string quartet audio and motion capture (bowing) data. Their
procedure entailed recording each member of a string quartet in solo
and ensemble conditions while performing musical exercises aimed at
highlighting the following dimensions: intonation, dynamics, timbre,
and tempo. For the solo condition, each quartet member was recorded
playing each exercise alone. The resultant solo recordings were
time-warped and aligned with the ensemble recordings, so that they
could emulate an ensemble performance and enable the interdepen-
dence analysis. Thus, interdependence between features for each
dimension could be calculated between recordings taken during both
solo and ensemble conditions, controlling for timing information. This
analysis demonstrated the feasibility of discriminating between solo and
ensemble conditions using such non-temporal musical features,
providing evidence that performers share information beyond the tem-
poral domain.
Choir singing provides a promising domain for investigating co-
performer alignment in terms of relative intensity, intonation, and
timbre. Vocal blending (a process that serves to create a unied sound by
matching fellow singers) during choral performance is inuenced by the
combination of information along these dimensions across multiple
voices. Empirical research on choirs has revealed how singers strategi-
cally focus on intonation and control their use of vibrato to improve
blend during rehearsals (D’Amario et al., 2020; Daffern, 2017). Timbral
variations that affect blend have also been observed, including increases
in high spectral energy (the so-called “singer’s formant”; see Sundberg,
2018) that imbues the voice with an attractive ringing quality that is
more typical in solo singing, and may thus reduce choral blend in certain
contexts (Keller et al., 2017; Ternstr¨
om, 2003; Rossing et al., 1987).
Together, these studies show that interpersonal coordination in musical
contexts extends well beyond temporal coordination. Even when play-
ing scripted music, musicians interact with one another, dynamically,
along multiple non-temporal dimensions.
Experiments with improvising musicians have produced consistent
ndings to the previous studies with scripted music performances. Of
note, improvising musicians have a higher degree of freedom in terms of
what they can play, leaving more room for interpersonal exchange of
information, but perhaps coming at the cost of more challenging ana-
lyses. A series of studies overcame this challenge and characterized such
free information ow between improvising musicians (Walton et al.,
2014, 2015, 2018). In line with Papiotis et al. (2014), the authors
observed that the dynamics of two improvising jazz musicians can be
described as complex dynamical systems using non-linear time series
analysis, i.e., cross-wavelet spectral analysis, which permits the study of
musicians’ patterns of coordination across different time-frequency
scales (Walton et al., 2014, 2015). Transitioning from this analytic
approach to cross-recurrence quantication analysis, an empirical study
on professional piano duos investigated the recurrence patterns of their
musical outcome and body movements while they were improvising
S.F. Abalde et al.
Neuroscience and Biobehavioral Reviews 167 (2024) 105816
3
over either a continuous drone or a swing backing track (Walton et al.,
2018). Depending on the musical context, musicians’ interpersonal co-
ordination differed and dynamically oscillated over time in terms of
their musical outcome (both rhythm and harmony) and body movement
(head and arm movements) based on this recurrence analysis. When
musicians were improvising with the drone, their harmony and rhythm
(based on the notes played and keys pressed, respectively) were more
coordinated than when improvising with the swing backing track, as
shown in Fig. 2A. Notably, the degree of pre-existing structure in the
backing track (e.g., drone (less structure) vs. swing backing track (more
structure)) affects coordination during their improvisation: less struc-
ture appears to induce musicians to co-create structure, resulting in
more synchrony and coordination.
In contrast to improvisation based on a predetermined musical
structure, as provided by chord progressions, in collective free impro-
visation (Bailey, 1993; Canonne, 2013), musicians share the goal of
creating a compelling musical improvisation on the spot without a priori
constraints on roles, tempo, and melodic or harmonic structures. In a
study with duos of professional jazz pianists freely improvising, the
coordination of each pair was analyzed using alignment between note
onsets as temporal information in combination with a tonal consonance
measure formulated from music-theory-based harmony analysis (Setzler
and Goldstone, 2020). It was reported that pianists improvising in a
mutually adaptative condition showed strengthened synchronization
both rhythmically and harmonically in comparison to a unidirectional
condition in which each pianist improvised along a previously recorded
free improvisation. This result indicates that coupling directionality
impacts not only temporal coordination between musicians, but also the
coordination of non-temporal musical dimensions (but see also Pachet
et al., 2017).
To further understand how musicians coordinate during free
improvisation, Goupil et al. (2021) studied the spontaneous emergence
of directional intentions between musicians in improvising trios. The
researchers refer to directional intentions as intended changes of the
musical direction, i.e., changing or maintaining the direction of the
performance or ending the improvisation, as reported by the musicians
themselves. This study showed that when musicians share directional
intentions, their performance is more coordinated. Such coordination
was assessed using both temporal and non-temporal musical di-
mensions, as well as listeners’ qualitative evaluations of the recordings
(Goupil et al., 2021; see also Goupil et al., 2020 for a similar study with a
larger group). This result indicates that coordination during JMM does
not only depend on endogenous properties of individual musicians or
exogenous sources of sensory information (as previously reviewed).
Rather, it also relies on high-level cognitive processes, such as sharing
similar goals or intentions (Sebanz et al., 2006), which will be further
discussed below.
2.1.3. Integration and segregation
When people make music together, they coordinate interpersonally
along different musical dimensions, which involves paying attention to
the others’ behavior and musical outcome. Therefore, musicians inte-
grate information from self and other to achieve coordination, i.e., in-
formation about others is used to modify the ongoing behavior of a given
musician. For example, when jazz musicians are taking turns to impro-
vise solo, they need to constantly integrate the others’ melodies,
rhythms, and dynamics into their own playing. However, this integra-
tion of information might vary across all musicians and depends on the
intended outcome. For instance, playing within a large orchestra implies
weighting how much attention to pay to some players (e.g., a rst violin
or a conductor) as opposed to others, as presented in the previous sec-
tions. Thus, JMM in this context requires not only the integration of the
joint musical outcome but also the segregation of one’s own musical
behaviors from the different performers’ parts and behaviors to achieve
coordination. Taken together, JMM requires a balance between inte-
gration and segregation.
A similar, albeit not conceptually equivalent balance has been
addressed by the coordination dynamics perspective as metastability, a
conceptualization of behavioral and brain function occurring at
different spatiotemporal scales (see more details in Kelso et al., 2013;
Kelso and Tognoli, 2007; Tognoli and Kelso, 2014). During JMM and
musical performances, integration and segregation have been formal-
ized as prioritised integrative attending (Keller, 1999, 2001; Keller and
Burnham, 2005) according to which high priority attention is given to
monitor one’s own actions while lower priority attention is paid to
monitor others’ actions and the joint outcome. This cognitive process
Fig. 2. Illustrative examples of research addressing interpersonal coordination in JMM. (A) Plots representing recurrences of harmonic or rhythmic patterns in two
improvising musicians [adapted from Walton et al. (2018)]. (B) Top: Event-related potentials (ERP) elicited by unexpected musical tones. The waveforms represent
the difference between expected vs. unexpected musical tones affecting the joint outcome, either associated with the Self (green) or the Other (red) [adapted from
Loehr et al. (2013)]. Bottom: Interpersonal neural synchronization in a dyad of guitarists, estimated using interbrain phase coherence (IPC) of neural oscillations in
the theta frequency range (~3.3 Hz). Colored lines indicate synchrony (IPC values >0.51) between electrode pairs of the two guitarists. From Lindenberger et al.
(2009) (C) Two representative computational models of interpersonal coordination belonging to different families. Top: diagram illustrating the (linear) ADaptation
Anticipation Model (ADAM) [adapted from van der Steen and Keller (2013)]. Bottom: diagram illustrating one of the experimental tasks modeled with the Strong
Anticipation in Periodic Perception Action (SAPPA) [adapted from Roman et al. (2019)].
S.F. Abalde et al.
Neuroscience and Biobehavioral Reviews 167 (2024) 105816
4
requires the simultaneous and continuous integration and segregation of
self and others’ information to facilitate group cohesion. The prioriti-
zation process can vary with momentary demands depending on the
context, e.g., by directing more attention to the joint outcome when
one’s own part is familiar and automated.
Recent studies support the idea that integration and segregation co-
exist during JMM. In Paas et al. (2021), dyads of pianists played scores at
fast tempi, either at the same octave or one octave apart. Even though it
was presumably challenging to distinguish between the two parts when
played at the same octave, spontaneous errors (induced by the
requirement for fast performances) selectively impaired one’s own
performance, but not that of the other. This nding might suggest that
expert musicians are well-trained in the simultaneous integration and
segregation of their own and others’ performances. MacRitchie et al.
(2018) more directly addressed this issue through the behavioral anal-
ysis of piano duos while creating incongruence in expressive goals. This
was achieved by differentially instructing each player to execute
particular tempos (one pianist accelerates, the other decelerates) and
dynamics (one pianist increases, the other decreases loudness). Results
indicated that dyadic coordination (assessed by interpersonal synchrony
of keystrokes) improved steeply with repetitions of the performance,
suggesting a process of rapid nonverbal negotiation. Moreover, there
was evidence that pianists prioritize the collective goal, sacricing their
own goal, when incongruences occurred in tempo. In contrast, when
instructions differed in relation to dynamics, pianists did not compro-
mise to attain a coherent joint outcome, but prioritized their own goal
(MacRitchie et al., 2018). Besides unveiling the importance of tempo
alignment on joint musical performance, these ndings suggest that
there is a degree of exibility in how musicians integrate and segregate
self and others’ goals, and that such exibility might depend on the
specic dimension upon which the coordination is built.
Consistent with these ideas, another study addressed self-other
integration and segregation in JMM with non-musicians, extending
the previous studies with musically trained pianists. Lie-
bermann-Jordanidis et al. (2021) studied musical production and
interaction with non-musically-trained participants using digital in-
struments in the form of E-music boxes. The E-music box is an electronic
music box that produces music whose tempo is controlled by the rota-
tion speed of the hand movement (Novembre et al., 2015). Lie-
bermann-Jordanidis and colleagues examined the role of two different
sensory modalities (visual and auditory) through manipulations of the
pitch played by each participant (either same or different) and the fre-
quency of the rotary movements required to perform the melody in
synchrony at the correct tempo (same or different), respectively.
Rhythmic interpersonal coordination was found to be worse when the
pitch played by each participant was the same and/or the frequency of
the rotary movements performed to produce the melody differed. In
contrast, when the pitch of the excerpt played by each participant was
different, but the movement frequency was the same, coordination
improved with respect to all other conditions (Liebermann-Jordanidis
et al., 2021). These results indicate that segregation and integration are
also exible based on the sensory modality through which musicians are
interacting. Similar outputs are better integrated in the visual domain
but might worsen performance in the auditory domain (at least in
non-musicians). Putting all studies together, it appears that the balance
between self-other integration and segregation is a exible process that
takes several factors into account such as select musical dimensions and
distinct sensory modalities.
2.2. Neural processes
Studies addressing the neurophysiological correlates of people
coordinating to make music together have pursued two main lines of
research. One line focuses on the attentional mechanisms involved in
coordination during JMM. We address this literature under the label
single-brain approach, simply because these are studies where brain
activity is measured from interacting individuals and analyzed sepa-
rately for each person and independently of the partner’s brain activity.
Complementarily, we refer to a second group of studies taking a multi-
brain approach, i.e., simultaneously measuring neural signals from
multiple performers to examine relations between them.
2.2.1. Single-brain approach
Several studies investigating the neural processes underlying atten-
tion to self and other during JMM have recorded neural signals in dyads
to examine how the brain responds to unexpected musical outcomes
(Lappe et al., 2017; Loehr et al., 2013; Paas et al. (2021)). Because the
timing of these responses is potentially informative about underlying
processes, these studies used neuroimaging techniques such as electro-
encephalography (EEG) or magnetoencephalography (MEG), yielding
so-called event-related potentials (ERPs) or elds (ERFs), respectively.
These responses are amplitude modulations in the electrical brain ac-
tivity generated in response to salient events (Luck, 2014). As such, ERPs
and ERFs are phase-locked neural responses, comprising positive and
negative peaks (indicated by ‘P’ or ‘N’, respectively) which occur at
different latencies (indicated by a number representing the ordinal po-
sition or peak latency of the waveform in milliseconds) depending on the
task or stimuli that evoked them.
Researchers examining the neural processes of single brains during
JMM have hypothesized that performance alterations of the musical
outcome would elicit ERPs, as salient events deviating from the per-
former’s prediction. In line with this hypothesis, a study with non-
musicians performing piano duets before and after short-term training
reported a negative electrophysiological component, i.e., mismatch
negativity (MMN), which was evoked in response to both melodic and
rhythmic performance errors (Lappe et al., 2017). These MMN responses
signicantly increased after training and, notably, they appeared
regardless of whether errors occurred on their own or their partner’s
part. This result provides a neurophysiological correlate of the integra-
tion processes we described above (Section 2.1.3): if musicians integrate
their performance with that of their partner, they should monitor all
performances simultaneously and respond to unexpected events elicited
by either.
Neural responses reecting this self-other distinction/segregation
were also found in a study of duetting pianists exposed to pitch alter-
ations in their performance (Loehr et al., 2013). In accordance with the
ndings by Lappe et al. (2017), the pianists exhibited feedback related
negativity (FRN), a type of negative ERP component recorded from EEG
elicited by performance errors of both the self and the other. Self-other
distinction was associated with larger amplitudes of the positive
component P300, as shown in Fig. 2B (top panel). The P300 amplitudes
were larger during self-performance alterations compared to the other,
and when alterations affected the joint outcome in comparison to the
individual outcome. Specically, the P300 ERP component was
enhanced when pitch alterations affected the harmony emerging from
the combined self- and other-related pitches (Loehr et al., 2013). This
result provides another neurophysiological substrate responsible for
self-other integration, notably in a non-temporal musical dimension.
Building upon this previous work, Paas et al. (2021) explored neural
and behavioral responses to ambiguity about one’s own or others’ per-
formance errors during JMM in their study of piano duos playing mel-
odies in pitch unison or in octaves. The study distinguished two error
types, note substitutions and additional notes, which were found to be
associated with differences in event-related potentials and behavioral
responses. However, ambiguity in the source of performance errors
appeared to have no effect on neural processes and behavior. This could
be because effective internal models, based on strong links between
perception and action, allow individuals to differentiate between
themselves and others even when faced with uncertainty. Accordingly,
ambiguity did not lead to any reliable modulation.
Together, the above studies measuring ERP/ERF following perfor-
mance errors in dyads reveal potential neural mechanisms that may
S.F. Abalde et al.
Neuroscience and Biobehavioral Reviews 167 (2024) 105816
5
support self-other integration and segregation in JMM. More research is
necessary to elucidate the nature of these neural mechanisms.
2.2.2. Multi-brain approach
Many studies measuring neural activity during JMM have monitored
multiple brains at the same time, a process known as multi-brain
recording or hyperscanning (Montague et al., 2002). Some of these
studies adopt a multi-brain approach also while analyzing neural data, i.
e. attempting to identify inter-personal dependencies between simulta-
neously recorded neural oscillations, namely measures of EEG phase or
power, extracted from multiple performing musicians in hyperscanning
setups. Lindenberger et al. (2009) conducted a pioneering study of this
type by simultaneously recording EEG from pairs of guitarists playing
together. They reported interpersonal neural synchrony (INS) when
musicians were both playing a melody together, as well as preparing to
play while listening to a metronome, as shown in Fig. 2B(bottom panel).
This result provides an additional measure of interpersonal synchroni-
zation, which notably builds upon neural processes and might comple-
ment behavioral measures. Extending this nding, the same group of
researchers used a graph-theoretical approach to further characterize
such synchronization across musicians’ brains. Hyper-brain networks –
encompassing synchronized brain regions across musicians – feature
dynamic small world topology changing across time and frequency
bands among two or four musicians (S¨
anger et al., 2012; Müller et al.,
2013, 2018, 2021). Further work has also demonstrated that these
hyper-brain networks can also be characterized by computing the con-
nectivity between guitarists’ EEG and the acoustic signal elicited by the
musical instruments (Müller and Lindenberger, 2019). Additionally,
these complex hyper-brain networks have also been investigated be-
tween a quartet of guitarists and four audience members, thus capturing
brain dynamics of musical communication at multiple levels simulta-
neously (Müller and Lindenberger, 2023). Methodologically deviating
from phase-based approaches, other studies with wireless EEG hyper-
scanning of piano duets focused on amplitude-based analysis of INS by
computing the power spectral density and EEG amplitude envelopes
(Zamm et al., 2018; Zamm, Palmer, et al., 2021). These studies showed
that neural oscillations were period-coupled between duet pianists,
corroborating phase-based research. Together, this group of studies
demonstrates the potential of EEG hyperscanning to measure the degree
to which people’s brain signals synchronize (or, possibly, desynchronize
- see Froese et al., 2024) across individuals when they interpersonally
coordinate to make music together.
Nevertheless, a potential limitation of the previous experiments is
that INS was recorded while participants were exposed to similar sen-
sory input (Novembre and Ianetti, 2021). When two brains receive the
same input at the same time, their activities might seem synchronized,
when, in fact, they might simply yield similar responses to events
happening simultaneously. Some studies have tried to address this issue.
For instance, Novembre et al. (2017) synchronized two brains exoge-
nously using in-phase transcranial alternating current stimulation
(tACS), a non-invasive brain stimulation technique that relies on
small-amplitude electric currents. Participants were able to tap their
ngers in synchrony with one another more accurately when both brains
were coupled in-phase through stimulation in the beta band (which is
known to regulate action timing in several contexts including JMM;
Rosso et al., 2022; Zamm, Palmer, et al., 2021, see also Zamm, Debener,
et al., 2021). This nding provides causal evidence that INS might be
important for regulating interpersonal synchronization (see also Pan
et al., 2021; Lu et al., 2023). However, it should be noted this study
specically addressed INS in the motor cortex and relied on a specic
nger tapping task. Studies might be developed in the future to
follow-up existing JMM studies and address causality.
A related research question was recently addressed by Gugnowska
et al. (2022). The authors investigated whether INS emerges in the
absence of a shared musical outcome in JMM. INS was measured during
both the planning and execution of piano duets consisting of two short
phrases with an intervening silent pause. Pianists prepared to change to
the same tempo (congruent), or both planned a different tempo change
(incongruent) during the pause. INS was operationalized as the period
matching of oscillating gamma envelopes. INS was higher when pianists
were planning a congruent, as opposed to an incongruent tempo change.
This result shows that INS does not merely depend on shared input, and
suggests that similar motor plans are sufcient to elicit INS. This is
reminiscent of the behavioral study by Goupil et al. (2020), where
congruent reports of intended musical changes predicted better inter-
personal coordination. Here, INS predicted musicians’ intended tempo
change (Gugnowska et al., 2022). Hence, both behavioral coordination
and neural synchrony might capture shared goals or intentions across
ensemble musicians. More generally, the above studies indicate that INS
can serve as a valid and reliable marker of coordination—at least in the
temporal dimension—during JMM.
2.3. Computational models
Computational models of rhythmic interpersonal coordination can
be powerful means to unveil the mechanisms involved in JMM by
providing access to hidden or latent parameters underlying behavior
and brain activity. For instance, one such mechanism is reactive tem-
poral adaptation, which pertains to the processes through which in-
dividuals adjust their temporal behaviors in response to external cues.
By studying these different mechanisms, researchers can gain a better
understanding of different behavioral and neural phenomena, providing
a valuable tool for uncovering and investigating the factors that
contribute to interpersonal coordination during JMM. These computa-
tional models encompass diverse perspectives, which can mainly be
divided into two different families: linear and nonlinear models (Clayton
et al., 2020). Linear models are characteristically event-based and take a
discrete information processing approach, whereas nonlinear models
based on dynamical systems theory address the system as interacting
coupled oscillators. We group linear models into two separate sections:
linear error correction models and linear anticipatory models. This
subdivision was motivated to highlight that linear anticipatory models
incorporate the concept of anticipation to predict future events, some-
thing that traditional linear error correction models do not achieve.
Following the linear models, we present two nonlinear model types,
namely dynamical systems models, and lastly, hybrid models that syn-
thesize dynamical systems with probabilistic models (i.e., predictive
coding).
2.3.1. Linear error correction models
According to information processing theory, internal processes un-
derlying synchronization and timing mechanisms are explained as time
series of discrete events. Rhythmic tasks, such as tapping in synchrony
with an auditory metronome, involve adaptation of internal timekeepers
to correct synchronization errors arising between movements and
sounds in the pacing sequence (Repp, 2005). These correction processes
entail making adjustments that compensate for a percentage of each
asynchrony when generating the timing of the next action. When the
sequence is isochronous (i.e., the events occur at equal time intervals) or
contains small timing variations, such corrections are performed auto-
matically by participants via a process known as phase correction.
Different rst-order linear models have been proposed to explain phase
correction when an individual is synchronizing with an isochronous
metronome (Vorberg and Wing, 1996), a time-varying click sequence
(Schulze and Vorberg, 2002; Vorberg and Schulze, 2002), or an adaptive
virtual partner (Repp and Keller, 2008; Vorberg, 2005). Additionally,
another study extended such models to ensemble synchronization
within a string quartet (Wing et al., 2014), which is further explained in
the computational models of goals and strategies (Section 4.3.1).
In contrast to phase correction, which is a local adjustment of the
internal timekeeper, period correction is an adaptive mechanism that
changes the timekeeper interval during rhythmic synchronization. This
S.F. Abalde et al.
Neuroscience and Biobehavioral Reviews 167 (2024) 105816
6
type of reactive correction is relevant when attempting to synchronize to
an auditory sequence that contains large deviations from temporal
regularity, such as tempo changes in expressive musical performances
(Schulze et al., 2005). Period correction involves changing the base in-
terval of the internal timekeeper under the guidance of cognitive pro-
cesses such as attention and awareness (Repp and Keller, 2004). By
testing a formal model encompassing both phase and period correction
(Mates, 1994a, 1994b), Repp and Keller found that phase correction
occurs automatically without having to consciously acknowledge the
timing error. Since phase correction alone is not sufcient to maintain
synchronization in the face of tempo changes, period correction is also
necessary and requires more cognitive effort, like paying attention to
tempo changes (Repp and Keller, 2004). Furthermore, in a set of ex-
periments exploring synchronization with virtual partners that adapted
to varying degrees, participants maintained a consistent level of phase
correction when tapping along with cooperative virtual partners, i.e.
employing positive phase correction settings that contributed to joint
error correction (Repp and Keller, 2008). However, in response to un-
cooperative virtual partners that implemented negative phase correc-
tion, which increases asynchronies, participants exibly adjusted their
error correction strategies to deal with this challenging form of
coordination.
2.3.2. Linear anticipatory models
There is evidence that reactive temporal adaptation mechanisms are
supplemented by temporal anticipation processes that come into play
during synchronization with sequences that change tempo (Repp, 1999;
Rankin et al., 2009; Pecenka and Keller, 2011). In light of such evidence,
van der Steen and Keller (2013) proposed an Adaptation and Anticipa-
tion Model (ADAM) of sensorimotor synchronization, which takes into
account the role of both processes in achieving precise yet exible
interpersonal coordination during JMM (see also van der Steen, Jacoby,
et al., 2015; van der Steen, Schwartze, et al., 2015; Harry and Keller,
2019). Temporal adaptation and anticipation are included in ADAM as
different modules that can interact with one another, as illustrated in in
Fig. 2C(top panel). The adaptation module implements phase and period
correction based on asynchronies between sounds produced by self and
other. This module then feeds its output to a ‘self’ internal model that
generates an action plan for one’s own upcoming movement. In parallel,
the anticipation module determines the likely timing of future sounds
produced by the other individual based on the previous inter-onset in-
tervals received as input. This information is passed to an ‘other’ internal
model, which generates temporal predictions about upcoming sounds.
The outputs of the self and other internal models are combined in a joint
module, which compares the plans and predictions and reduces in-
consistencies by implementing an anticipatory error correction mecha-
nism that adjusts action plans before the next movement is executed.
This process thus reduces potential temporal discrepancies between
action plans in ‘self’ internal models and predictions in ‘other’ internal
models by balancing the degree of self-other integration and segregation
via attentional regulation (van der Steen, Jacoby, et al., 2015; Harry and
Keller, 2019).
The functional relevance of the ADAM modules has been evaluated
in simulation studies and experiments in which different versions of the
model are applied to behavioral data from sensorimotor synchronization
tasks. One study assessed the t of three different versions of the model
to data from a task that required synchronization with pacing sequences
with varying degrees of tempo change, ranging from simple to complex
tempo changes designed to resemble those found in expressively timed
music (van der Steen, Jacoby, et al., 2015). It was found that, with more
complex tempo changes, model ts were improved when the anticipa-
tion model was included in addition to the adaptation module, and even
more so when adaptation and anticipation were incorporated into the
joint module, allowing for anticipatory error corrections reecting the
integration of information about self and other. Consistent results were
observed in a subsequent study showing that individual differences in
sensorimotor synchronization skill can be explained largely by adapta-
tion alone in the context of synchronization at a steady tempo, whereas
tempo changes benet from anticipation and self-other integration
(Harry et al., 2023).
2.3.3. Dynamical systems models
From the dynamical systems perspective, synchronization is
conceptualized as the entrainment of nonlinear oscillatory processes
that become coupled with one another through interaction. This
computational framework is based on principles of theoretical physics
aimed at explaining how systems couple through information transfer
(Kelso, 1995; Strogatz, 2015). In contrast to discrete information pro-
cessing frameworks, dynamical systems models approach error correc-
tion as a continuous coupling of oscillators. An early application of this
principle that is relevant to musical synchronization is found in Dynamic
Attending Theory (Jones, 1976; Jones and Boltz, 1989). This theory,
which was originally developed in the context of rhythm perception,
proposes that internal neural oscillations constitute attending rhythms
that play a role in facilitating the processing of sounds by entraining to
auditory sequences (Jones, 2019; Large and Jones, 1999; Large and
Kolen, 1994; Large and Palmer, 2002; Large et al., 2015). The original
formulation of Dynamic Attending Theory in perception has been
extended to account for social interactive behaviors, such as those per-
formed during JMM or rhythmic synchronization (Large and Grondin,
2008; Large et al., 2002; Loehr et al., 2011).
A branch of computational models based on dynamical systems
theory has addressed interpersonal coordination in rhythmical tasks,
such as nger tapping paradigms (Demos et al., 2019; Heggli, Cabral,
et al., 2019; Roman et al., 2019) or collective free improvisation
(Canonne and Garnier, 2011, 2012). It has been proposed that limb
coordination encapsulates the basic timing demands of joint actions
including JMM (Schmidt and Richardson, 2008). These proposed
models are based on bidirectional dynamical systems. Each interacting
individual’s action and perception are modeled as two nonlinear
coupled oscillators that interact across distinct spatiotemporal scales, as
individuals may rely on the shared auditory outcomes of both their own
and their partner’s actions.
Heggli, Cabral, et al. (2019) proposed a Kuramoto model, in which
each individual’s perception and action are formalized as two oscillators
coupled with each other. In addition, the actions of one individual are
coupled to the perception of the other. Nevertheless, this model does not
address anticipation during interpersonal synchronization between
participants (Heggli, Cabral, et al., 2019). In contrast, other models
address a form of anticipation—the tendency for nger taps to precede
tones in sensorimotor synchronization tasks (Repp, 2005)—by intro-
ducing delay coupling (Demos et al., 2019; Roman et al., 2019; see also
Stepp and Turvey, 2010). (Note that this is qualitatively different from
the concept of anticipation in ADAM, where the term refers to predicting
upcoming tempo changes in a pacing sequence (van der Steen and
Keller, 2013)). In delay coupling, an oscillator compares externally
received input with the time-delayed memory of a past state, and thus,
models anticipation based on past and current oscillations.
In a bidirectional delay-coupled dynamical system model called the
Mutual Anticipation and Adaptation Model (MAAM; Demos et al.,
2019), both components share information and are delay-coupled to
account for mutual adaptation and anticipation during synchronization
tasks. In a similar line, the Strong Anticipation in Periodic Perception
Action (SAPPA) model uses a time-delayed single oscillator which
adapts to a stimulus frequency (Roman et al., 2019). The single oscil-
lator is unidirectionally coupled to the stimulus to which it is synchro-
nized (either a metronome or another person’s action). For example,
when tting experimental data from interpersonal coordination with
this model, each participant is modelled as a SAPPA model, as illustrated
in Fig. 2C (bottom panel). These models have successfully t experi-
mental data. In particular, they proved capable of differentiating syn-
chronization strategies, such as leading-following or mutual adaptation,
S.F. Abalde et al.
Neuroscience and Biobehavioral Reviews 167 (2024) 105816
7
in the context of a tapping task. As such, these models will also be
addressed in Section 4.3.3, where we explicitly refer to synchronization
strategies.
2.3.4. Synthesis of predictive coding and dynamical systems models
In addition to canonical models adhering to a specic conceptual and
formal tradition, there have been efforts to describe hybrid models that
emphasize the equivalence or combination of different approaches (see
Harry and Keller, 2019; Keller, 2023; Pressing, 1999; see also Palmer
and Demos, 2022).
One such model is the Metastable Attractor Model of Self–Other
Integration (MEAMSO; Heggli, Konvalinka, Kringelbach, et al., 2021),
which conceptualizes interpersonal synchronization as continuous
evaluation of the action and perception processes involved in self-other
integration and segregation. Through the lens of the dynamical systems
perspective, this time-varying balance between integration or segrega-
tion can be described as metastability between attractors (Kelso and
Tognoli, 2007; Tognoli and Kelso, 2014), comprised of different pre-
dictive models - hence motivating the classication of this model as
hybrid. The social nature of interpersonal coordination, and especially
the role of prediction during interpersonal coordination tasks such as
JMM has previously been addressed by applying the predictive coding
framework (Vuust et al., 2022; Friston and Kiebel, 2009; Kilner et al.,
2007; Koelsch et al., 2019). This framework stems from the elds of
information theory and systems neuroscience, building upon Bayesian
statistics, as conceptualized by Helmholtz (1867). Based on predictive
coding theory, perception and action during joint tasks like JMM are
considered top-down processes, implying that the perception of one
person’s actions become another’s perceptual input (Vuust et al., 2022).
When joint action is specic to musical performance, top-down pre-
dictions are also inuenced by internal models of musical elements such
as rhythm or harmony. In MEAMSO, each member of a dyad is modeled
as two different predictive cognitive models, one for self- and one for
other- produced actions. These models are segregated and/or integrated
according to the synchronization strategy adopted by the dyad (see also
Section 4.3.3). The resulting interpersonal interaction is described as a
metastable system featuring two distinct attractor states: self-other
integration (collapsing the predictive self- and other- models into one
model) and self-other segregation (preserving separate predictive
models). Hence, MEAMSO could be considered as a combination of
dynamical systems and predictive coding.
2.4. Interim conclusions on interpersonal coordination
Behavioral research has shown how JMM entails interpersonal co-
ordination in time and across multiple acoustic dimensions. At least
three classes of factors appear to support such coordination: external-
oriented sensory processes (mostly audition), endogenous properties
of each player (e.g., spontaneous/preferred tempo), and higher-level
cognitive processes that balance the integration and segregation be-
tween self- and other-related information. Neuroscientic studies have
provided evidence about how such cognitive processes are biologically
grounded: musical events that are unexpected, either due to their timing
or spectral composition, trigger error-like neural responses. Notably,
these responses depend on the combined outcome of self- and other-
generated actions, and can relate both to events that are associated
with the performance of the self or of other individuals. Furthermore,
multi-brain studies typically examine mixed external-oriented sensory
processes and endogenous properties of each player by measuring inter-
brain synchrony. As an estimate of the similarity between the time
courses of self and others’ brain activity, interbrain synchrony has been
used to track temporal coordination during JMM. However, how the
above neural processes operate is far from understood. A promising
approach to gaining such understanding comes from work on compu-
tational modeling of JMM. A variety of computational models have been
employed to simulate temporal coordination. These rely on different
families of model architectures and assumptions, describing the above
processes either linearly (event-based) or nonlinearly (coupled oscilla-
tors), occasionally also incorporating predictive processes as a key
ingredient. Given the large range of neural resources and the existence
of both linear and nonlinear neural signatures, there is no a priori reason
to discount the complementarity of these different approaches and their
eventual integration to address the rich complexity of human brain and
behavior in the context of JMM.
3. Knowledge
3.1. Behavioral ndings
Interpersonal coordination in JMM can be inuenced by knowledge
acquired by rehearsing specic musical material or the instrument itself.
Performers develop different types of familiarity in the context of JMM.
We classify the types of familiarity that impact JMM in two main groups:
sensory familiarity and motor familiarity. The rst group refers to the
familiarity that is developed when listening to a musical piece, to a co-
performer’s idiosyncratic style of playing, or even playing along
repeatedly with the same people. The latter, motor familiarity, addresses
the motor knowledge that is developed through actively rehearsing the
specic musical material (as performed by the self).
3.1.1. Sensory familiarity
Musicians have idiosyncratic playing styles and timing patterns that
differ from those of other musicians (Loehr and Palmer, 2011; Repp,
1992, 1998; Repp and Knoblich, 2004; Zamm et al., 2016). Interestingly,
musicians seem to be able to use their sensory familiarity with the
co-performers’ idiosyncratic playing style when rehearsing and per-
forming together. To explore this effect, Ragert et al. (2013) studied the
keystroke coordination of piano duets, which occurs at the millisecond
level (i.e., micro-timing synchronization), as illustrated in Fig. 3A. The
duets became more familiar with each other’s idiosyncratic playing style
during rehearsals, and musicians who were more familiar with the
others’ playing style then went on to improve interpersonal keystroke
synchronization with performance repetitions. In line with these nd-
ings, synchronization between expert and novice pianists during joint
performance was enhanced when experts were familiar with the unique
timing pattern of novice players (Wolf et al., 2018). Moreover, other
studies have reported that performers rehearsing together develop rep-
resentations of joint musical outcomes (Loehr and Vesper, 2016), as well
as more consistent gesture patterns (Bishop et al., 2019b) and increased
mutual eye gaze and body movements (see Section 4.1).
Sensory familiarity has also been investigated in the context of col-
lective free improvisation (see Section 2.1.2). Familiarity amongst
improvising musicians, based on their reported frequency of impro-
vising together, predicts stronger coordination of self-reported musical
intentions during their shared performance (Goupil et al., 2020).
Musical intentions relate to their actions (whether to play or stop
playing) and their directional intentions (i.e., to change or to support the
joint musical outcome, as dened in Section 2.1). The more these
high-level general intentions are shared among co-performers, the more
performances became predictable and presumably easier to synchronize
with (Vaarten and Wiltshire, 2023). A related study suggested that
musicians who have experience improvising together, share similar
mental models of improvisational music (Canonne and Aucouturier,
2016). In other words, when they are more familiar with each other,
they think about free improvisation in a similar way, which may help
them to coordinate better. Interestingly, musicians who are experts in
‘free’ improvisation might even share higher-level knowledge, as they
do not create music based upon a referent piece or structure, but rather
based on the shared idea of what improvisation is (Canonne and
Aucouturier, 2016).
S.F. Abalde et al.
Neuroscience and Biobehavioral Reviews 167 (2024) 105816
8
3.1.2. Motor familiarity
Motor familiarity refers to the effects of knowledge developed by
executing the musical material, so motor representations embody a
performer’s action style and are unique to each individual. For example,
pianists coordinate better with their own recordings than with others’
(Keller et al., 2007; Loehr and Palmer, 2011). However, studies have
shown that when pianists previously rehearsed their co-performer’s part
and became familiar with it, their duet performances were less syn-
chronized (Ragert et al., 2013; Novembre et al., 2016; Gugnowska et al.,
2022; Kohler et al., 2022). These ndings show that motor familiarity
with a co-performer’s part can, under some circumstances, impair
interpersonal coordination. Presumably the predictions of the other’s
performance are based on one’s own idiosyncratic action
representations, and thus, have some degree of mismatch with the
other’s action. Yet, as previously discussed, this impairment in syn-
chronization can be reduced with sensory familiarity about the others’
playing style and through joint rehearsing (Ginsborg et al., 2006; Ragert
et al., 2013). It has been proposed that motor representations that
develop through motor familiarity with the musical material are linked
to the concept of musical imagery (Keller, 2008, 2012; Pecenka and
Keller, 2009, 2011). Such imagery is a high-level, idiosyncratic,
cognitive-motor skill presumably involving action simulation, working
memory, and internal models that are unique to each individual (Keller
and Appel, 2010) and may play a role in interpersonal coordination by
facilitating anticipatory processing during musical performance (Keller,
2012, 2014).
Fig. 3. Illustrative examples of research addressing familiarity in JMM. (A) Interpersonal synchronization accuracy grows stronger over time when players rehearse
together. However, synchrony is weaker when players have practiced each other’s parts (increasing motor familiarity) before rehearsing together [modied from
Ragert et al. (2013)]. (B) Prior motor familiarity leads to motor simulation processes in the brain, which support the rapid adaptation to others’ performance timing.
Accordingly, using repetitive TMS to interfere with motor simulation impairs pianists’ ability to adapt to others’ tempo changes [adapted from Novembre et al.
(2014)] (C) Key components of a theoretical model of JMM comprising internal models for Self, Other and Joint. These self and other internal models are inuenced
by knowledge (such as motor familiarity) [adapted from Keller et al. (2016)].
S.F. Abalde et al.
Neuroscience and Biobehavioral Reviews 167 (2024) 105816
9
3.2. Neural processes
Neural mechanisms underlying the inuence of familiarity on JMM
have focused on motor familiarity acquired by rehearsing the musical
material through practice. In this section, we provide an overview of the
current state of the art.
It has been proposed that motor familiarity acquired through
rehearsing the material implies the formation of a motor representation
in the brain, which is triggered by perceiving (without producing)
motorically familiar music (Novembre and Keller, 2014). Of note and
inspirational to this concept of motor representation is the discovery of
the mirror neuron system (reviewed by Rizzolatti and Sinigaglia (2010,
2016)). This system includes neurons in the pre-motor cortex (besides
other regions) that are active not only during motor action execution,
but also while observing the same action that belongs to the observer’s
motor repertoire performed by someone else. To examine the role of
motor familiarity during JMM, Novembre et al. (2012) conducted a
study with pianists that had previously rehearsed a bimanual musical
task but were only asked to perform the right-hand part. While they
were playing, single-pulse transcranial magnetic stimulation (TMS) was
applied over the right primary motor cortex (which controls the left
hand) and changes in corticospinal excitability were captured by
measuring motor-evoked potentials (MEPs) from the resting left fore-
arm. MEPs would normally be higher in amplitude when pianists listen
to the previously-rehearsed left hand part (D’Ausilio et al., 2006),
implying the presence of a motor simulation. Notably, when pianists
were led to believe they were performing with a partner executing the
left-hand part, they showed an increase in MEP amplitude as measured
in the resting left forearm, in contrast to when playing alone, and this
was the case even in the absence of auditory feedback from the
co-performer (Novembre et al., 2012). This suggests that motor simu-
lation during JMM has social signicance. A functional magnetic reso-
nance imaging (fMRI) study examined the underlying neural networks
of motor simulation during piano duos (Kohler et al., 2022). fMRI can be
used to localize brain activation with high spatial resolution by
measuring changes in blood oxygenation associated with neural activity.
The study revealed increased activity in premotor, parietal, and cere-
bellar sensorimotor areas by mapping the cortico-cerebellar audio--
motor neural networks involved in motor simulation. Consistent with
the previous ndings, the results suggested that motor simulation was
enhanced when playing with a co-performer, and thus, by the social
context of the task (for a review see Loehr (2022)).
Extending the above program of research, the hypothesis was tested
from another, more causal direction: if motor simulation is important for
coordination, then inhibiting motor simulation will impair coordination.
A series of studies applied repetitive TMS (a technique used to induce
transient virtual lesions in the brain) to pianists playing right-hand parts
of piano duets. Repetitive TMS produced inhibition in the right dorsal
premotor or primary motor cortex, which controls the left hand (Hadley
et al., 2015; Novembre et al., 2014; Timmers et al., 2020). The left-hand
part of the duet was not performed by the participants themselves but
was presented either in a recording to synchronize with or by their
partner. Interpersonal temporal coordination was impaired when the
participant had rehearsed the musical piece before (i.e., was familiar
with it), as illustrated in Fig. 3B. This is also in line with the notion that
inhibitory repetitive TMS over the dorsal premotor cortex impairs the
formation of auditory-motor associations (Lega et al., 2016). Together,
these studies demonstrate that the capacity for motor simulation is built
with rehearsal and then used by musicians to coordinate with others’
actions.
Similarly, the role of motor familiarity and the relationship between
motor simulation and coordination in JMM was investigated using EEG
measurements, which have a ner temporal resolution compared to the
TMS and fMRI techniques discussed above. In a study with single brain
measurements of piano duos, Novembre et al. (2016) showed that os-
cillations in the alpha power band are modulated by the interaction
between motor familiarity and coordination accuracy at the millisecond
timescale. Specically, alpha suppression was observed when self and
other performances were highly synchronized and participants were
familiar with each other’s parts. This was associated with both high
interpersonal coordination and self-other integration. Conversely, alpha
oscillations were enhanced when the players were familiar with each
other’s parts but their performances were slightly desynchronized. This
was associated with greater reliance on self-other segregation processes
(Novembre et al., 2016; see also Christensen et al., 2022 for a similar
result obtained measuring from a small orchestra). Shifting to
multi-brain measures, a recent study reported higher INS in the gamma
band when pianists were unfamiliar with each other’s part (Gugnowska
et al., 2022). A possible explanation for this nding could be that INS is
higher as a consequence of paying more attention to the joint outcome
due to the lack of familiarity of the other’s part. These results comple-
ment previous results from Novembre et al. (2016) by showing that
(behavioral) coordination accuracy is indeed higher when playing with
an unfamiliar part, and together suggest that intra- and inter-brain
measures might capture different aspects of JMM coordination dy-
namics, at least with respect to the role played by knowledge.
3.3. Computational models
From a computational perspective, mechanisms underlying coordi-
nation during JMM involve control of one’s own actions while predict-
ing and anticipating the co-performer actions to achieve a shared goal.
As in action control more generally, these processes may be underpinned
by internal models capturing the sensorimotor association between motor
commands and their sensory effects in the environment (Wolpert et al.,
1998, 2003). There are two types of internal models: forward and in-
verse models. Forward models represent the causal link between motor
instructions issued by the brain and their outcome on the body and the
environment. Inverse models, in contrast, provide the motor instructions
appropriate to produce an intended change in action outcomes. Re-
searchers in the computational and systems neuroscience elds have
studied these internal models, believed to arise in the cerebellum and to
interact with other regions of the central nervous system (Wolpert et al.,
1998; Tanaka et al., 2020; Welniarz et al., 2021). Internal models are
tightly linked to the concepts of musical imagery and motor simulation,
which have been hypothesized to play a role in guiding joint musical
action and can be enhanced with knowledge and familiarity (Jeannerod,
2001; Keller, 2014). As such, a number of internal model conceptuali-
zations of JMM have been advanced in varying degrees of detail (e.g.,
Keller, 2008, 2012; Keller et al., 2016; Müller et al., 2021; Pesquita et al.,
2018; S¨
anger et al., 2011).
Keller et al. (2016) proposed a conceptual framework specically
addressing joint musical performance. The framework comprises inter-
nal models about the self, other, and joint actions as shown in Fig. 3C
(Keller et al., 2016). This conceptualization builds upon a combination
of the computational model ADAM (van der Steen and Keller, 2013; see
Section 2.3.2) and the architecture proposed to link social interaction
and motor control through hierarchical internal models (introduced
earlier in this section). Self, other and joint internal models are arranged
at different hierarchical levels, operate at multiple timescales, and
comprise a combination of forward and inverse models. Self-internal
models, which are acquired and rened through knowledge and
training, play a role in action planning and motor control. Self-inverse
models assist in generating motor commands that are consistent with
desired goals. Self-forward models enable anticipation and prediction of
future motor actions by covertly running copies of the motor commands
arising from self-inverse models. Other-internal models generate pre-
dictions of the co-performer’s actions via top-down or bottom-up routes
depending on a priori knowledge (Phillips-Silver and Keller, 2012). For
example, when the performer knows the other’s goal, the predictions are
made via top-down processes, generating an inverse model of the others’
actions. These other-inverse models are developed with increasing
S.F. Abalde et al.
Neuroscience and Biobehavioral Reviews 167 (2024) 105816
10
familiarity, and other-forward models are calibrated to the co-per-
former’s action style. Nevertheless, in the absence of such familiarity,
other-internal models might be based instead on self-internal models
(Ragert et al., 2013) or on perceived information about the other player,
such as their body movements during performance. At a higher level, a
joint internal model is obtained by integrating the outcomes of the self-
and other-internal models. The joint internal model establishes the
shared goal with a dynamic representation based on a joint predicted
and estimated state (analogous to that in van der Steen and Keller, 2013
and discussed in Section 2.3). When inconsistencies arise between these
states, the self-inverse models are modied to compensate for the
discrepancy. In a related conceptual framework, Pesquita et al. (2018)
proposed a Predictive Joint-Action Model based on three hierarchical
processing levels: goal representation, action planning and sensory
routing. The authors propose that self- and other-internal models are
interdependent and continuously updated by the generated predictions
based on knowledge.
3.4. Interim conclusions on knowledge
Behavioral research has shown that musicians’ experiences and
repertoires impact coordination accuracy, notably with dissociable ef-
fects of sensory versus motor familiarity. Rehearsing with other musi-
cians leads to better coordination, both when performing the specic
material a group has practiced as well as new improvised music. This
suggests that sensory familiarity enables musicians to learn each other’s
idiosyncratic playing styles. Conversely, practicing a co-performer’s
musical part can impair coordination, at least when musicians start
performing together following solo practice. This implies that solo
practice establishes an association between a given piece and one’s own
idiosyncratic style, which may not align with that of another player,
thereby leading to impaired coordination accuracy when a practiced
piece is performed by another player. Neuroscientic studies shed light
on this nding by demonstrating that listening to practiced music acti-
vates motor regions in the brain, specically neural resources that would
be recruited if the listener were performing the music. When players
possess motor familiarity with each other’s performances, they coordi-
nate with others through a process of internally-generated motor
simulation. This scenario can be computationally formalized using in-
ternal models capturing the experience-dependent, sensorimotor asso-
ciation between motor commands and their sensory effects in the
environment. When these sensorimotor associations are lacking, players
pay greater attention to the actual performance of another, as indicated
by higher interneural synchrony and higher coordination accuracy.
Hence, prior motor knowledge shapes how we represent others in JMM,
and whether we orient attention intra- or extra-personally.
4. Goals and strategies
4.1. Behavioral ndings
Successfully making music with others involves pursuing acoustic
and aesthetic goals that are shared between co-performers. These goals
may entail executing a musical score, a memorized piece, or an
appealing improvised performance. To achieve such goals, co-
performers can adopt different strategies while continuously
exchanging information between one another through multiple non-
verbal communication channels (see also Izen et al., 2023). Intui-
tively, one might expect this information to be solely transferred
through the auditory domain, given that music is something we pri-
marily listen to. However, musicians also communicate through the
visual modality. For example, a musician might signal the intention to
start a performance by head-nodding. Similarly, a group of musicians
might designate a leader to set the tempo using body gestures. These
ancillary movements are not technically necessary to produce musical
sounds but nevertheless typically accompany performance (Jensenius
et al., 2010). In contrast, instrumental movements are those that act
directly upon an instrument and are essential to produce musical
sounds. While not sound-producing, ancillary movements become
communicative when they can be seen, as they can carry information
about both the musical structure (Ragert et al., 2013; Wanderley et al.,
2005) and the mood or emotion that a player aims to express (Castellano
et al., 2008; Chang et al., 2019; Davidson, 2012).
In this section, we rst present studies that highlight the communi-
cative functions of ancillary movements during JMM tasks. Then, we
address studies focusing on the specic strategies musicians use to
efciently share information. Specically, we will discuss studies that
explore how taking a leadership role in a musical ensemble, either
spontaneously or because of pre-established roles, can inuence the
quality of interpersonal coordination. The assumption we are making is
that leadership in JMM can be viewed as a strategy that a group of in-
dividuals adopts (intentionally or not) to achieve a future goal, i.e.
interpersonal coordination (the central component of our framework).
4.1.1. Communicative signicance of ancillary movements
The role played by ancillary movements during JMM has been
documented mostly through two types of evidence: comparing solo and
joint performances or by manipulation of visual feedback. The com-
parison of solo and joint performances has revealed that, e.g., violinists’
head movements are more predictable when playing in an ensemble
(Glowinski et al., 2013). Furthermore, during a piano duo performance,
pianists move their head less, but more smoothly, and these movements
tend to coordinate interpersonally (Bishop and Goebl, 2020). Studies
manipulating visual feedback have yielded comparable results. Violin-
ists playing within an orchestra have been shown to change the
intra-personal coupling between their (ancillary) head and (instru-
mental) bow movements when visual feedback of the co-performers is
perturbed, presumably as a strategy to boost intra-personal coordination
(Laroche et al., 2022). Similarly, musicians playing in a string quartet
reduce their head motion when their eye gaze is constrained (Bishop
et al., 2021). Together, these ndings indicate how ancillary movements
– specically head movements – might serve communicative functions
and facilitate coordination between co-performers.
However, ancillary movements are not always necessary for inter-
personal coordination, but instead may be useful in particular cases. For
instance, ancillary movements might help when it is challenging to
achieve coordination from sounds alone. Indeed, when duet pianists
performed pieces that did not contain large tempo changes or pauses,
seeing the other player did not improve coordination accuracy (Keller
and Appel, 2010). Yet, when a musical piece is relatively complex and,
for example, includes irregular timing, pianists make more eye contact.
This behavior improves synchronization accuracy, especially before
challenging parts such as tempo changes (Kawase, 2014b; Williamon
and Davidson, 2002).
The idea that ancillary movements are especially important when
coordination demands are higher has been supported by studies in
different JMM contexts. Visual cues seemed to facilitate synchronization
in piano and piano-violin duos when auditory information was limited.
The facilitative effect was stronger when feedback from the melody was
not available to the performers playing the accompaniment, and espe-
cially at entry points and after long pauses [(Bishop and Goebl, 2015);
see (Bishop et al., 2019a) for comparable ndings]. In a similar vein,
studies examining singers producing duet performances reported that
visual feedback increases note synchronization especially at the begin-
ning of the performances (D’Amario et al., 2018; Palmer et al., 2019).
Another study examined performers’ visual interaction as well as upper
body ancillary movements during improvised performances that either
had a regular pulse or were non-pulsed (Eerola et al., 2018). The per-
formers’ movements and their coordination were analyzed over multi-
ple timescales (i.e., across time and frequency) using cross-wavelet
transformation. With this method, the energy of the cross-wavelet
transforms predicted visual interaction between co-performers
S.F. Abalde et al.
Neuroscience and Biobehavioral Reviews 167 (2024) 105816
11
(labelled post-hoc by expert musicians) when the performance was
non-pulsed. Co-performers may have visually interacted more in the
non-pulsed performance due to greater uncertainty about auditory event
timing than in the regularly pulsed performance. This set of studies
highlights different perspectives on how ancillary movements during
JMM might be particularly important when coordination demands are
high due to temporal complexity, limited auditory information, and
heightened uncertainty.
4.1.2. Conductors as leaders of musical ensembles
The above-described communication dynamics change further when
JMM is regulated by a conductor. In this scenario, the musical ensemble
acquires a new property: the differentiation of explicit roles such as
leaders and followers, with the conductor acting as the leader. Con-
ductors have different leadership styles (Lanaro et al., 2023; Sutherland
and Cartwright, 2022) and rely on specic gestures to communicate
with the musicians (Luck and Sloboda, 2008; Luck and Toiviainen,
2006). These gestures are comparable to the ancillary movements
described above in that they rely on visual interaction and do not
directly produce acoustic effects. However, conductor gestures are
executed deliberately to fulll specic objectives in regulating most, if
not all, aspects of the performances of large numbers of musicians.
Conductors often use one hand (typically the one holding a baton) to
provide a temporal reference by giving beat gestures, while the other
hand regulates dynamics (loudness changes), phrasing, and other
expressive parameters including articulation (W¨
ollner and Auhagen,
2008). Many of these directives are conveyed during rehearsals, where
conductors also use verbal communication (and whatever other means
available, occasionally singing and even dancing) to share their ideas
with the musicians. This means that conductors’ gestures during actual
concert performances serve as memory cues in addition to aids to
real-time coordination.
Several studies have investigated how a conductor inuences tem-
poral coordination. Colley et al. (2018) created a virtual conductor by
averaging the body kinematics of three different conductors moving
according to a common trajectory (see W¨
ollner et al., 2012). Participants
were asked to drum in time with different visual cues: the virtual
conductor, a tempo-changing musical sequence, or a stationary circle.
Synchronization accuracy was higher when drumming with the virtual
conductor, as compared to drumming with the other visual cues. This
result is noteworthy in that it demonstrates that the movements
exhibited by a conductor facilitate temporal coordination for both mu-
sicians and participants without musical training, consistent with gen-
eral benets of multisensory information in guiding precise action
timing (see Elliott et al., 2010). In a follow up study, the effect of a
conductor was examined while two participants were asked to drum
together. Interpersonal synchrony was again higher when both partici-
pants could see the conductor as compared to when they could only see
each other (Colley et al., 2020), consistent with the results of another
study examining the coordination of a wind orchestra transitioning from
an uncoordinated group to an orchestrated performance with the
conductor (Proksch et al., 2022). Hence, the gestures of a conductor can
facilitate achieving coordination goals at both the individual and dyadic
level. Other studies have examined whether such information transfer
between the conductor and the musicians might also impact the
aesthetic appreciation of the performance in the audience. To do so,
D’Ausilio et al. (2012) estimated a quantitative measure of information
transfer from the violinists’ and conductors’ full-body kinematics
(comprising both instrumental and ancillary movements). This measure
was related to assessments of the music, as yielded by experts rating the
quality of the execution (D’Ausilio et al., 2012; Hilt et al., 2019). Results
indicated that higher-quality performances were associated with
changes in the directionality of information transfer between the per-
formers, specically with increased conductor-to-musicians and
decreased musician-to-musician information transfer. These results
reinforce and expand the conclusions from the previous studies, showing
that orchestras behave as complex networks, in which information about
body kinematics ows between the conductor and the musicians, as well
as between the musicians themselves. Most importantly, these ndings
highlight how ancillary movements not only impact coordination dy-
namics but might also have notable aesthetic effects measurable in the
audience (see also Varni et al., 2019).
4.1.3. Flexible musical leadership in ensembles
As mentioned above, a conductor can be seen as an explicit leader
who regulates the interaction between musicians. However, in musical
ensembles without a conductor, leadership roles can either emerge
spontaneously or be pre-established depending on a musician’s instru-
ment or their part in the notated score. Instrument-based leadership
often depends on musical conventions. For example, within a string
ensemble, the rst violinist is typically assumed to act as the leader
(Murnighan and Conlon, 1991). Some studies have shown that musi-
cians within a string ensemble tend to look at the rst violinist more
often than the rst violinist looks at the other musicians (Bishop et al.,
2023). Even if this result was not replicated for a second (expert-level)
quartet, which may suggest that the leadership role of the rst violinist
may vary under different conditions, another study showed that musi-
cians’ head movements are strongly driven by those of the rst violinist
(Glowinski et al., 2013).
These leadership dynamics within the ensemble, like ancillary
movements, might be particularly prominent when coordination de-
mands are high, such as when synchronization needs to be established at
the start of a piece (Bishop and Goebl, 2018a, 2018b). Duets consisting
of violinists, pianists, or a mix of both were alternatingly asked to act as
leaders and required to precisely establish synchronization (Bishop and
Goebl, 2018a). To achieve that, the designated leaders cued the beat
position using ancillary movements, specically head nods in which
peak acceleration indicated the beat. Analogously, tempo was signaled
through their head and bowing hand gesture duration and periodicity.
When comparing the different duet combinations, violin duos were
found to synchronize the best. This nding is perhaps due to the tradi-
tional leadership role of that instrument and maybe having more
ensemble experience as soloists. Additionally, communicative gestures
facilitated co-performer synchronization in piano duets when they were
smoother, higher in magnitude, and less prototypical (Bishop and Goebl,
2018b). Overall, leaders with more experience either in ensemble per-
formance or conducting executed more effective and higher quality
cueing gestures. Together, these results indicate that leadership
behavior is indeed exible but also constrained by factors such as
musical conventions and prior experience.
Strengthening the idea that leadership roles are not rigid, even when
pre-established, emerging research has shown that roles can change
depending on task demands (Davidson and Good, 2002; Glowinski et al.,
2013; Badino et al., 2014; Timmers et al., 2014; Wing et al., 2014; Chang
et al., 2017; Bishop et al., 2021; Kawase, 2014a; Heggli, Konvalinka,
et al., 2019). For example, Timmers et al. (2014) revealed complex
directional relationships in a string quartet by analyzing
cross-correlations of inter-beat intervals between different instrument
pairs. Namely, the viola was dependent on violin I, and violin I was
dependent on the cello, while bidirectional relationships existed be-
tween violin II and cello, as well as violin II and viola. These results
further imply that interpersonal networks in musical ensembles behave
in complex, and sometimes unpredictable manners. Information can
ow unidirectionally and bidirectionally, not necessarily relying on the
pre-established role assigned to the instrument or musical part per-
formed. In typical circumstances, bow speed analysis indicated that the
rst violin (violin I) set the tempo at the beginning of the piece (using
visual cues) and assumed leadership (Timmers et al., 2014).
Another study examined musicians’ upper body and instrumental
movements while manipulating the temporal and dynamic characteris-
tics of the score and only made these alternations known to the leader, i.
e., violin I (Badino et al., 2014). Granger causality was used to derive a
S.F. Abalde et al.
Neuroscience and Biobehavioral Reviews 167 (2024) 105816
12
measure of information transfer between musicians (i.e., driving force,
Fig. 4A). It was observed that effective leadership emerges when
co-performers share information about which person is the leader.
Chang et al. (2017) examined further causal relationships among
ensemble musicians with and without leadership roles. The leader role
was privately assigned to one of the musicians without the knowledge of
the others in different trials. Analysis of head movements of the musi-
cians using Granger causality revealed that assigned leaders exerted
more inuence on their co-performers and were less inuenced by them
in return. Together, these studies indicate that shared information about
leadership roles also impacts the exibility of leader-follower dynamics
during JMM.
4.2. Neural processes
In contrast to the abundance of behavioral research, we know less
about neural mechanisms that underlie the achievement of goals and the
implementation of strategies during JMM. Relevant studies have mainly
investigated the effect of dyadic synchronization dynamics, such as
comparing leaders and followers, on neural processes like INS. Notably,
this research has been conducted mostly using nger tapping paradigms,
and less often with collective music making tasks. In this section, we
review studies investigating the neural correlates of synchronization
strategies, rst focusing on single-brain measures and then examining
multi-brain measures, i.e., addressing the relationship between signals
generated by distinct brains.
4.2.1. Single-brain approach
Different neuroimaging techniques have been used to explore the
neural signatures of interpersonal synchronization strategies. In this
section, we organize the studies mainly according to the methodologies;
rst, we review those studies using functional magnetic resonance im-
aging (fMRI), followed by those recording electroencephalography
(EEG) and lastly, we address two studies: one using functional near-
infrared spectroscopy (fNIRS) and another one using magnetoencepha-
lography (MEG). Together, this research showcases the different neural
correlates of individual brains adopting leader-follower roles during
JMM.
Fairhurst et al. (2013, 2014) analyzed fMRI data from participants,
who had varying degrees of musical training, performing a nger tap-
ping task in which they were asked to synchronize with a virtual partner.
The virtual partner serves as an auditory pacing signal, that can
temporally adapt to the participants’ taps, simulating various degrees of
cooperativity. When the virtual partner is overly adaptative, partici-
pants perceive themselves as leaders, since they exert a greater inuence
on the auditory pacing signal. In this specic case, activation of the
participants’ right lateral frontal cortex was found (Fairhurst et al.,
2013, 2014). These areas are implicated in central executive control
Fig. 4. Illustrative examples of research addressing goals and strategies in JMM. (A) Left: Reconstructed musicians’ upper-body postures (and instrument positions)
yielded by motion capture recordings. Right: Diagram illustrating the inuence of each musician’s movements on the other musicians, as quantied using Granger
Causality (the thickness of the lines represents the strength of such “driving force”) [adapted from Badino et al. (2014)]. (B) Brain regions either receiving (blue) or
driving (red) information interpersonally, as estimated using directed phase transfer entropy. The illustration is meant to address changes in inter-personal neural
dynamics as a function of different synchronization strategies (leading-leading and mutual adaptation). From Heggli, Konvalinka, Cabral, et al. (2021). (C) Illus-
tration of the MEtastable Attractor Model of Self–Other integration (MEAMSO). Self and other exhibit different patterns of self-other coupling (left), as a function of
the adopted synchronization strategy, and dyadic nger-tapping behavior changes accordingly (right) [adapted from Heggli, Konvalinka, Kringelbach, et al. (2021)].
S.F. Abalde et al.
Neuroscience and Biobehavioral Reviews 167 (2024) 105816
13
processes and self-initiation of actions; the same brain areas were shown
to be less active in “followers” (Fairhurst et al., 2014).
Other studies have investigated the neural processes underlying
leadership roles of interpersonal rhythmic coordination (i.e., nger
tapping and piano duos) by analyzing participants’ EEG recordings
(Konvalinka et al., 2014; Washburn et al., 2019). Konvalinka et al.
(2014) measured EEG during a nger-tapping task in which participants
had to synchronize either with another partner or a computer metro-
nome. The ndings showed that alpha (~10 Hz) and beta (~20 Hz)
oscillations over motor and frontal areas were strongly suppressed
during the interactive task with a human partner as compared to tapping
along with the computer (Konvalinka et al., 2014). Suppression of alpha
oscillations is also found in joint action paradigms not relying on nger
tapping, yet including an interactive component (Dumas et al., 2012;
Naeem et al., 2012a, 2012b; Tognoli et al., 2007). Moreover, leaders
were characterized by alpha suppression over the frontal area both
before and during the task, a phenomenon that was not observed in the
followers’ brains (Konvalinka et al., 2014). This asymmetry between the
neural signatures of leaders and followers has been also reported in
other studies with different experimental paradigms and dual-EEG re-
cordings (Babiloni et al., 2007; Dumas et al., 2012) as well as in a fMRI
study with tango dancers (Chauvign´
e et al., 2018). Another study
recorded EEG in piano duos playing musical excerpts with a human or
computer partner (Washburn et al., 2019). The duos started with
alternating parts (melodically similar or dissimilar) and played a last
part in unison. Asymmetries were reported in alpha power. Together,
these studies highlight several neural markers of leadership behavior,
and further indicate that leadership dynamics might be captured in
asymmetries between neural activity in leaders and followers.
In addition to the above studies using EEG and fMRI neuroimaging
techniques, Vanzella et al. (2019)examined leader-follower dynamics in
violin duets using fNIRS. When comparing patterns of functional brain
activation in violinists performing solo vs a duet, they reported signi-
cant differences in the violinist playing the follower role, but not in the
violinist playing the leader role. Specically, greater activation was
found in temporo-parietal and somatomotor brain regions of the vio-
linists playing the 2nd violin part, but not in the violinists playing the 1st
violin part. Another study measured MEG from dyads of musicians
assuming leader-follower roles while playing MEG-compatible key-
boards (Yoneta et al., 2022). They found an increase in occipital alpha
and beta power when musicians were assigned the follower role, but not
when assigned the leader role.
Together, these single-brain studies indicate that asymmetries in
leader-follower dynamics map onto asymmetries in neural activity in
leaders and followers. Leader behavior appears to rely more on neural
networks that are associated with executive control and action initia-
tion, while follower behavior seems to rely more on networks that are
implicated in (multi) sensory processing.
4.2.2. Multi-brain approach
To assess leadership-related brain connectivity patterns, interper-
sonal neural directional information ow between brains was measured
while musicians performed rhythmical tapping tasks adopting different
leadership roles (Heggli, Konvalinka, Cabral, et al., 2021). Dynamic
phase-locking between pairs of brains was estimated after performing
source reconstruction of dual-EEG recordings. Stronger phase-locking
was found in the alpha range when musicians adopted a mutual adap-
tation strategy compared to a leading-leading strategy. The network
underlying this process primarily relied on the right-lateralized tem-
poroparietal area, which has been suggested to comprise relevant brain
regions involved in both musical perception and production (Zatorre
et al., 2007). A particular region within this network – the precuneus –
exhibited changes in information ow directionality depending on
synchronization strategies (Fig. 4B). During mutual adaptation, the right
precuneus mostly transmitted information to other brain regions, while
during leading-leading strategies it appeared to mostly receive
information, notably from a greater number of brain regions (Heggli,
Konvalinka, Cabral, et al., 2021). Relatedly, another research group
investigated the effect of musical roles on inter-brain synchronization
between guitar players using EEG (S¨
anger et al., 2012, 2013). The re-
sults showed that acting as leader or follower inuenced coupling
directionality, in both the alpha and beta frequency ranges. Particularly,
leader-to-follower coupling was more pronounced than
follower-to-leader coupling (S¨
anger et al., 2013).
While multi-brain studies examining the effect of leadership on brain
dynamics are still few in number, they are beginning to shed light on a
complex interplay between brain regions of interacting musicians. These
coupling mechanisms might help orchestrate interpersonal coordina-
tion, and their organization may change as a function of synchronization
strategies.
4.3. Computational models
Different families of computational models have been used to
formalize the mechanisms underlying goals and strategies during JMM.
We rst address linear (anticipatory and non-anticipatory) models that
pertain to leader-follower relationships using either nger-tapping or
JMM tasks. Subsequently, we describe computational models that
formalize emergent synchronization strategies, taking a dynamical sys-
tems perspective. Finally, we examine two computational models rooted
in predictive coding theory that highlight how sensorimotor strategies
can rely on body movements. Although the latter models were not
specically conceived for JMM or rhythmic interpersonal coordination,
we believe they could contribute to the understanding of how body
movements facilitate interactive tasks, including JMM.
4.3.1. Linear models
From the lens of computational models based on information theory,
interpersonal coordination involves different error correction mecha-
nisms (described in Section 2.3.1). Depending on how these mechanisms
are applied for each person involved in JMM, different strategies arise,
such as mutual adaptation, or leader-follower relationships. One study
applied a linear phase correction model (Vorberg and Wing, 1996;
Vorberg and Schulze, 2002) to string quartet performances (Wing et al.,
2014). By tting the asynchronies between pairs of musicians and
computing their corrections and interactions during their performance
in terms of their adjustments, they were able to describe leader-follower
relationships between performers of two different string quartets. For
example, the rst violin in one of the string quartets showed less
adjustment to the other performers, acting as a leader of the ensemble,
whereas the other quartet showed more mutual adjustment within
performers. Therefore, using this nested model of phase correction,
Wing et al. (2014) successfully accounted for different strategies adop-
ted by the performers to achieve temporal synchronization in real string
quartets. The quartet scenario modelled by Wing et al. (2014) involved
performing a piece of music at a steady tempo. For performances con-
taining large-scale tempo variations, it may be worthwhile to use ADAM
(van der Steen and Keller, 2013; Harry and Keller, 2019) to investigate
the effects of leadership on temporal predictions and self-other inte-
gration, in addition to reactive error correction (Sections 2.3.1 and
2.3.2).
4.3.2. Dynamical systems
Computational models based on dynamical systems theory have been
used to formalize emerging leadership dynamics – namely mutual
adaptation, leader-follower, and leading-leading – within dyads syn-
chronizing through nger tapping (Heggli, Cabral, et al., 2019; Heggli,
Konvalinka, Kringelbach, et al., 2021; Konvalinka et al., 2014). These
models rely on unidirectional or bidirectional delay-coupled oscillators
(see Section 2.3.3) to conceptualize different synchronization strategies
by modifying the coupling of these oscillators. For example,
leading-following relationships might be explained as the leader
S.F. Abalde et al.
Neuroscience and Biobehavioral Reviews 167 (2024) 105816
14
decoupling their motor action from the perception of the other’s actions,
while the follower more strongly couples their motor actions to the
perception of the leader’s actions (Heggli, Cabral, et al., 2019). Simi-
larly, another model reviewed above (MAAM; Section 2.3.3), originally
developed to account for mutual adaptation and anticipation, could be
used to model leadership dynamics, although this was not explicitly
done so far (Demos et al., 2019).
4.3.3. Synthesis of predictive coding and dynamical systems models
Notably, the hybrid model MEAMSO - inspired by both dynamical
system theory and predictive coding (see Section 2.3.4) - was specically
used to model emergent synchronization strategies (Heggli, Konvalinka,
Kringelbach, et al., 2021). As illustrated in Fig. 4C, each of the predictive
models for the self and other couple differently depending on the
adopted synchronization strategy. During leading-following, the leader
segregates predictive models of self and other while the follower in-
tegrates them. Instead, during mutual adaptation, both participants
integrate predictive models of self and other to form a shared model.
4.3.4. Probabilistic models
Two computational models, based on predictive coding theory, have
been developed to address goals and strategies underlying joint action
(Candidi et al., 2015; Pezzulo and Dindo, 2011). These models focus on
signaling mechanisms, i.e., the use of body movements or gestures to
convey information about intentions or goals, with a Bayesian approach
representing uncertainties and predictions in probabilistic terms. Pez-
zulo and Dindo (2011) developed a probabilistic model and applied it to
t data from an interactive human-computer game, where successful
performance relied on effective communication through signaling.
Related work explored links between signaling and the achievement of
shared goals through different probabilistic modelling of data from a
joint action task with leaders and followers (Candidi et al., 2015).
Findings show that signaling behavior emerges over time and facilitates
interpersonal coordination. This is reminiscent of above-reviewed
studies (Section 4.1) indicating that musicians communicate their in-
tentions using ancillary body movements [(Bishop et al., 2019b;
D’Amario et al., 2018; Eerola et al., 2018); see also (Goebl and Palmer,
2009)]. Hence, even though the current models were not developed to
specically target the communicative signicance of musical move-
ments, their contribution might be signicant to formalize some of the
observations made examining JMM. Further research examining this
issue directly would enrich the eld.
4.4. Interim conclusions on goals and strategies
Behavioral research investigating how musicians achieve collective
goals during JMM has extensively explored the role played by ancillary
body movements. Musicians use these movements as communicative
signals, often reinforcing temporal features of the music to augment
interpersonal coordination. Under conditions that include multiple
players or a conductor, complex networks and leadership dynamics
emerge, which can be often inferred from such body movements.
Neuroscientic studies examining the brains of leaders vs. followers
have provided evidence of the recruitment of distinct neural networks
based on leadership role: with leaders relying more on neural structures
implicated in executive control and action initiation, and followers
relying more on (multi)sensory processing. Additionally, studies exam-
ining the relationship between multiple brains have revealed that brain
activity in leaders predicts activity in followers, more than the converse
direction. From a computational perspective, synchronization strategies
and associated leadership behavior have been formalized using a wide
range of models including linear, dynamical systems, predictive coding,
and probabilistic models. Most of these models have focused on differ-
entiating the presumed mechanisms that underlie symmetrical (mutual
adaptation) vs. asymmetrical (leader-follower) synchronization strate-
gies, but very few have focused on JMM specically.
5. Social factors
5.1. Behavioral ndings
As an inherently social activity, JMM is tightly linked to social factors
like personality and prosocial behaviors, thus spanning both trait and
state variables (see Cervone, 2021; Power and Pluess, 2015). Research in
the eld has suggested a bidirectional relationship functioning as a
positive feedback loop between JMM and social factors (Keller et al.,
2014; Novembre et al., 2019; Tzanaki, 2022). As such, personality traits
inuence JMM in one direction, while JMM might result in prosocial
consequences in the other direction. Accordingly, we organized the
current section based on the two directions of the positive feedback loop:
rst, we present studies that investigate personality-related social fac-
tors inuencing JMM. Subsequently, we delve into studies exploring
prosocial consequences of performing interpersonal coordination tasks,
mainly JMM.
5.1.1. Personality-related social factors
Personality traits inuence JMM in various ways. Several studies
have reported that empathy, that is, the ability to understand and share
the internal states of others, shapes how people act in social contexts
(Singer and Lamm, 2009), including skill domains such as sports and the
performing arts (Sevdalis and Raab, 2014). Notably, studies in the music
domain have shown that empathy and behavior are intertwined in
different musical contexts (Carlson et al., 2018; Stupacher et al., 2021;
Carlson et al., 2019; Sevdalis and Keller, 2011, 2012).
Empathy is often conceptualized as a multidimensional capacity, to
the extent that it entails several components relating to individuals’
cognitive and perspective-taking capabilities, as well as their emotional
reactivity. These components can be assessed through the Interpersonal
Reactivity Index, a questionnaire with four subscales: fantasy, perspec-
tive taking, empathic concern, and personal distress (Davis, 1980,
1983). A specic research avenue within JMM has focused on the
component of empathy known as Empathic Perspective Taking (EPT).
This component reects how spontaneously one person adopts the
perspective of others and might be especially relevant for interpersonal
synchronization tasks.
Early evidence came from a nger tapping study in which partici-
pants were asked to tap along with a tempo-changing metronome. Re-
sults indicated that participants with relatively higher perspective
taking skills displayed anticipatory behavior indicating superior tem-
poral prediction abilities (Pecenka and Keller, 2011). This observation
was further corroborated by a following musical study, in which pairs of
non-musicians were formed by participants displaying relatively high or
relatively low EPT. Participants were asked to synchronize musical
outputs using electronic musical boxes (e-Music box, explained in Sec-
tion 2.1.3). Interpersonal synchronization was signicantly enhanced in
high EPT pairs as shown in the top panel of Fig. 5A (Novembre et al.,
2019). Notably, these individuals were further shown to be better at
predicting their partner’s timing, particularly when instructed to follow,
as opposed to lead the interaction. This process was further shown to
change over time, depending on the structure of the musical piece (i.e.
note density). In line with this, another study investigated piano-singer
duos and reported that singers with high EPT are relatively more likely
to adapt their leading-following behavior (indexed by the tendency of
head movements to anticipate or to lag behind) as a function of the
musical piece structure (D’Amario et al., 2023). Together, these results
suggest that EPT traits facilitate JMM, presumably by enhancing antic-
ipatory mechanisms that support interpersonal coordination.
Another personality trait that has turned out to be relevant in the
context of JMM is locus of control, which refers to whether people believe
that the events of their life are caused by internal forces (their own
behavior) or external forces (such as fate or God) (Rotter, 1966). Locus
of control affects interpersonal coordination, particularly temporal
adaptation. Using a nger tapping paradigm with an overly adaptive
S.F. Abalde et al.
Neuroscience and Biobehavioral Reviews 167 (2024) 105816
15
virtual partner that induces drifts in tempo, Fairhurst et al. (2014)
showed that participants with relatively higher external locus of control
used more phase correction, hence stronger coupling to the virtual
partner, than those with internal locus of control. This implies that
participants with an internal locus of control, who attribute the conse-
quences of their actions to be their own responsibility, tend to adopt a
leader role characterized by stabilizing the tempo of their own perfor-
mance and sacricing synchronization with the virtual partner. In
contrast, participants with an external locus of control prioritize syn-
chronizing their tapping to the partner, to the detriment of keeping a
constant tempo (Fairhurst et al., 2014). Nevertheless, it should also be
noted that another study with piano dyads who had incongruent goals
found that locus of control did not signicantly predict leader-follower
roles (MacRitchie et al., 2018), perhaps suggesting that experienced
musicians may be able to overcome effects of personality. Further
research is needed to clarify the role of locus of control on JMM, as well
as that of other personality-related social factors. For example, the
ve-factor model of personality (Digman, 1990; McCrae and John,
1992), that includes social-related factors such as extraversion, agree-
ableness, or openness, has been reported to positively correlate with
ensemble aptitude (Kawase, 2016). Studies on spontaneous movement
to music have also found links between these personality factors and
how musical emotions are embodied (Mendoza Garay et al., 2023), as
well as with their responsiveness to small tempo changes (Carlson et al.,
2016).
5.1.2. Prosocial effects
JMM has been shown to have prosocial consequences (Mogan et al.,
2017; Rennung and G¨
oritz, 2016) in a variety of populations including
healthy adults, children, and infants, as well as in clinical contexts
(Fusar-Poli et al., 2022; Tzanaki, 2022). Prosocial effects can emerge
from multiple (not necessarily music-specic) factors implicated in
JMM, be it the prevailing social context or the act of synchronizing
interpersonally. For instance, more cooperation and trust was observed
between participants after performing synchronized actions, such as
walking, dancing, nger-tapping, or singing together (Anshel and
Kipper, 1988; Hove and Risen, 2009; Wiltermuth and Heath, 2009).
Similar results emerge even in the absence of a truly interpersonal
synchronization task, e.g., when participants believed they were tapping
along with a partner, when, in fact, they were tapping with a computer
(Launay et al., 2013). Other studies have shown that rhythmic
interpersonal coordination tasks can enhance other aspects of proso-
ciality among participants involved in the joint action, such as social
bonding (McNeill, 1997), the sense of “team-ness” (Marsh et al., 2009),
group cohesion (Gordon et al., 2020; Vicaria and Dickens, 2016),
empathy (Behrends et al., 2012) and afliation (Hove and Risen, 2009).
Prosocial effects related to interpersonal synchronization, such as
JMM, emerge rapidly throughout ontogenetic development, and have
been observed in infants and preschool children. One study showed that
(14 month-old) infants were more likely to help an experimenter with
whom they had bounced in synchrony to music as compared to being
bounced asynchronously (Cirelli et al., 2014). Kirschner and Tomasello
(2009) found that preschool children synchronized more accurately
when drumming with a human partner, as compared to synchronizing
with a machine. The same authors reported that cooperative behavior
was boosted in preschool children after participating in a musical game,
consisting of singing, drumming, and walking synchronously to songs,
more so than those that performed the same game without the songs
(Kirschner and Tomasello, 2010). Another study reported that
emotional empathy in primary school children was enhanced after
performing interactive musical games during the school year, as
compared to both baseline assessment prior to the intervention and a
control group undergoing a non-musical interactive game (Rabinowitch
et al., 2013; see also Rabinowitch and Gill, 2021), as shown in the
bottom panel of Fig. 5A. These musical games aimed at facilitating social
interactions through music (i.e., aspects such as entrainment, imitation,
and exibility), while the control group targeted the same aspects
without the use of music by means of drama or story-telling exercises.
Together, these ndings suggest that JMM has robust prosocial conse-
quences that are already identiable during infancy and childhood (see
also Wan et al., 2023).
Relatedly, a growing body of research on music therapy and music-
based interventions in multiple healthcare and rehabilitation contexts
has investigated the prosocial consequences of JMM (Clements, 2018;
Overy, 2012). A notable example is the effect of joint musical activities
on participants with autism, a type of neurodivergence characterized by
difculties in neurotypical communication and social interaction
(Happ´
e and Frith, 2020). JMM therapeutic sessions have been shown to
facilitate social behaviors including joint attention (Kim et al., 2008;
LaGasse, 2014), emotional and motivational responsivity (Kim et al.,
2009), communication (Sharda et al., 2018), social attention (Srinivasan
et al., 2016), cooperation and self-regulation (Yoo and Kim, 2018) in
Fig. 5. Illustrative examples of research addressing social factors in JMM. (A) Relationship between empathic traits and prosocial behavior. Top: Participants with
relatively high empathic traits (but no musical training) are better able to synchronize interpersonally in a JMM task [adapted from Novembre et al. (2019)]. Bottom:
children engaging in musical activities show enhanced empathic skills [adapted from Rabinowitch et al. (2013)]. (B) Enhanced activity of the caudate after
interpersonal synchronized drumming. Activity of this brain region was also enhanced after receiving monetary rewards [adapted from Kokal et al. (2011)]. (C)
Diagram illustrating the relationship between empathy and interpersonal synchronization as a positive feedback loop [modied from Tzanaki (2022)].
S.F. Abalde et al.
Neuroscience and Biobehavioral Reviews 167 (2024) 105816
16
autistic individuals. Touching upon other rehabilitative settings, JMM
with prisoners and ex-prisoners has been shown to have positive effects
on their reintegration process to society. Effects include those such as
increasing their knowledge, sense of community, and creativity
(Bensimon, 2021; Bensimon and Gilboa, 2010; Tuastad and O’Grady,
2013).
Based on the demonstrated prosocial outcomes resulting from music
interventions with a variety of populations, it appears that JMM may
also promote health and impart clinical benets. The promising results
of the music interventions mentioned above suggest that it would be
fruitful to examine the specicity of JMM for prosocial outcomes, as well
as discernment of critical musical components that drive these effects.
Studies targeting specic components have already pioneered these
future directions (Latif et al., 2021; Spiro and Himberg, 2016; Yoo and
Kim, 2018). Still, more research is needed as well as greater multidis-
ciplinary collaboration.
5.2. Neural processes
In parallel to behavioral research, here we rst refer to research
examining the neural underpinnings of the social personality traits that
affect JMM. Then, we address studies that investigated the neurophys-
iological consequences of JMM using neuroimaging techniques and
endocrinological measurements.
5.2.1. Inuence of social-related personality traits
A series of studies has examined neural representations related to
Empathic Perspective Taking (EPT) in the context of JMM. Novembre
et al. (2012) obtained evidence that participants with relatively higher
EPT more strongly simulated the actions of another co-performer in their
own motor system. Complementary to this result, disrupting motor
simulation through repetitive TMS impairs interpersonal temporal co-
ordination, particularly in participants with higher EPT (Novembre
et al., 2014). Together, these results suggest that motor simulation – a
neural mechanism facilitating JMM (see Section 3.2) – is more devel-
oped in highly empathic participants (Sevdalis and Raab, 2014), who
benet from and rely more on it (Gallese, 2003; Gazzola et al., 2006).
The relationship between electrophysiological neural activity and
empathy in the context of actual JMM has also been explored. The EEG
of four members of a saxophone quartet was recorded during the
following contexts: while they were playing together, while observing
their previous performance, while engaging in a control task, or during
rest (Babiloni et al., 2012). The authors reported a positive correlation
between musicians’ empathic traits and neural activity associated with
the frontal Brodmann areas 44/45, specically when the musicians were
observing their previous performances. This correlation was interpreted
as revealing the neural basis of “emotional” empathy, a cognitive pro-
cess that appears to be related to perception rather than execution of
JMM. More recent studies have suggested that hyperscanning protocols
– particularly those entailing not only the simultaneous measurement of
multiple individuals’ brains but also their interdependencies (reviewed
in Sections 2.2.2 and 4.2.2) – constitute a promising method for un-
derstanding the role played by empathy in JMM (Roque et al., 2022).
5.2.2. Neurophysiological consequences
Exploring the prosocial effects of interpersonal synchronization, an
fMRI study measured the brain activity of non-musicians while drum-
ming a trained sequence along with another one they assumed was
produced by a partner (while in fact it was controlled by a computer)
(Kokal et al., 2011). Participants were not explicitly instructed to syn-
chronize with the partner but to play at their own pace. The caudate - a
pair of nuclei within the basal ganglia - was active both while syn-
chronizing with the partner (illustrated in Fig. 5B) as well as when
receiving monetary rewards. This overlap is consistent with the proposal
that achieving synchrony might be intrinsically rewarding. The caudate
activity also predicted participants’ collaborative attitude (towards the
partner) in a subsequent behavioral task, strengthening the idea that
such neural activity might have prosocial signicance.
Neural plasticity underlying prosocial effects of JMM has also been
demonstrated in neurodivergent children. More specically, autistic
children in a music-making group exhibited increased resting-state
functional connectivity between auditory and both subcortical and
fronto-motor brain regions in comparison to a control group who un-
derwent a non-musical intervention (Sharda et al., 2018). In addition,
auditory and visual regions, which are typically overly connected in
autistic children, displayed decreased functional connectivity after the
music intervention. This study thus highlights that participating in JMM
induces changes in functional connectivity of brain networks of autistic
children.
The role of hormonal processes in the prosocial consequences of
interpersonal synchronization tasks has been tested using neuroendo-
crinology. A number of studies have demonstrated that engaging in
musical actions, such as group singing, can trigger endocrine activity
that could facilitate social bonding (Bowling et al., 2022). For example,
preliminary evidence suggested that endorphins are released after per-
forming synchronized group musical activities, such as singing, dancing,
and drumming (Dunbar et al., 2012; Tarr et al., 2014). Because endor-
phins are normally naturally produced to cope with pain, these results
might suggest that active engagement in musical activities can have a
positive impact on participants’ wellbeing. In another study, dancing to
music (specically tango) with a partner was found to provoke positive
effects on emotional states by reducing stress, which was marked by a
decrease in salivary cortisol concentrations (Murcia et al., 2009). There
is also growing interest in the role of the nonapeptide oxytocin in JMM.
Grape et al. (2002) observed that social interaction through music leads
to a release of oxytocin, a hormone that is often associated with proso-
cial behaviors such as infant-caregiver bonding, group cohesion, or
courtship among others (see also Quintana and Guastella 2020). Along a
similar line, oxytocin has also been reported to positively inuence
synchronization in leader-follower interactions during a nger tapping
task (Gebauer et al., 2016). The broader literature on links between
oxytocin and JMM contains mixed ndings that can be accommodated
by the proposal that oxytocin functions as a socio-allostatic agent that
promotes exibility in brain states and behavioral responses to changing
conditions when interacting with others (Hansen and Keller, 2021).
Together, the above studies provide preliminary, and at present
somewhat scattered ndings about potential neural structures (the
caudate), physiological processes (functional connectivity) and endo-
crinological effects (increases of endorphins and oxytocin, decreases of
cortisol) that might mediate the prosocial effects of JMM. This is an area
where a large-scale, theory-driven program of research would be
particularly welcome, given the potential implications for the use of
JMM in health interventions in neurological and neurodevelopmental
populations.
5.3. Computational models
Research integrating computational models and social factors in
interpersonal coordination tasks, such as JMM or nger tapping, are
scarce and generally conceptual in nature (Clayton et al., 2020; Tzanaki,
2022). However, the models reviewed in the previous sections are viable
candidates to be applied to this area of research. Specically, the
manipulation of particular parameter settings, such as modulation of the
priors in previously reviewed predictive coding models or of the ‘self’
internal models in ADAM, would likely allow future research to model
the social factors inuencing JMM as well as the prosocial outcomes of
JMM.
Guidance for such an endeavor might be provided by work such as
the theoretical framework developed by Tzanaki (2022), which pro-
poses the existence of a positive feedback loop between empathy and
interpersonal coordination, as illustrated in Fig. 5C. This feedback loop
entails two aspects of empathy – trait empathy and situational empathy
S.F. Abalde et al.
Neuroscience and Biobehavioral Reviews 167 (2024) 105816
17
– each being related to interpersonal synchronization and reciprocally
enhancing one another. Trait empathy might increase through
long-term musical interactions, and simultaneously, individuals with
higher empathy traits also exhibit enhanced predictive skills. Finer
predictions, in turn, positively impact interpersonal rhythmic coordi-
nation. Relatedly, situational empathy, i.e., situation-specic respon-
siveness, can be modulated by short-term musical interactions, and
might enhance afliative effects, such as facilitating the development of
social and emotional bonds with others. Although this framework is
focused on empathy, it could potentially be generalized to other social
factors and personality dimensions (e.g., locus of control).
5.4. Interim conclusions on social factors
Behavioral research has shown that social personality traits (e.g.
empathic perspective taking) inuence interpersonal coordination dur-
ing JMM. Conversely, achieving interpersonal coordination can boost
prosocial behavior, notably across distinct developmental (infants,
children, adults) and clinical (individuals with ASD) contexts. This
bidirectional relationship between prosocial behavior (or traits) and
JMM is also grounded in neuroscientic evidence showing that social
traits are informative about the neural mechanisms individuals rely on
to achieve interpersonal coordination (e.g. more empathic participants
rely more on action simulation). In turn, JMM has direct effects on ac-
tivity of reward-related neural structures, in addition to endocrinolog-
ical mechanisms that can facilitate social bonding. To date, no
computational models have been specically formulated to address the
relationship between social factors and coordination in JMM, yet a
number of models described above could be adapted to address this in
the future.
6. Future perspectives and limitations
We have reviewed research on JMM from behavioral, neuroscien-
tic, and computational perspectives. We have organized a large
collection of studies within a framework, comprised of a central
component of JMM - interpersonal coordination – and three modulators
of this component: individuals’ (past) experience, (current) social fac-
tors, and (future) goals. This organization is intended to help foster the
consolidation of this eld of research, and we conclude by highlighting a
few important challenges this eld might face in the future.
First, as research in JMM is still in its early stages, an important
challenge this eld should address is replication (“Go forth and repli-
cate!,” 2016; Ioannidis, 2005; Korbmacher et al., 2023; Open Science,
2015; Tincani and Travers, 2019). Throughout our work, we have listed
several different tasks and very few replications, both within and be-
tween laboratories. The eld should aim to develop standard paradigms
that could become platforms where scientists can easily confront their
ndings. This practice could be encouraged by developing multi-lab
research projects. The same challenge, and proposed solution, applies
to the modelling work, where we have listed several families of models,
but few attempts at integration or direct comparisons, especially be-
tween linear and non-linear models.
Next, while our framework was built around the interpersonal co-
ordination of body movement, we highlight that this phenomenon is not
always studied consistently across studies. One issue that deserves
attention is the (often lacking) differentiation between instrumental and
ancillary movements (see Bishop et al., 2019b; Goebl and Palmer, 2009;
Hilt et al., 2019; Keller and Appel, 2010; Laroche et al., 2022). As dis-
cussed above (Section 4.1.1), these types of movements have different
signicance, and are presumably supported by distinct mechanisms. Yet,
studies often address interpersonal coordination while considering one
and ignoring the other, or by taking measures that ambiguously combine
the two together. For future research, we believe this differentiation
might be important and informative.
Another issue relates to how interpersonal coordination is measured.
Measures of “synchrony” rely on a wide range of heterogeneous for-
mulas. Some of these approaches capture pure phase synchronization (as
in-phase vs. anti-phase synchrony), while others (e.g., phase-locking
value) capture period synchronization (where perfect in-phase and
anti-phase synchrony yield identical values). There is yet another class
of measures that address temporal dependencies (such as Granger cau-
sality or recurrence quantication analysis) while not necessarily
capturing synchrony. We propose that studies should clearly describe
what measures of coordination are examined and, when possible,
compare multiple approaches (or deposit data in repositories to allow
others to do so).
Group numerosity is another feature of JMM that warrants consid-
eration. The majority of the studies reviewed above analyzed either
individuals acting in a social context, or dyads. However, univariate vs.
bivariate dependent measures, i.e., measures that are affected by either
one or two individuals, can sometimes shed light on different or com-
plementary phenomena (c.f. Novembre et al., 2016 vs. Gugnowska et al.,
2022). In a similar vein, recent work suggests that observations made
while examining dyadic behavior might not always generalize to larger
groups (Dotov et al., 2022; Demos and Palmer, 2023b, 2023a; Keller,
2023). Together, these considerations suggest that caution should be
exerted when comparing different studies, and that group numerosity
can impact how JMM is achieved, potentially at all (behavioral, neural,
and computational) levels.
Our framework has addressed JMM focusing on interpersonal coor-
dination, which is a dependent variable that can be objectively quanti-
ed, at least when it refers to the attempt to minimize temporal
asynchronies. Even though we assume that musicians routinely aim to
achieve high coordination accuracy, situations arise where musicians
intentionally reduce synchrony or add temporal variability (i.e. increase
self-other segregation). For instance, during joint improvisation, intro-
ducing small asynchronies might fulll expressive goals (D’Ausilio et al.,
2012) and even trigger higher preference in listeners (Hofmann et al.,
2017). Additionally, studies investigating improvised JMM have dened
coordination as “cooperation” or “sharing intentions” between
co-performers. Some of these studies have suggested that when such
coordination is reduced, other cognitive processes might emerge or
become stronger, including creativity (Kempf et al., 2024; Wolf et al.,
2023; see also Golvet et al., 2024), or subjective experiences such as the
sense of agency (Schiavio and Jaegher, 2017; Loehr, 2022; Gesbert et al.,
2022). These phenomena are intriguing aspects of JMM that deserve to
be studied more closely in the future.
Lastly, JMM is primarily examined in trained musicians. However,
the timely discussion of universal social functions of music (Mehr et al.,
2021; Savage et al., 2021), besides a few studies reviewed above, sug-
gests that the capacity for JMM might also rely on human pre-
dispositions for social coordination and musicality (Malloch, 1999;
Malloch and Trevarthen, 2009, 2018; Trevarthen, 1999; van der Schyff
et al., 2022; van der Schyff and Schiavio, 2017). In the future, this could
be tested using accessible JMM tasks (relying on e.g. singing, or on
mechanical instruments such as the e-music box, or on accessible tasks
such as tapping to play melodic sequences (Weiss and Peretz, 2022;
Novembre et al., 2015; Zamm et al., 2023; Coorevits et al., 2020; Nicol
et al., 2024). Addressing this issue thoroughly and strategically might be
important to make JMM activities more accessible not only to
non-musicians, but also to individuals belonging to different cultures or
neurodivergent individuals. Also, addressing this issue would permit
researchers to distinguish mechanisms that depend on musical experi-
ence (or enculturation) from those that are general-purpose and might
have evolved for other reasons. Identifying the former processes would
illuminate the uniquity of music, while the latter would further suggest
that JMM is a valid and powerful model to study social interaction in its
broadest form.
S.F. Abalde et al.
Neuroscience and Biobehavioral Reviews 167 (2024) 105816
18
Acknowledgments
We thank Trinh Nguyen, Roberta Bianco, Flavia Arnese and Felix
Bigand for their comments on an earlier version of this manuscript. This
work was supported by the European Research Council (ERC StG
MUSICOM grant number 948186), Australian Research Council
(FT140101162) and the Danish National Research Foundation
(DNRF117). The Open University Afliated Research Centre at Istituto
Italiano di Tecnologia (ARC@IIT) is part of the Open University, Milton
Keynes MK7 6AA, United Kingdom.
References
“Go forth and replicate!”, 2016. Nature 536, 373. https://doi.org/10.1038/536373a.
Anshel, A., Kipper, D.A., 1988. The inuence of group singing on trust and cooperation.
J. Music Ther. 25 (3), 145–155. https://doi.org/10.1093/jmt/25.3.145.
Babiloni, C., Buffo, P., Vecchio, F., Marzano, N., Del Percio, C., Spada, D., Rossi, S.,
Bruni, I., Rossini, P.M., Perani, D., 2012. Brains in concert : frontal oscillatory alpha
rhythms and empathy in professional musicians. NeuroImage 60 (1), 105–116.
https://doi.org/10.1016/j.neuroimage.2011.12.008.
Babiloni, F., Cincotti, F., Mattia, D., De Vico Fallani, F., Tocci, A., Bianchi, L., Salinari, S.,
Marciani, M.G., Colosimo, A., Astol, L., 2007. High resolution EEG hyperscanning
during a card game. 2007 29th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc.
4957–4960. https://doi.org/10.1109/IEMBS.2007.4353453.
Badino, L., D’Ausilio, A., Glowinski, D., Camurri, A., Fadiga, L., 2014. Sensorimotor
communication in professional quartets. Neuropsychologia 55, 98–104. https://doi.
org/10.1016/j.neuropsychologia.2013.11.012.
Bailey, D. (1993). Improvisation: Its nature and practice in music.
B´
egel, V., Demos, A.P., Wang, M., Palmer, C., 2022. Social interaction and rate effects in
models of musical synchronization. Front. Psychol. 13 https://doi.org/10.3389/
fpsyg.2022.865536.
Behrends, A., Müller, S., Dziobek, I., 2012. Moving in and out of synchrony: a concept for
a new intervention fostering empathy through interactional movement and dance.
Arts Psychother. 39 (2), 107–116. https://doi.org/10.1016/j.aip.2012.02.003.
Bensimon, M., 2021. Explaining post-prison rehabilitation through music from the good
lives model perspective. Eur. J. Criminol. https://doi.org/10.1177/
1477370821997339.
Bensimon, M., Gilboa, A., 2010. The music of my life: the impact of the musical
presentation on the sense of purpose in life and on self-consciousness. Arts
Psychother. 37 (3), 172–178. https://doi.org/10.1016/j.aip.2010.03.002.
Bishop, L., Cancino-Chac´
on, C., Goebl, W., 2019a. Eye gaze as a means of giving and
seeking information during musical interaction. Conscious. Cogn. 68, 73–96.
https://doi.org/10.1016/j.concog.2019.01.002.
Bishop, L., Cancino-Chac´
on, C., Goebl, W., 2019b. Moving to communicate, moving to
interact: patterns of body motion in musical duo performance. Music Percept. 37 (1),
1–25. https://doi.org/10.1525/mp.2019.37.1.1.
Bishop, L., Goebl, W., 2015. When they listen and when they watch: pianists’ use of
nonverbal audio and visual cues during duet performance. Musica Sci. 19 (1),
84–110. https://doi.org/10.1177/1029864915570355.
Bishop, L., Goebl, W., 2018a. Beating time: how ensemble musicians’ cueing gestures
communicate beat position and tempo. Psychol. Music 46 (1), 84–106. https://doi.
org/10.1177/0305735617702971.
Bishop, L., Goebl, W., 2018b. Communication for coordination: gesture kinematics and
conventionality affect synchronization success in piano duos. Psychol. Res. 82 (6),
1177–1194. https://doi.org/10.1007/s00426-017-0893-3.
Bishop, L., Goebl, W., 2020. Negotiating a shared interpretation during piano duo
performance. Music Sci. 3 https://doi.org/10.1177/2059204319896152.
Bishop, L., Gonz´
alez S´
anchez, V., Laeng, B., Jensenius, A.R., Høffding, S., 2021. Move
like everyone is watching: social context affects head motion and gaze in string
quartet performance. J. N. Music Res. 50 (4), 392–412. https://doi.org/10.1080/
09298215.2021.1977338.
Bishop, L., Høffding, S., Lartillot, O., Laeng, B., 2023. Mental effort and expressive
interaction in expert and student string quartet performance. Music Sci. 6 https://
doi.org/10.1177/20592043231208000.
Bowling, D.L., Gahr, J., Ancochea, P.G., Hoeschele, M., Canoine, V., Fusani, L., Fitch, W.
T., 2022. Endogenous oxytocin, cortisol, and testosterone in response to group
singing. Horm. Behav. 139 https://doi.org/10.1016/j.yhbeh.2021.105105.
Candidi, M., Curioni, A., Donnarumma, F., Sacheli, L.M., Pezzulo, G., 2015. Interactional
leader-follower sensorimotor communication strategies during repetitive joint
actions. J. R. Soc., Interface 12 (110), 0644. https://doi.org/10.1098/
rsif.2015.0644.
Canonne, C., 2013. Focal points in collective free improvisation. Perspect. N. Music 51
(1), 40–55. https://doi.org/10.7757/persnewmusi.51.1.0040.
Canonne, C., Aucouturier, J.-J., 2016. Play together, think alike: shared mental models in
expert music improvisers. Psychol. Music 44 (3), 544–558. https://doi.org/10.1177/
0305735615577406.
Canonne, C., Garnier, N., 2011. A model for collective free improvisation. In: Agon, C.,
Andreatta, M., Assayag, G., Amiot, E., Bresson, J., Mandereau, J. (Eds.), Mathematics
and Computation in Music. Springer, pp. 29–41. https://doi.org/10.1007/978-3-
642-21590-2_3.
Canonne, C., & Garnier, N., 2012. Cognition and segmentation in collective free
improvisation: An exploratory study. In Proceedings of the 12th International
Conference on Music Perception and Cognition and 8th Triennial Conference of the
European Society for the Cognitive Sciences of Music (pp. 197-204). doi: 10.13140/
2.1.3534.9445.
Carlson, E., Burger, B., Toiviainen, P., 2018. Dance like someone is watching: a social
relations model study of music-induced movement, 2059204318807846 Music Sci.
1. https://doi.org/10.1177/2059204318807846.
Carlson, E., Burger, B., Toiviainen, P., 2019. Empathy, entrainment, and perceived
interaction in complex dyadic dance movement. Music Percept. 36 (4), 390–405.
https://doi.org/10.1525/mp.2019.36.4.390.
Carlson, E., Burger, B., London, J., Thompson, M.R., Toiviainen, P., 2016.
Conscientiousness and extraversion relate to responsiveness to tempo in dance. Hum.
Mov. Sci. 49, 315–325. https://doi.org/10.1016/j.humov.2016.08.006.
Castellano, G., Mortillaro, M., Camurri, A., Volpe, G., Scherer, K., 2008. Automated
analysis of body movement in emotionally expressive piano performances. Music
Percept. 26 (2), 103–119. https://doi.org/10.1525/mp.2008.26.2.103.
Cervone, D., 2021. Chapter 22—The KAPA model of personality structure and dynamics.
In: Rauthmann, J.F. (Ed.), The Handbook of Personality Dynamics and Processes.
Academic Press, pp. 601–620. https://doi.org/10.1016/B978-0-12-813995-
0.00022-4.
Chang, A., Kragness, H.E., Livingstone, S.R., Bosnyak, D.J., Trainor, L.J., 2019. Body
sway reects joint emotional expression in music ensemble performance. Article 1.
Sci. Rep. 9 (1) https://doi.org/10.1038/s41598-018-36358-4.
Chang, A., Livingstone, S.R., Bosnyak, D.J., Trainor, L.J., 2017. Body sway reects
leadership in joint music performance. Proc. Natl. Acad. Sci. USA 114 (21),
E4134–E4141. https://doi.org/10.1073/pnas.1617657114.
Chauvign´
e, L.A.S., Belyk, M., Brown, S., 2018. Taking two to tango: fMRI analysis of
improvised joint action with physical contact. PLoS One 13, e0191098. https://doi.
org/10.1371/journal.pone.0191098.
Christensen, J., Slavik, L., Nicol, J.J., Loehr, J.D., 2022. Alpha oscillations related to self-
other integration and distinction during live orchestral performance: a naturalistic
case study, 03057356221091313 Psychol. Music. https://doi.org/10.1177/
03057356221091313.
Cirelli, L.K., Einarson, K.M., Trainor, L.J., 2014. Interpersonal synchrony increases
prosocial behavior in infants. Dev. Sci. 17 (6), 1003–1011. https://doi.org/10.1111/
desc.12193.
Clayton, M., Jakubowski, K., Eerola, T., Keller, P.E., Camurri, A., Volpe, G., Alborno, P.,
2020. Interpersonal entrainment in music performance: theory, method, and model.
Music Percept. 38 (2), 136–194. https://doi.org/10.1525/mp.2020.38.2.136.
Clements, A., 2018. Joint music making and prosocial behavior in infants and young
children: a review of literature. Perspect.: J. Early Child. Music Mov. Assoc. 13
https://doi.org/10.1386/IJMEC_0361_1.
Coey, C., Varlet, M., Schmidt, R.C., Richardson, M.J., 2011. Effects of movement stability
and congruency on the emergence of spontaneous interpersonal coordination. Exp.
Brain Res. 211 (3–4), 483–493. https://doi.org/10.1007/s00221-011-2689-9.
Colley, I.D., Varlet, M., MacRitchie, J., Keller, P.E., 2018. The inuence of visual cues on
temporal anticipation and movement synchronization with musical sequences. Acta
Psychol. 191, 190–200. https://doi.org/10.1016/j.actpsy.2018.09.014.
Colley, I.D., Varlet, M., MacRitchie, J., Keller, P.E., 2020. The inuence of a conductor
and co-performer on auditory-motor synchronisation, temporal prediction, and
ancillary entrainment in a musical drumming task. Hum. Mov. Sci. 72, 102653
https://doi.org/10.1016/j.humov.2020.102653.
Coorevits, E., Maes, P.-J., Six, J., Leman, M., 2020. The inuence of performing gesture
type on interpersonal musical timing, and the role of visual contact and tempo. Acta
Psychol. 210, 103166 https://doi.org/10.1016/j.actpsy.2020.103166.
D’Amario, S., Daffern, H., Bailes, F., 2018. Synchronization in singing duo performances:
the roles of visual contact and leadership instruction. Front. Psychol. 9 https://doi.
org/10.3389/fpsyg.2018.01208.
D’Amario, S., Howard, D.M., Daffern, H., Pennill, N., 2020. A longitudinal Study of
Intonation in an a cappella singing quintet. J. Voice 34 (1), 159.e13–159.e27.
https://doi.org/10.1016/j.jvoice.2018.07.015.
D’Amario, S., Schmidbauer, H., Roesch, A., Goebl, W., Niemand, A.M., Bishop, L., 2023.
Interperformer coordination in piano-singing duo performances: phrase structure
and empathy impact. Psychol. Res. https://doi.org/10.1007/s00426-023-01818-8.
D’Ausilio, A., Altenmüller, E., Olivetti Belardinelli, M., Lotze, M., 2006. Cross-modal
plasticity of the motor cortex while listening to a rehearsed musical piece. Eur. J.
Neurosci. 24 (3), 955–958. https://doi.org/10.1111/j.1460-9568.2006.04960.x.
D’Ausilio, A., Badino, L., Li, Y., Tokay, S., Craighero, L., Canto, R., Aloimonos, Y.,
Fadiga, L., 2012. Leadership in orchestra emerges from the causal relationships of
movement kinematics. PLOS ONE 7 (5), e35757. https://doi.org/10.1371/journal.
pone.0035757.
D’Ausilio, A., Novembre, G., Fadiga, L., Keller, P.E., 2015. What can music tell us about
social interaction? Trends Cogn. Sci. 19 (3), 111–114. https://doi.org/10.1016/j.
tics.2015.01.005.
Daffern, H., 2017. Blend in singing ensemble performance: vibrato production in a vocal
quartet. J. Voice 31 (3), 385.e23–385.e29. https://doi.org/10.1016/j.
jvoice.2016.09.007.
Davidson, J.W., 2012. Bodily movement and facial actions in expressive musical
performance by solo and duo instrumentalists: two distinctive case studies. Psychol.
Music 40 (5), 595–633. https://doi.org/10.1177/0305735612449896.
Davidson, J.W., Broughton, M.C., 2016. Bodily mediated coordination, collaboration,
and communication in music performance. In: Hallam, S., Cross, I., Thaut, M.H.
(Eds.), The Oxford Handbook of Music Psychology. Oxford University Press. https://
doi.org/10.1093/oxfordhb/9780198722946.013.35.
Davidson, J.W., Good, J.M.M., 2002. Social and musical co-ordination between members
of a string quartet: an exploratory study. Psychol. Music 30 (2), 186–201. https://
doi.org/10.1177/0305735602302005.
S.F. Abalde et al.
Neuroscience and Biobehavioral Reviews 167 (2024) 105816
19
Davis, M., 1980. A Multidimensional Approach to Individual Differences in Empathy.
JSAS Catalog Sel. Doc. Psychol. 10, 85.
Davis, M., 1983. Measuring individual differences in empathy: evidence for a
multidimensional approach. J. Personal. Soc. Psychol. 44 (1), 113–126. https://doi.
org/10.1037/0022-3514.44.1.113.
Delius, J.A.M., Müller, V., 2023. Interpersonal synchrony when singing in a choir. Front.
Psychol. 13 https://doi.org/10.3389/fpsyg.2022.1087517.
Dell’Anna, A., Leman, M., Berti, A., 2021. Musical interaction reveals music as embodied
language. Front. Neurosci. 15, 667838 https://doi.org/10.3389/fnins.2021.667838.
Demos, A.P., Carter, D.J., Wanderley, M.M., Palmer, C., 2017. The unresponsive partner:
roles of social status, auditory feedback, and animacy in coordination of joint music
Performance. Front. Psychol. 8 https://doi.org/10.3389/fpsyg.2017.00149.
Demos, A.P., Layeghi, H., Wanderley, M.M., Palmer, C., 2019a. Staying together: a
bidirectional delay–coupled approach to joint action. Cogn. Sci. 43 (8), e12766
https://doi.org/10.1111/cogs.12766.
Demos, A.P., Palmer, C., 2023a. Musical synchrony, dynamical systems and information
processing: merger or redundancy? Trends Cogn. Sci. 0 (0) https://doi.org/10.1016/
j.tics.2023.08.015.
Demos, A.P., Palmer, C., 2023b. Social and nonlinear dynamics unite: musical group
synchrony. Trends Cogn. Sci. https://doi.org/10.1016/j.tics.2023.05.005.
Digman, J.M., 1990. Personality structure: emergence of the ve-factor model. Annu.
Rev. Psychol. 41 (1), 417–440. https://doi.org/10.1146/annurev.
ps.41.020190.002221.
Dotov, D., Delasanta, L., Cameron, D.J., Large, E.W., Trainor, L., 2022. Collective
dynamics support group drumming, reduce variability, and stabilize tempo drift.
eLife 11, e74816. https://doi.org/10.7554/eLife.74816.
Dumas, G., Martinerie, J., Soussignan, R., Nadel, J., 2012. Does the brain know who is at
the origin of what in an imitative interaction? Front. Hum. Neurosci. 6 https://doi.
org/10.3389/fnhum.2012.00128.
Dunbar, R.I.M., Kaskatis, K., MacDonald, I., Barra, V., 2012. Performance of music
elevates pain threshold and positive affect: implications for the evolutionary
function of music, 147470491201000403 Evolut. Psychol. 10 (4). https://doi.org/
10.1177/147470491201000403.
Eerola, T., Jakubowski, K., Moran, N., Keller, P.E., Clayton, M., 2018. Shared periodic
performer movements coordinate interactions in duo improvisations. R. Soc. Open
Sci. 5 (2), 171520 https://doi.org/10.1098/rsos.171520.
Elliott, M.T., Wing, A.M., Welchman, A.E., 2010. Multisensory cues improve
sensorimotor synchronisation. Eur. J. Neurosci. 31 (10), 1828–1835. https://doi.
org/10.1111/j.1460-9568.2010.07205.x.
Fairhurst, M.T., Janata, P., Keller, P.E., 2013. Being and feeling In sync with an adaptive
virtual partner: brain mechanisms underlying dynamic cooperativity. Cereb. Cortex
23 (11), 2592–2600. https://doi.org/10.1093/cercor/bhs243.
Fairhurst, M.T., Janata, P., Keller, P.E., 2014. Leading the follower: an fmri investigation
of dynamic cooperativity and leader–follower strategies in synchronization with an
adaptive virtual partner. NeuroImage 84, 688–697. https://doi.org/10.1016/j.
neuroimage.2013.09.027.
Friston, K., Kiebel, S., 2009. Predictive coding under the free-energy principle. Philos.
Trans. R. Soc. B: Biol. Sci. 364 (1521), 1211–1221. https://doi.org/10.1098/
rstb.2008.0300.
Froese, T., Loh, C.L., Putri, F., 2024. Inter-brain desynchronization in social interaction: a
consequence of subjective involvement? Front. Hum. Neurosci. 18 https://doi.org/
10.3389/fnhum.2024.1359841.
Fusar-Poli, L., Thompson, G., Lense, M.D., Gold, C., 2022. Music therapy and other
music-based interventions. In: Matson, J.L., Sturmey, P. (Eds.), Assessment, Diagnosis,
and Treatment (pp, Handbook of Autism and Pervasive Developmental Disorder. Springer
International Publishing, pp. 1409–1433. https://doi.org/10.1007/978-3-030-
88538-0_63.
Gallese, V., 2003. The roots of empathy: the shared manifold hypothesis and the neural
basis of intersubjectivity. Psychopathology 36 (4), 171–180. https://doi.org/
10.1159/000072786.
Gazzola, V., Aziz-Zadeh, L., Keysers, C., 2006. Empathy and the somatotopic auditory
mirror system in humans. Curr. Biol. 16 (18), 1824–1829. https://doi.org/10.1016/
j.cub.2006.07.072.
Gebauer, L., Witek, M. a G., Hansen, N.C., Thomas, J., Konvalinka, I., Vuust, P., 2016.
Oxytocin improves synchronisation in leader-follower interaction. Article 1. Sci.
Rep. 6 (1) https://doi.org/10.1038/srep38416.
Gesbert, V., Hauw, D., Kempf, A., Blauth, A., Schiavio, A., 2022. Creative togetherness. a
joint-methods analysis of collaborative artistic performance. Front. Psychol. 13
https://doi.org/10.3389/fpsyg.2022.835340.
Ginsborg, J., Chafn, R., Nicholson, G., 2006. Shared performance cues in singing and
conducting: a content analysis of talk during practice. Psychol. Music 34 (2),
167–194. https://doi.org/10.1177/0305735606061851.
Glowinski, D., Mancini, M., Cowie, R., Camurri, A., Chiorri, C., Doherty, C., 2013. The
movements made by performers in a skilled quartet: a distinctive pattern, and the
function that it serves. Front. Psychol. 4 https://doi.org/10.3389/fpsyg.2013.00841.
Goebl, W., Palmer, C., 2009. Synchronization of timing and motion among performing
musicians. Music Percept. 26 (5), 427–438. https://doi.org/10.1525/
mp.2009.26.5.427.
Golvet, A., Goupil, L., Saint-Germier, P., Matuszewski, B., Assayag, G., Nika, J.,
Canonne, C., 2024. With, against, or without? Familiarity and copresence increase
interactional dissensus and relational plasticity in freely improvising duos. Psychol.
Aesthet., Creat., Arts 18 (2), 182–195. https://doi.org/10.1037/aca0000422.
Gordon, I., Gilboa, A., Cohen, S., Milstein, N., Haimovich, N., Pinhasi, S., Siegman, S.,
2020. Physiological and behavioral synchrony predict group cohesion and
performance. Article 1. Sci. Rep. 10 (1) https://doi.org/10.1038/s41598-020-
65670-1.
Goupil, L., Saint-Germier, P., Rouvier, G., Schwarz, D., Canonne, C., 2020. Musical
coordination in a large group without plans nor leaders. Article 1. Sci. Rep. 10 (1)
https://doi.org/10.1038/s41598-020-77263-z.
Goupil, L., Wolf, T., Saint-Germier, P., Aucouturier, J.-J., Canonne, C., 2021. Emergent
shared intentions support coordination during collective musical improvisations.
Cogn. Sci. 45 (1), e12932 https://doi.org/10.1111/cogs.12932.
Grape, C., Sandgren, M., Hansson, L.-O., Ericson, M., Theorell, T., 2002. Does singing
promote well-being?: An empirical study of professional and amateur singers during
a singing lesson. Integr. Physiol. Behav. Sci. 38 (1), 65–74. https://doi.org/10.1007/
BF02734261.
Gugnowska, K., Novembre, G., Kohler, N., Villringer, A., Keller, P.E., Sammler, D., 2022.
Endogenous sources of interbrain synchrony in duetting pianists. Cereb. Cortex
bhab469. https://doi.org/10.1093/cercor/bhab469.
Hadley, L.V., Novembre, G., Keller, P.E., Pickering, M.J., 2015. Causal role of motor
simulation in turn-taking behavior. J. Neurosci. 35 (50), 16516–16520. https://doi.
org/10.1523/JNEUROSCI.1850-15.2015.
Hansen, N.C., Keller, P.E., 2021. Oxytocin as an allostatic agent in the social bonding
effects of music. Behav. Brain Sci. 44, e75 https://doi.org/10.1017/
S0140525X20001235.
Happ´
e, F., Frith, U., 2020. Annual research review: looking back to look forward –
changes in the concept of autism and implications for future research. J. Child
Psychol. Psychiatry 61 (3), 218–232. https://doi.org/10.1111/jcpp.13176.
Harry, B.B., Keller, P.E., 2019. Tutorial and simulations with ADAM: an adaptation and
anticipation model of sensorimotor synchronization. Biol. Cybern. 113 (4), 397–421.
https://doi.org/10.1007/s00422-019-00798-6.
Harry, B.B., Margulies, D.S., Falkiewicz, M., Keller, P.E., 2023. Brain networks for
temporal adaptation, anticipation, and sensory-motor integration in rhythmic
human behavior. Neuropsychologia 183, 108524. https://doi.org/10.1016/j.
neuropsychologia.2023.108524.
Heggli, O.A., Cabral, J., Konvalinka, I., Vuust, P., Kringelbach, M.L., 2019. A Kuramoto
model of self-other integration across interpersonal synchronization strategies. PLOS
Comput. Biol. 15 (10), e1007422 https://doi.org/10.1371/journal.pcbi.1007422.
Heggli, O.A., Konvalinka, I., Cabral, J., Brattico, E., Kringelbach, M.L., Vuust, P., 2021.
Transient brain networks underlying interpersonal strategies during synchronized
action. Soc. Cogn. Affect. Neurosci. 16 (1–2), 19–30. https://doi.org/10.1093/scan/
nsaa056.
Heggli, O.A., Konvalinka, I., Kringelbach, M.L., Vuust, P., 2019. Musical interaction is
inuenced by underlying predictive models and musical expertise. Article 1. Sci.
Rep. 9 (1) https://doi.org/10.1038/s41598-019-47471-3.
Heggli, O.A., Konvalinka, I., Kringelbach, M.L., Vuust, P., 2021. A metastable attractor
model of self–other integration (MEAMSO) in rhythmic synchronization. Philos.
Trans. R. Soc. B: Biol. Sci. 376 (1835), 20200332 https://doi.org/10.1098/
rstb.2020.0332.
Helmholtz, H. von, 1867. Handbuch der physiologischen Optik. Voss.
Hilt, P.M., Badino, L., D’Ausilio, A., Volpe, G., Tokay, S., Fadiga, L., Camurri, A., 2019.
Multi-layer adaptation of group coordination in musical ensembles. Article 1. Sci.
Rep. 9 (1) https://doi.org/10.1038/s41598-019-42395-4.
Hofmann, A., Wesolowski, B.C., Goebl, W., 2017. The tight-interlocked rhythm section:
production and perception of synchronisation in jazz trio performance. J. N. Music
Res. 46 (4), 329–341. https://doi.org/10.1080/09298215.2017.1355394.
Hove, M.J., Risen, J.L., 2009. It’s all in the timing: interpersonal synchrony increases
afliation. Soc. Cogn. 27 (6), 949–960. https://doi.org/10.1521/
soco.2009.27.6.949.
Ioannidis, J.P.A., 2005. Why most published research ndings are false. PLOS Med. 2 (8),
e124 https://doi.org/10.1371/journal.pmed.0020124.
Izen, S.C., Cassano-Coleman, R.Y., Piazza, E.A., 2023. Music as a window into real-world
communication. Front. Psychol. 14, 1012839 https://doi.org/10.3389/
fpsyg.2023.1012839.
Jeannerod, M., 2001. Neural simulation of action: a unifying mechanism for motor
cognition. NeuroImage 14 (1), S103–S109. https://doi.org/10.1006/
nimg.2001.0832.
Jensenius, A.R., Wanderley, M.M., Godøy, R.I., Leman, M., 2010. Musical Gestures:
concepts and methods in research. Musical Gestures: Sound, Movement, and
Meaning 12–35. https://doi.org/10.4324/9780203863411.
Jones, M.R., 1976. Time, our lost dimension: toward a new theory of perception,
attention, and memory. Psychol. Rev. 83 (5), 323–355. https://doi.org/10.1037/
0033-295X.83.5.323.
Jones, M.R., 2019. Time will tell: a theory of dynamic attending. Oxford University Press.
https://doi.org/10.1093/oso/9780190618216.001.0001.
Jones, M.R., Boltz, M., 1989. Dynamic attending and responses to time. Psychol. Rev. 96
(3), 459–491. https://doi.org/10.1037/0033-295X.96.3.459.
Kawase, S., 2014a. Assignment of leadership role changes performers’ gaze during piano
duo performances. Ecol. Psychol. 26 (3), 198–215. https://doi.org/10.1080/
10407413.2014.929477.
Kawase, S., 2014b. Gazing behavior and coordination during piano duo performance.
Atten., Percept., Psychophys. 76 (2), 527–540. https://doi.org/10.3758/s13414-
013-0568-0.
Kawase, S., 2016. Associations among music majors’ personality traits, empathy, and
aptitude for ensemble performance. Psychol. Music 44 (2), 293–302. https://doi.
org/10.1177/0305735614568697.
Keller, P.E., 1999. Attending in complex musical interactions: the adaptive dual role of
meter. Aust. J. Psychol. 51 (3), 166–175. https://doi.org/10.1080/
00049539908255354.
Keller, P.E., 2001. Attentional resource allocation in musical ensemble performance.
Psychol. Music 29 (1), 20–38. https://doi.org/10.1177/0305735601291003
resource allocation in musical ensemble performance.
S.F. Abalde et al.
Neuroscience and Biobehavioral Reviews 167 (2024) 105816
20
Keller, P.E., 2008. Joint action in music performance. In: Morganti, F., Carassa, A.,
Riva, G. (Eds.), Enacting intersubjectivity: A cognitive and social perspective on the
study of interactions. IOS Press, pp. 205–221.
Keller, P.E., 2012. Mental imagery in music performance: underlying mechanisms and
potential benets. Ann. N. Y. Acad. Sci. 1252 (1), 206–213. https://doi.org/
10.1111/j.1749-6632.2011.06439.x.
Keller, P.E., 2014. Ensemble performance: interpersonal alignment of musical
expression. In. Expressiveness in music performance. Oxford University Press.
https://doi.org/10.1093/acprof:oso/9780199659647.003.0015.
Keller, P.E., 2023. Integrating theory and models of musical group interaction. Trends
Cogn. Sci. 0 (0) https://doi.org/10.1016/j.tics.2023.07.008.
Keller, P.E., Appel, M., 2010. Individual differences, auditory imagery, and the
coordination of body movements and sounds in musical ensembles. Music Percept.
28 (1), 27–46. https://doi.org/10.1525/mp.2010.28.1.27.
Keller, P.E., Burnham, D.K., 2005. Musical meter in attention to multipart rhythm. Music
Percept. 22 (4), 629–661. https://doi.org/10.1525/mp.2005.22.4.629.
Keller, P.E., Knoblich, G., Repp, B.H., 2007. Pianists duet better when they play with
themselves: on the possible role of action simulation in synchronization. Conscious.
Cogn. 16 (1), 102–111. https://doi.org/10.1016/j.concog.2005.12.004.
Keller, P.E., K¨
onig, R., Novembre, G., 2017. Simultaneous cooperation and competition
in the evolution of musical behavior: sex-related modulations of the singer’s formant
in human chorusing. Front. Psychol. 8 https://doi.org/10.3389/fpsyg.2017.01559.
Keller, P.E., Novembre, G.N., Hove, M.J., 2014. Rhythm in joint action: psychological
and neurophysiological mechanisms for real-time interpersonal coordination. Philos.
Trans. R. Soc. Lond. Ser. B: Biol. Sci. https://doi.org/10.1098/rstb.2013.0394.
Keller, P.E., Novembre, G., Loehr, J., 2016. Musical ensemble performance: representing
self, other and joint action outcomes. In. In: Cross, E.S., Obhi, S.S. (Eds.), Shared
Representations: Sensorimotor Foundations of Social Life. Cambridge University
Press, pp. 280–310. https://doi.org/10.1017/CBO9781107279353.015.
Kelso, J.A.S., 1995. Dynamic patterns: The self-organization of brain and behavior. The
MIT Press.
Kelso, J.A.S., Dumas, G., Tognoli, E., 2013. Outline of a general theory of behavior and
brain coordination. Neural Netw.: Off. J. Int. Neural Netw. Soc. 37, 120–131.
https://doi.org/10.1016/j.neunet.2012.09.003.
Kelso, J.A.S., Tognoli, E., 2007. Toward a complementary neuroscience: metastable
coordination dynamics of the brain. In: Perlovsky, L.I., Kozma, R. (Eds.),
Neurodynamics of Cognition and Consciousness (pp. Springer, pp. 39–59. https://doi.
org/10.1007/978-3-540-73267-9_3.
Kempf, A., Benedek, M., Schiavio, A., 2024. An observation of a negative effect of social
cohesion on creativity in musical improvisation. Sci. Rep. 14 (1), 2922. https://doi.
org/10.1038/s41598-024-52350-7.
Kilner, J.M., Friston, K.J., Frith, C.D., 2007. Predictive coding: an account of the mirror
neuron system. Cogn. Process. 8 (3), 159–166. https://doi.org/10.1007/s10339-
007-0170-2.
Kim, J., Wigram, T., Gold, C., 2008. The effects of improvisational music therapy on joint
attention behaviors in autistic children: a randomized controlled study. J. Autism
Dev. Disord. 38 (9), 1758–1766. https://doi.org/10.1007/s10803-008-0566-6.
Kim, J., Wigram, T., Gold, C., 2009. Emotional, motivational and interpersonal
responsiveness of children with autism in improvisational music therapy. Autism 13
(4), 389–409. https://doi.org/10.1177/1362361309105660.
Kirschner, S., Tomasello, M., 2009. Joint drumming: social context facilitates
synchronization in preschool children. J. Exp. Child Psychol. 102 (3), 299–314.
https://doi.org/10.1016/j.jecp.2008.07.005.
Kirschner, S., Tomasello, M., 2010. Joint music making promotes prosocial behavior in 4-
year-old children. Evol. Hum. Behav. 31 (5), 354–364. https://doi.org/10.1016/j.
evolhumbehav.2010.04.004.
Koelsch, S., Vuust, P., Friston, K., 2019. Predictive processes and the peculiar case of
music. Trends Cogn. Sci. 23 (1), 63–77. https://doi.org/10.1016/j.tics.2018.10.006.
Kohler, N., Novembre, G., Gugnowska, K., Keller, P.E., Villringer, A., Sammler, D., 2022.
Cortico-cerebellar audio-motor regions coordinate self and other in musical joint
action. Cereb. Cortex bhac243. https://doi.org/10.1093/cercor/bhac243.
Kokal, I., Engel, A., Kirschner, S., Keysers, C., 2011. Synchronized drumming enhances
activity in the caudate and facilitates prosocial commitment—if the rhythm comes
easily. PLOS ONE 6 (11), e27272. https://doi.org/10.1371/journal.pone.0027272.
Konvalinka, I., Bauer, M., Stahlhut, C., Hansen, L.K., Roepstorff, A., Frith, C.D., 2014.
Frontal alpha oscillations distinguish leaders from followers: multivariate decoding
of mutually interacting brains. NeuroImage 94, 79–88. https://doi.org/10.1016/j.
neuroimage.2014.03.003.
Korbmacher, M., Azevedo, F., Pennington, C.R., Hartmann, H., Pownall, M., Schmidt, K.,
Elsherif, M., Breznau, N., Robertson, O., Kalandadze, T., Yu, S., Baker, B.J.,
O’Mahony, A., Olsnes, J.Ø.-S., Shaw, J.J., Gjoneska, B., Yamada, Y., R¨
oer, J.P.,
Murphy, J., Evans, T., 2023. The replication crisis has led to positive structural,
procedural, and community changes. Article 1 Commun. Psychol. 1 (1). https://doi.
org/10.1038/s44271-023-00003-2.
LaGasse, A.B., 2014. Effects of a music therapy group intervention on enhancing social
skills in children with autism. J. Music Ther. 51 (3), 250–275. https://doi.org/
10.1093/jmt/thu012.
Lanaro, L., Bobbio, A., Biasutti, M., Himonides, E., 2023. Five parameters for studying
leadership styles in orchestra conductors, 1321103X221149940 Res. Stud. Music
Educ.. https://doi.org/10.1177/1321103X221149940.
Lange, E.B., Omigie, D., Trenado, C., Müller, V., Wald-Fuhrmann, M., Merrill, J., 2022. In
touch: cardiac and respiratory patterns synchronize during ensemble singing with
physical contact. Front. Hum. Neurosci. 16, 928563 https://doi.org/10.3389/
fnhum.2022.928563.
Lappe, C., Bodeck, S., Lappe, M., Pantev, C., 2017. Shared neural mechanisms for the
prediction of own and partner musical sequences after short-term piano duet
training. Front. Neurosci. 11 https://doi.org/10.3389/fnins.2017.00165.
Large, E.W., Fink, P., Kelso, S.J., 2002. Tracking simple and complex sequences. Psychol.
Res. 66 (1), 3–17. https://doi.org/10.1007/s004260100069.
Large, E.W., Grondin, S., 2008. Resonating to musical rhythm: theory and experiment.
Psychol. Time 189–232. https://doi.org/10.1016/B978-0-08046-977-5.00006-5.
Large, E.W., Herrera, J.A., Velasco, M.J., 2015. Neural networks for beat perception in
musical rhythm. Front. Syst. Neurosci. 9 https://doi.org/10.3389/fnsys.2015.00159.
Large, E.W., Jones, M.R., 1999. The dynamics of attending: how people track time-
varying events. Psychol. Rev. 106 (1), 119–159. https://doi.org/10.1037/0033-
295X.106.1.119.
Large, E.W., Kolen, J.F., 1994. Resonance and the perception of musical meter. Connect.
Sci. 6 (2–3), 177–208. https://doi.org/10.1080/09540099408915723.
Large, E.W., Palmer, C., 2002. Perceiving temporal regularity in music. Cogn. Sci. 26 (1),
1–37. https://doi.org/10.1207/s15516709cog2601_1.
Laroche, J., Tomassini, A., Volpe, G., Camurri, A., Fadiga, L., D’Ausilio, A., 2022.
Interpersonal sensorimotor communication shapes intrapersonal coordination in a
musical ensemble. Front. Hum. Neurosci. 16 https://doi.org/10.3389/
fnhum.2022.899676.
Latif, N., Di Francesco, C., Custo-Blanch, M., Hyde, K., Sharda, M., Nadig, A., 2021. Joint
engagement and movement: active ingredients of a music-based intervention with
school-age children with autism. NeuroRehabilitation 48 (2), 167–185. https://doi.
org/10.3233/NRE-208012.
Launay, J., Dean, R.T., Bailes, F., 2013. Synchronization can inuence trust following
virtual interaction. Exp. Psychol. 60 (1), 53–63. https://doi.org/10.1027/1618-
3169/a000173.
Lega, C., Stephan, M.A., Zatorre, R.J., Penhune, V., 2016. Testing the role of dorsal
premotor cortex in auditory-motor association learning using transcranical magnetic
stimulation (TMS). PLoS ONE 11 (9), e0163380. https://doi.org/10.1371/journal.
pone.0163380.
Liebermann-Jordanidis, H., Novembre, G., Koch, I., Keller, P.E., 2021. Simultaneous self-
other integration and segregation support real-time interpersonal coordination in a
musical joint action task. Acta Psychol. 218, 103348 https://doi.org/10.1016/j.
actpsy.2021.103348.
Lindenberger, U., Li, S.-C., Gruber, W., Müller, V., 2009. Brains swinging in concert:
cortical phase synchronization while playing guitar. BMC Neurosci. 10 (1), 22.
https://doi.org/10.1186/1471-2202-10-22.
Loehr, J.D., 2022. The sense of agency in joint action: an integrative review. Psychon.
Bull. Rev. 29 (4), 1089–1117. https://doi.org/10.3758/s13423-021-02051-3.
Loehr, J.D., Kourtis, D., Vesper, C., Sebanz, N., Knoblich, G., 2013. Monitoring individual
and joint action outcomes in duet music performance. J. Cogn. Neurosci. 25 (7),
1049–1061. https://doi.org/10.1162/jocn_a_00388.
Loehr, J.D., Large, E.W., Palmer, C., 2011. Temporal coordination and adaptation to rate
change in music performance. J. Exp. Psychol.: Hum. Percept. Perform. 37 (4),
1292–1309. https://doi.org/10.1037/a0023102.
Loehr, J.D., Palmer, C., 2011. Temporal coordination between performing musicians.
Q. J. Exp. Psychol. 64 (11), 2153–2167. https://doi.org/10.1080/
17470218.2011.603427.
Loehr, J.D., Vesper, C., 2016. The sound of you and me: novices represent shared goals in
joint action. Q. J. Exp. Psychol. 69 (3), 535–547. https://doi.org/10.1080/
17470218.2015.1061029.
Lu, H., Wang, X., Zhang, Y., Huang, P., Xing, C., Zhang, M., Zhu, X., 2023. Increased
interbrain synchronization and neural efciency of the frontal cortex to enhance
human coordinative behavior: a combined hyper-tES and fNIRS study. NeuroImage
282, 120385. https://doi.org/10.1016/j.neuroimage.2023.120385.
Luck, S.J., 2014. An Introduction to the Event-Related Potential Technique, second
edition. MIT Press.
Luck, G., Sloboda, J., 2008. Exploring the spatio-temporal properties of simple
conducting gestures using a synchronization task. Music Percept. 25 (3), 225–239.
https://doi.org/10.1525/mp.2008.25.3.225.
Luck, G., Toiviainen, P., 2006. Ensemble musicians’ synchronization with conductors’
gestures: an automated feature-extraction analysis. Music Percept. 24 (2), 189–200.
https://doi.org/10.1525/mp.2006.24.2.189.
MacRitchie, J., Herff, S.A., Procopio, A., Keller, P.E., 2018. Negotiating between
individual and joint goals in ensemble musical performance. Q. J. Exp. Psychol. 71
(7), 1535–1551. https://doi.org/10.1080/17470218.2017.1339098.
Malloch, S., 1999. Mothers and infants and communicative musicality. Music Sci. 1 (3(1_
suppl)), 29–57.
Malloch, S., Trevarthen, C. (Eds.), 2009. Communicative Musicality: Exploring the Basis
of Human Companionship. Oxford University Pressdoi. https://doi.org/10.1093/
oso/9780198566281.001.0001.
Malloch, S., Trevarthen, C., 2018. The human nature of music. Front. Psychol. 9 https://
doi.org/10.3389/fpsyg.2018.01680.
Marsh, K.L., Richardson, M.J., Schmidt, R.C., 2009. Social connection through joint
action and interpersonal coordination. Top. Cogn. Sci. 1 (2), 320–339. https://doi.
org/10.1111/j.1756-8765.2009.01022.x.
Mates, J., 1994a. A model of synchronization of motor acts to a stimulus sequence. Biol.
Cybern. 70 (5), 463–473. https://doi.org/10.1007/BF00203239.
Mates, J., 1994b. A model of synchronization of motor acts to a stimulus sequence. Biol.
Cybern. 70 (5), 475–484. https://doi.org/10.1007/BF00203240.
McCrae, R.R., John, O.P., 1992. An introduction to the ve-factor model and its
applications. J. Personal. 60 (2), 175–215. https://doi.org/10.1111/j.1467-
6494.1992.tb00970.x.
McNeill, W.H., 1997. Keeping Together in Time: Dance and Drill in Human History. Harvard
University Press. https://doi.org/10.2307/j.ctvjf9wq6.
S.F. Abalde et al.
Neuroscience and Biobehavioral Reviews 167 (2024) 105816
21
Mehr, S.A., Krasnow, M.M., Bryant, G.A., Hagen, E.H., 2021. Origins of music in credible
signaling. Behav. Brain Sci. 44 https://doi.org/10.1017/S0140525X20000345.
Mehr, S.A., Singh, M., Knox, D., Ketter, D.M., Pickens-Jones, D., Atwood, S., Lucas, C.,
Jacoby, N., Egner, A.A., Hopkins, E.J., Howard, R.M., Hartshorne, J.K., Jennings, M.
V., Simson, J., Bainbridge, C.M., Pinker, S., O’Donnell, T.J., Krasnow, M.M.,
Glowacki, L., 2019. Universality and diversity in human song. Science 366 (6468).
https://doi.org/10.1126/science.aax0868.
Mendoza Garay, J.I., Burger, B., Luck, G., 2023. Exploring relations between big ve
personality traits and musical emotions embodied in spontaneous dance. Psychol.
Music 51 (4), 1314–1332. https://doi.org/10.1177/03057356221135355.
Mogan, R., Fischer, R., Bulbulia, J.A., 2017. To be in synchrony or not? A meta-analysis
of synchrony’s effects on behavior, perception, cognition and affect. J. Exp. Soc.
Psychol. 72, 13–20. https://doi.org/10.1016/j.jesp.2017.03.009.
Montague, P.R., Berns, G.S., Cohen, J.D., McClure, S.M., Pagnoni, G., Dhamala, M.,
Wiest, M.C., Karpov, I., King, R.D., Apple, N., Fisher, R.E., 2002. Hyperscanning:
simultaneous fmri during linked social interactions. NeuroImage 16 (4), 1159–1164.
https://doi.org/10.1006/nimg.2002.1150.
Müller, V., Lindenberger, U., 2019. Dynamic orchestration of brains and instruments
during free guitar improvisation. Front. Integr. Neurosci. 13 https://doi.org/
10.3389/fnint.2019.00050.
Müller, V., Lindenberger, U., 2023. Intra- and interbrain synchrony and hyperbrain
network dynamics of a guitarist quartet and its audience during a concert. Ann. N. Y.
Acad. Sci. https://doi.org/10.1111/nyas.14987.
Müller, V., Ohstr¨
om, K.-R.P., Lindenberger, U., 2021. Interactive brains, social minds:
neural and physiological mechanisms of interpersonal action coordination. Neurosci.
Biobehav. Rev. 128, 661–677. https://doi.org/10.1016/j.neubiorev.2021.07.017.
Müller, V., S¨
anger, J., Lindenberger, U., 2013. Intra- and inter-brain synchronization
during musical improvisation on the guitar. PLOS ONE 8 (9), e73852. https://doi.
org/10.1371/journal.pone.0073852.
Müller, V., S¨
anger, J., Lindenberger, U., 2018. Hyperbrain network properties of
guitarists playing in quartet. Ann. N. Y. Acad. Sci. 1423 (1), 198–210. https://doi.
org/10.1111/nyas.13656.
Munn, Z., Peters, M.D.J., Stern, C., Tufanaru, C., McArthur, A., Aromataris, E., 2018.
Systematic review or scoping review? Guidance for authors when choosing between
a systematic or scoping review approach. BMC Med. Res. Methodol. 18 (1), 143.
https://doi.org/10.1186/s12874-018-0611-x.
Murcia, C., Bongard, S., Kreutz, G., 2009. Emotional and neurohumoral responses to
dancing tango argentino: the effects of music and partner. Music Med. 1, 14–21.
https://doi.org/10.1177/1943862109335064.
Murnighan, J.K., Conlon, D.E., 1991. The dynamics of intense work groups: a study of
british string quartets. Adm. Sci. Q. 36 (2), 165–186. https://doi.org/10.2307/
2393352.
Naeem, M., Prasad, G., Watson, D.R., Kelso, J.A.S., 2012a. Electrophysiological
signatures of intentional social coordination in the 10–12Hz range. NeuroImage 59
(2), 1795–1803. https://doi.org/10.1016/j.neuroimage.2011.08.010.
Naeem, M., Prasad, G., Watson, D.R., Kelso, J.A.S., 2012b. Functional dissociation of
brain rhythms in social coordination. Clin. Neurophysiol. 123 (9), 1789–1797.
https://doi.org/10.1016/j.clinph.2012.02.065.
Nicol, J., Loehr, J., Christensen, J., Lang, J., Peacock, S., 2024. Duet playing in dementia
care: a new therapeutic music technology. Disabil. Rehabil.: Assist. Technol. 1–14.
https://doi.org/10.1080/17483107.2024.2351498.
Novembre, G., Ianetti, G.D., 2021. Hyperscanning alone cannot prove causality.
multibrain stimulation can. Trends Cogn. Sci. https://doi.org/10.1016/j.
tics.2020.11.003.
Novembre, G., Keller, P.E., 2014. A conceptual review on action-perception coupling in
the musicians’ brain: What is it good for? Front. Hum. Neurosci. 8 https://doi.org/
10.3389/fnhum.2014.00603.
Novembre, G., Knoblich, G., Dunne, L., Keller, P.E., 2017. Interpersonal synchrony
enhanced through 20 Hz phase-coupled dual brain stimulation. Soc. Cogn. Affect.
Neurosci. 12 (4), 662–670. https://doi.org/10.1093/scan/nsw172.
Novembre, G., Mitsopoulos, Z., Keller, P.E., 2019. Empathic perspective taking promotes
interpersonal coordination through music. Article 1. Sci. Rep. 9 (1) https://doi.org/
10.1038/s41598-019-48556-9.
Novembre, G., Sammler, D., Keller, P.E., 2016. Neural alpha oscillations index the
balance between self-other integration and segregation in real-time joint action.
Neuropsychologia 89, 414–425. https://doi.org/10.1016/j.
neuropsychologia.2016.07.027.
Novembre, G., Ticini, L.F., Schütz-Bosbach, S., Keller, P.E., 2012. Distinguishing self and
other in joint action. evidence from a musical paradigm. Cereb. Cortex 22 (12),
2894–2903. https://doi.org/10.1093/cercor/bhr364.
Novembre, G., Ticini, L.F., Schütz-Bosbach, S., Keller, P.E., 2014. Motor simulation and
the coordination of self and other in real-time joint action. Soc. Cogn. Affect.
Neurosci. 9 (8), 1062–1068. https://doi.org/10.1093/scan/nst086.
Novembre, G., Varlet, M., Muawiyath, S., Stevens, C.J., Keller, P.E., 2015. The E-music
box: an empirical method for exploring the universal capacity for musical production
and for social interaction through music. R. Soc. Open Sci. 2 (11), 150286 https://
doi.org/10.1098/rsos.150286.
Open Science, 2015. Estimating the reproducibility of psychological science. Science 349
(6251). https://doi.org/10.1126/science.aac4716.
Overy, K., 2012. Making music in a group: synchronization and shared experience. Ann.
N. Y. Acad. Sci. 1252 (1), 65–68. https://doi.org/10.1111/j.1749-6632.2012.06530.
x.
Paas, A., Novembre, G., Lappe, C., Keller, P.E., 2021. Not all errors are alike: modulation
of error-related neural responses in musical joint action. Soc. Cogn. Affect. Neurosci.
16 (5), 512–524. https://doi.org/10.1093/scan/nsab019.
Pachet, F., Roy, P., Foulon, R., 2017. Do jazz improvisers really interact?: The score effect
in collective jazz improvisation. In. Routledge Companion Embodied Music Interact.
167–176. https://doi.org/10.4324/9781315621364-19.
Page, M.J., Moher, D., Bossuyt, P.M., Boutron, I., Hoffmann, T.C., Mulrow, C.D.,
Shamseer, L., Tetzlaff, J.M., Akl, E.A., Brennan, S.E., Chou, R., Glanville, J.,
Grimshaw, J.M., Hr´
objartsson, A., Lalu, M.M., Li, T., Loder, E.W., Mayo-Wilson, E.,
McDonald, S., McKenzie, J.E., 2021. PRISMA 2020 explanation and elaboration:
updated guidance and exemplars for reporting systematic reviews. BMJ (Clinical
research ed.) 372, 160. https://doi.org/10.1136/bmj.n160.
Palmer, C., Demos, A.P., 2022. Are we in Time? How Predictive Coding and Dynamical
Systems Explain Musical Synchrony. Curr. Dir. Psychol. Sci. 31 (2), 147–153.
https://doi.org/10.1177/09637214211053635.
Palmer, C., Spidle, F., Koopmans, E., Schubert, P., 2019. Ears, heads, and eyes: when
singers synchronise. Q. J. Exp. Psychol. 72 (9), 2272–2287. https://doi.org/
10.1177/1747021819833968.
Pan, Y., Novembre, G., Song, B., Zhu, Y., Hu, Y., 2021. Dual brain stimulation enhances
interpersonal learning through spontaneous movement synchrony. Soc. Cogn. Affect.
Neurosci. 16 (1–2), 210–221. https://doi.org/10.1093/scan/nsaa080.
Papiotis, P., Marchini, M., Perez-Carrillo, A., Maestre, E., 2014. Measuring ensemble
interdependence in a string quartet through analysis of multidimensional
performance data. Front. Psychol. 5 https://doi.org/10.3389/fpsyg.2014.00963.
Pecenka, N., Keller, P.E., 2011. The role of temporal prediction abilities in interpersonal
sensorimotor synchronization. Exp. Brain Res. 211 (3), 505–515. https://doi.org/
10.1007/s00221-011-2616-0.
Pecenka, N., Keller, P.E., 2009. The Relationship between Auditory Imagery and Musical
Synchronization Abilities in Musicians. ESCOM 2009: 7th Triennial Conference of
European Society for the Cognitive Sciences of Music. 〈https://jyx.jyu./handl
e/123456789/20939〉.
Pesquita, A., Whitwell, R.L., Enns, J.T., 2018. Predictive joint-action model: a
hierarchical predictive approach to human cooperation. Psychon. Bull. Rev. 25 (5),
1751–1769. https://doi.org/10.3758/s13423-017-1393-6.
Pezzulo, G., Dindo, H., 2011. What should I do next? Using shared representations to
solve interaction problems. Exp. Brain Res. 211 (3), 613–630. https://doi.org/
10.1007/s00221-011-2712-1.
Phillips-Silver, J., Keller, P.E., 2012. Searching for roots of entrainment and joint action
in early musical interactions. Front. Hum. Neurosci. 6 https://doi.org/10.3389/
fnhum.2012.00026.
Power, R.A., Pluess, M., 2015. Heritability estimates of the big ve personality traits
based on common genetic variants. Transl. Psychiatry 5 (7), e604. https://doi.org/
10.1038/tp.2015.96.
Pressing, J., 1999. The referential dynamics of cognition and action. Psychol. Rev. 106
(4), 714–747. https://doi.org/10.1037/0033-295X.106.4.714.
Proksch, S., Reeves, M., Spivey, M., Balasubramaniam, R., 2022. Coordination dynamics
of multi-agent interaction in a musical ensemble. Sci. Rep. 12 (1), 421. https://doi.
org/10.1038/s41598-021-04463-6.
Quintana, D.S., Guastella, A.J., 2020. An allostatic theory of oxytocin. Trends Cogn. Sci.
24 (7), 515–528. https://doi.org/10.1016/j.tics.2020.03.008.
Rabinowitch, T.-C., Cross, I., Burnard, P., 2013. Long-term musical group interaction has
a positive inuence on empathy in children. Psychol. Music 41 (4), 484–498.
https://doi.org/10.1177/0305735612440609.
Rabinowitch, T.-C., Gill, S., 2021. Musical interaction, social communication, and
wellbeing. In: Timmers, R., Bailes, F., Daffern, H. (Eds.), Together in Music:
Coordination, expression, participation. Oxford University Press. https://doi.org/
10.1093/oso/9780198860761.003.0026.
Ragert, M., Schroeder, T., Keller, P.E., 2013. Knowing too little or too much: the effects of
familiarity with a co-performer’s part on interpersonal coordination in musical
ensembles. Front. Psychol. 4 https://doi.org/10.3389/fpsyg.2013.00368.
Rankin, S.K., Large, E.W., Fink, P.W., 2009. Fractal tempo uctuation and pulse
prediction. Music Percept. 26 (5), 401–413. https://doi.org/10.1525/
mp.2009.26.5.401.
Rasch, R.A., 1988. Timing and synchronization in ensemble performance. Generative
processes in music: The psychology of performance, improvisation, and composition.
Clarendon Press/Oxford University Press, pp. 70–90. https://doi.org/10.1093/
acprof:oso/9780198508465.001.0001.
Ravignani, A., Bowling, D.L., Fitch, W.T., 2014. Chorusing, synchrony, and the
evolutionary functions of rhythm. Front. Psychol. 5 https://doi.org/10.3389/
fpsyg.2014.01118.
Rennung, M., G¨
oritz, A.S., 2016. Prosocial consequences of interpersonal synchrony. Z. F.
üR. Psychol. 224 (3), 168–189. https://doi.org/10.1027/2151-2604/a000252.
Repp, B.H., 1992. Diversity and commonality in music performance: An analysis of
timing microstructure in Schumann’s ‘“Tr¨
aumerei. J. Acoust. Soc. Am. 92,
2546–2568. https://doi.org/10.1121/1.404425.
Repp, B.H., 1998. A microcosm of musical expression. I. Quantitative analysis of pianists’
timing in the initial measures of Chopin’s Etude in E major. J. Acoust. Soc. Am. 104,
1085–1100. https://doi.org/10.1121/1.423325.
Repp, B.H., 1999. Control of expressive and metronomic timing in pianists. J. Mot.
Behav. 31 (2), 145–164. https://doi.org/10.1080/00222899909600985.
Repp, B.H., 2005. Sensorimotor synchronization: a review of the tapping literature.
Psychon. Bull. Rev. 12 (6), 969–992. https://doi.org/10.3758/BF03206433.
Repp, B.H., Keller, P.E., 2004. Adaptation to tempo changes in sensorimotor
synchronization: Effects of intention, attention, and awareness. Q. J. Exp. Psychol.
57 (3), 499–521. https://doi.org/10.1080/02724980343000369.
Repp, B.H., Keller, P.E., 2008. Sensorimotor synchronization with adaptively timed
sequences. Hum. Mov. Sci. 27 (3), 423–456. https://doi.org/10.1016/j.
humov.2008.02.016.
S.F. Abalde et al.
Neuroscience and Biobehavioral Reviews 167 (2024) 105816
22
Repp, B.H., Knoblich, G., 2004. Perceiving Action Identity: How Pianists Recognize Their
Own Performances. Psychol. Sci. 15, 604–609. https://doi.org/10.1111/j.0956-
7976.2004.00727.x.
Repp, B.H., Su, Y.-H., 2013. Sensorimotor synchronization: a review of recent research
(2006-2012). Psychon. Bull. Rev. 20 (3), 403–452. https://doi.org/10.3758/s13423-
012-0371-2.
Rizzolatti, G., Sinigaglia, C., 2010. The functional role of the parieto-frontal mirror
circuit: interpretations and misinterpretations. Nat. Rev. Neurosci. 11, 264–274.
https://doi.org/10.1038/nrn2805.
Rizzolatti, G., Sinigaglia, C., 2016. The mirror mechanism: a basic principle of brain
function. Nat. Rev. Neurosci. 17, 757–765. https://doi.org/10.1038/nrn.2016.135.
Roman, I.R., Roman, A.S., Kim, J.C., Large, E.W., 2023. Hebbian learning with elasticity
explains how the spontaneous motor tempo affects music performance
synchronization. PLOS Comput. Biol. 19 (6), e1011154 https://doi.org/10.1371/
journal.pcbi.1011154.
Roman, I.R., Washburn, A., Large, E.W., Chafe, C., Fujioka, T., 2019. Delayed feedback
embedded in perception-action coordination cycles results in anticipation behavior
during synchronized rhythmic action: a dynamical systems approach. PLOS Comput.
Biol. 15 (10), e1007371 https://doi.org/10.1371/journal.pcbi.1007371.
Roque, T.R., Rajagopalan, N., Jain, S., Mehdizadeh, S.K., Leslie, G., 2022. Multimodal,
Musical Hyperscanning to Promote Empathy in HCI. In Proceedings of CHI
Conference on Human Factors in Computing Systems (CHI ’22).
Rossing, T.D., Sundberg, J., Ternstr¨
om, S., 1987. Acoustic comparison of soprano solo
and choir singing. J. Acoust. Soc. Am. 82 (3), 830–836. https://doi.org/10.1121/
1.395281.
Rosso, M., Heggli, O.A., Maes, P.J., Vuust, P., Leman, M., 2022. Mutual beta power
modulation in dyadic entrainment. NeuroImage 257, 119326. https://doi.org/
10.1016/j.neuroimage.2022.119326.
Rotter, J.B., 1966. Generalized expectancies for internal versus external control of
reinforcement. Psychol. Monogr.: Gen. Appl. 80 (1), 1–28. https://doi.org/10.1037/
h0092976.
Sabharwal, S.R., Varlet, M., Breaden, M., Volpe, G., Camurri, A., Keller, P.E., 2022.
huSync - a model and system for the measure of synchronization in small groups: a
case study on musical joint action. IEEE Access 10, 92357–92372. https://doi.org/
10.1109/ACCESS.2022.3202959.
S¨
anger, J., Lindenberger, U., Müller, V., 2011. Interactive brains, social minds. Commun.
Integr. Biol. 4 (6), 655–663. https://doi.org/10.4161/cib.17934.
S¨
anger, J., Müller, V., Lindenberger, U., 2012. Intra- and interbrain synchronization and
network properties when playing guitar in duets. Front. Hum. Neurosci. 6 https://
doi.org/10.3389/fnhum.2012.00312.
S¨
anger, J., Müller, V., Lindenberger, U., 2013. Directionality in hyperbrain networks
discriminates between leaders and followers in guitar duets. Front. Hum. Neurosci. 7
https://doi.org/10.3389/fnhum.2013.00234.
Savage, P.E., Brown, S., Sakai, E., Currie, T.E., 2015. Statistical universals reveal the
structures and functions of human music. Proc. Natl. Acad. Sci. 112 (29),
8987–8992. https://doi.org/10.1073/pnas.1414495112.
Savage, P.E., Loui, P., Tarr, B., Schachner, A., Glowacki, L., Mithen, S., Fitch, W.T., 2021.
Music as a coevolved system for social bonding. Behav. Brain Sci. 44, e59 https://
doi.org/10.1017/S0140525X20000333.
Schiavio, A., Jaegher, H.D., 2017. Participatory sense-making in joint musical practice.
The Routledge Companion to Embodied Music Interaction. Routledge. https://doi.
org/10.4324/9781315621364-4.
Schmidt, R.C., Richardson, M.J., 2008. Dynamics of interpersonal coordination. In:
Fuchs, A., Jirsa, V.K. (Eds.), Coordination: Neural, behavioral and social dynamics.
Springer, pp. 281–308. https://doi.org/10.1007/978-3-540-74479-5_14.
Schulze, H.-H., Cordes, A., Vorberg, D., 2005. Keeping synchrony while tempo changes:
accelerando and ritardando. Music Percept. 22 (3), 461–477. https://doi.org/
10.1525/mp.2005.22.3.461.
Schulze, H.-H., Vorberg, D., 2002. Linear phase correction models for synchronization:
parameter identication and estimation of parameters. Brain Cogn. 48 (1), 80–97.
https://doi.org/10.1006/brcg.2001.1305.
Sebanz, N., Bekkering, H., Knoblich, G., 2006. Joint action: bodies and minds moving
together. Trends Cogn. Sci. 10 (2), 70–76. https://doi.org/10.1016/j.
tics.2005.12.009.
Setzler, M., Goldstone, R., 2020. Coordination and consonance between interacting,
improvising musicians. Open Mind 4, 88–101. https://doi.org/10.1162/opmi_a_
00036.
Sevdalis, V., Keller, P.E., 2011. Perceiving performer identity and intended expression
intensity in point-light displays of dance. Psychol. Res. 75 (5), 423–434. https://doi.
org/10.1007/s00426-010-0312-5.
Sevdalis, V., Keller, P.E., 2012. Perceiving bodies in motion: expression intensity,
empathy, and experience. Exp. Brain Res. 222 (4), 447–453. https://doi.org/
10.1007/s00221-012-3229-y.
Sevdalis, V., Raab, M., 2014. Empathy in sports, exercise, and the performing arts.
Psychol. Sport Exerc. 15 (2), 173–179. https://doi.org/10.1016/j.
psychsport.2013.10.013.
Shaffer, L.H., 1984. Timing in solo and duet piano performances. Q. J. Exp. Psychol. A:
Hum. Exp. Psychol., 36A (4), 577–595. https://doi.org/10.1080/
14640748408402180.
Sharda, M., Tuerk, C., Chowdhury, R., Jamey, K., Foster, N., Custo-Blanch, M., Tan, M.,
Nadig, A., Hyde, K., 2018. Music improves social communication and
auditory–motor connectivity in children with autism. Article 1. Transl. Psychiatry 8
(1) https://doi.org/10.1038/s41398-018-0287-3.
Singer, T., Lamm, C., 2009. The social neuroscience of empathy. Ann. N. Y. Acad. Sci.
1156 (1), 81–96. https://doi.org/10.1111/j.1749-6632.2009.04418.x.
Spiro, N., Himberg, T., 2016. Analysing change in music therapy interactions of children
with communication difculties. Philos. Trans. R. Soc. B: Biol. Sci. 371 (1693),
20150374. https://doi.org/10.1098/rstb.2015.0374.
Srinivasan, S.M., Eigsti, I.-M., Neelly, L., Bhat, A.N., 2016. The effects of embodied
rhythm and robotic interventions on the spontaneous and responsive social attention
patterns of children with autism spectrum disorder (ASD): a pilot randomized
controlled trial. Res. Autism Spectr. Disord. 27, 54–72. https://doi.org/10.1016/j.
rasd.2016.01.004.
Stepp, N., Turvey, M.T., 2010. On strong anticipation. Cogn. Syst. Res. 11 (2), 148–164.
https://doi.org/10.1016/j.cogsys.2009.03.003.
Strogatz, S.H., 2015. Nonlinear Dynamics and Chaos: With Applications to Physics,
Biology, Chemistry, and Engineering, Second Edition (2nd edition). CRC Press.
https://doi.org/10.1201/9780429492563.
Stupacher, J., Mikkelsen, J., Vuust, P., 2021. Higher empathy is associated with stronger
social bonding when moving together with music. Psychol. Music. 50 (5),
1511–1526. https://doi.org/10.1177/03057356211050681.
Sundberg, J., 2018. The Singing Voice. In: Frühholz, S., Belin, P. (Eds.), The Oxford
Handbook of Voice Perception. Oxford University Press, p. 0. https://doi.org/
10.1093/oxfordhb/9780198743187.013.6.
Sutherland, A., Cartwright, P.A., 2022. Working together: implications of leadership
style for the music ensemble, 02557614221084310 Int. J. Music Educ.. https://doi.
org/10.1177/02557614221084310.
Tanaka, H., Ishikawa, T., Lee, J., Kakei, S., 2020. The cerebro-cerebellum as a locus of
forward model: a review. Front. Syst. Neurosci. 14 https://doi.org/10.3389/
fnsys.2020.00019.
Tarr, B., Launay, J., Dunbar, R.I.M., 2014. Music and social bonding: “self-other”
merging and neurohormonal mechanisms. Front. Psychol. 5 https://doi.org/
10.3389/fpsyg.2014.01096.
Ternstr¨
om, S., 2003. Choir acoustics: an overview of scientic research published to date.
Int. J. Res. Choral Sing. 1 (1), 3–12.
Timmers, R., Endo, S., Bradbury, A., Wing, A.M., 2014. Synchronization and leadership
in string quartet performance: a case study of auditory and visual cues. Front.
Psychol. 5 https://doi.org/10.3389/fpsyg.2014.00645.
Timmers, R., MacRitchie, J., Schabrun, S.M., Thapa, T., Varlet, M., Keller, P.E., 2020.
Neural multimodal integration underlying synchronization with a co-performer in
music: inuences of motor expertise and visual information. Neurosci. Lett. 721,
134803 https://doi.org/10.1016/j.neulet.2020.134803.
Tincani, M., Travers, J., 2019. Replication research, publication bias, and applied
behavior analysis. Perspect. Behav. Sci. 42 (1), 59–75. https://doi.org/10.1007/
s40614-019-00191-5.
Tognoli, E., Kelso, J.A.S., 2014. The metastable brain. Neuron 81 (1), 35–48. https://doi.
org/10.1016/j.neuron.2013.12.022.
Tognoli, E., Lagarde, J., DeGuzman, G.C., Kelso, J.A.S., 2007. The phi complex as a
neuromarker of human social coordination. Proc. Natl. Acad. Sci. 104 (19),
8190–8195. https://doi.org/10.1073/pnas.0611453104.
Tranchant, P., Scholler, E., Palmer, C., 2022. Endogenous rhythms inuence musicians’
and non-musicians’ interpersonal synchrony. Article 1. Sci. Rep. 12 (1) https://doi.
org/10.1038/s41598-022-16686-2.
Trevarthen, C., 1999. Musicality and the intrinsic motive pulse: evidence from human
psychobiology and infant communication. Musicae Scientiae. 3, 155–215. https://
doi.org/10.1177/10298649000030S109.
Tuastad, L., O’Grady, L., 2013. Music therapy inside and outside prison—a freedom
practice? Nord. J. Music Ther. 22 (3), 210–232. https://doi.org/10.1080/
08098131.2012.752760.
Tzanaki, P., 2022. The positive feedback loop of empathy and interpersonal
synchronisation: discussing a theoretical model and its implications for musical and
social development, 20592043221142715 Music Sci. 5. https://doi.org/10.1177/
20592043221142715.
Vaarten, L., Wiltshire, T.J., 2023. Capturing coordination and intentionality in joint
musical improvisation. Psychomusicology: Music, Mind, Brain. https://doi.org/
10.1037/pmu0000299.
van der Schyff, D., Schiavio, A., Elliott, D.J., 2022. Musical Bodies, musical minds:
enactive cognitive science and the meaning of human musicality. The MIT Press.
https://doi.org/10.7551/mitpress/12117.001.0001.
van der Schyff, D., Schiavio, A., Walton, A., Velardo, V., Chemero, A., 2018. Musical
creativity and the embodied mind: exploring the possibilities of 4e cognition and
dynamical systems theory. Music Sci. 1 https://doi.org/10.1177/
2059204318792319.
van der Schyff, D., Schiavio, A., 2017. Evolutionary musicology meets embodied
cognition: biocultural coevolution and the enactive origins of human musicality.
Front. Neurosci. 11 https://doi.org/10.3389/fnins.2017.00519.
van der Steen, M.C., Jacoby, N., Fairhurst, M.T., Keller, P.E., 2015. Sensorimotor
synchronization with tempo-changing auditory sequences: modeling temporal
adaptation and anticipation. Brain Res. 1626, 66–87. https://doi.org/10.1016/j.
brainres.2015.01.053.
van der Steen, M.C., Keller, P.E., 2013. The Adaptation and anticipation model (adam) of
sensorimotor synchronization. Front. Hum. Neurosci. 7, 253. https://doi.org/
10.3389/fnhum.2013.00253.
van der Steen, M.C., Schwartze, M., Kotz, S.A., Keller, P.E., 2015. Modeling effects of
cerebellar and basal ganglia lesions on adaptation and anticipation during
sensorimotor synchronization. Ann. N. Y. Acad. Sci. 1337, 101–110. https://doi.org/
10.1111/nyas.12628.
Vanzella, P., Balardin, J.B., Furucho, R.A., Zimeo Morais, G.A., Braun Janzen, T.,
Sammler, D., Sato, J.R., 2019. fNIRS Responses in professional violinists while
playing duets: evidence for distinct leader and follower roles at the brain level.
Front. Psychol. 10 https://doi.org/10.3389/fpsyg.2019.00164.
S.F. Abalde et al.
Neuroscience and Biobehavioral Reviews 167 (2024) 105816
23
Varni, G., Mancini, M., Fadiga, L., Camurri, A., Volpe, G., 2019. The change matters!
Measuring the effect of changing the leader in joint music performances. IEEE Trans.
Affect. Comput. 1. https://doi.org/10.1109/TAFFC.2019.2951368, 1.
Vicaria, I.M., Dickens, L., 2016. Meta-analyses of the intra- and interpersonal outcomes
of interpersonal coordination. J. Nonverbal Behav. 40 (4), 335–361. https://doi.org/
10.1007/s10919-016-0238-8.
Vickhoff, B., Malmgren, H., Åstr¨
om, R., Nyberg, G., Engvall, M., Snygg, J., Nilsson, M.,
J¨
ornsten, R., 2013. Music structure determines heart rate variability of singers.
Front. Psychol. 4 https://doi.org/10.3389/fpsyg.2013.00334.
Vorberg, D., Schulze, H.-H., 2002. Linear phase-correction in synchronization:
predictions, parameter estimation, and simulations. J. Math. Psychol. 46 (1), 56–87.
https://doi.org/10.1006/jmps.2001.1375.
Vorberg, D., Wing, A., 1996. Modeling variability and dependence in timing. In
Handbook of perception and action, Vol. 2: Motor skills. Academic Press, pp. 181–262.
Vorberg, D., 2005. Synchronization in duet performance: Testing the two-person phase
error correction model. Tenth Rhythm Perception and Production Workshop, Alden
Biesen, Belgium.
Vuust, P., Heggli, O.A., Friston, K.J., Kringelbach, M.L., 2022. Music in the brain. Nat.
Rev. Neurosci. 23 (5), 287–305. https://doi.org/10.1038/s41583-022-00578-5.
Walton, A.E., Richardson, M.J., Chemero, A., 2014. Self-organization and semiosis in jazz
improvisation. Int. J. Signs Semiot. Syst. (IJSSS) 3 (2), 12–25. https://doi.org/
10.4018/IJSSS.2014070102.
Walton, A.E., Richardson, M.J., Langland-Hassan, P., Chemero, A., 2015. Improvisation
and the self-organization of multiple musical bodies. Front. Psychol. 6 https://doi.
org/10.3389/fpsyg.2015.00313.
Walton, A.E., Washburn, A., Langland-Hassan, P., Chemero, A., Kloos, H., Richardson, M.
J., 2018a. Creating time: social collaboration in music improvisation. Top. Cogn. Sci.
10 (1), 95–119. https://doi.org/10.1111/tops.12306.
Wan, Y., Wei, Y., Xu, B., Zhu, L., Tanenhaus, M.K., 2023. Musical coordination affects
children’s perspective-taking, but musical synchrony does not. Dev. sci. 26 (5)
https://doi.org/10.1111/desc.13367.
Wanderley, M.M., Vines, B.W., Middleton, N., McKay, C., Hatch, W., 2005. The musical
signicance of clarinetists’ ancillary gestures: an exploration of the eld. J. N. Music
Res. 34 (1), 97–113. https://doi.org/10.1080/09298210500124208.
Washburn, A., Rom´
an, I., Huberth, M., Gang, N., Dauer, T., Reid, W., Nanou, C.,
Wright, M., Fujioka, T., 2019. Musical role asymmetries in piano duet performance
inuence alpha-band neural oscillation and behavioral synchronization. Front.
Neurosci. 13 https://doi.org/10.3389/fnins.2019.01088.
Weiss, M.W., Peretz, I., 2022. Improvisation is a novel tool to study musicality. Article 1.
Sci. Rep. 12 (1) https://doi.org/10.1038/s41598-022-15312-5.
Welniarz, Q., Worbe, Y., Gallea, C., 2021. The forward model: a unifying theory for the
role of the cerebellum in motor control and sense of agency. Front. Syst. Neurosci.
15, 644059 https://doi.org/10.3389/fnsys.2021.644059.
Williamon, A., Davidson, J.W., 2002. Exploring co-performer communication. Musica
Sci. 6 (1), 53–72. https://doi.org/10.1177/102986490200600103.
Wiltermuth, S.S., Heath, C., 2009. Synchrony and cooperation. Psychol. Sci. 20 (1), 1–5.
https://doi.org/10.1111/j.1467-9280.2008.02253.x.
Wing, A.M., Endo, S., Bradbury, A., Vorberg, D., 2014. Optimal feedback correction in
string quartet synchronization. J. R. Soc. Interface 11 (93), 20131125. https://doi.
org/10.1098/rsif.2013.1125.
Wolf, T., Goupil, L., Canonne, C., 2023. Beyond togetherness: interactional dissensus
fosters creativity and tension in freely improvised musical duos. Psychol. Aesthet.,
Creat., Arts. https://doi.org/10.1037/aca0000588.
Wolf, T., Sebanz, N., Knoblich, G., 2018. Joint action coordination in expert-novice pairs:
Can experts predict novices’ suboptimal timing? Cognition 178, 103–108. https://
doi.org/10.1016/j.cognition.2018.05.012.
W¨
ollner, C., Auhagen, W., 2008. Perceiving conductors’ expressive gestures from
different visual perspectives. an exploratory continuous response study. Music
Percept. 26 (2), 129–143. https://doi.org/10.1525/mp.2008.26.2.129.
W¨
ollner, C., Deconinck, F.J.A., Parkinson, J., Hove, M.J., Keller, P.E., 2012. The
perception of prototypical motion: synchronization is enhanced with quantitatively
morphed gestures of musical conductors. J. Exp. Psychol.: Hum. Percept. Perform. 38
(6), 1390–1403. https://doi.org/10.1037/a0028130.
Wolpert, D.M., Doya, K., Kawato, M., 2003. A unifying computational framework for
motor control and social interaction. Philos. Trans. R. Soc. B: Biol. Sci. 358 (1431),
593–602. https://doi.org/10.1098/rstb.2002.1238.
Wolpert, D.M., Miall, R.C., Kawato, M., 1998. Internal models in the cerebellum. Trends
Cogn. Sci. 2 (9), 338–347. https://doi.org/10.1016/S1364-6613(98)01221-2.
Yoneta, N., Watanabe, H., Shimojo, A., Takano, K., Saito, T., Yagyu, K., Shiraishi, H.,
Yokosawa, K., Boasen, J., 2022. Magnetoencephalography hyperscanning evidence
of differing cognitive strategies due to social role during auditory communication.
Front. Neurosci. 16, 790057 https://doi.org/10.3389/fnins.2022.790057.
Yoo, G.E., Kim, S.J., 2018. Dyadic drum playing and social skills: implications for
rhythm-mediated intervention for children with autism spectrum disorder. J. Music
Ther. 55 (3), 340–375. https://doi.org/10.1093/jmt/thy013.
Zamm, A., Debener, S., Sebanz, N., 2023. The spontaneous emergence of rhythmic
coordination in turn taking. Sci. Rep. 13 (1), 3259. https://doi.org/10.1038/s41598-
022-18480-6.
Zamm, A., Debener, S., Bauer, A.-K.R., Bleichner, M.G., Demos, A.P., Palmer, C., 2018.
Amplitude envelope correlations measure synchronous cortical oscillations in
performing musicians. Ann. N. Y. Acad. Sci. 1423 (1), 251–263. https://doi.org/
10.1111/nyas.13738.
Zamm, A., Debener, S., Konvalinka, I., Sebanz, N., Knoblich, G., 2021. The sound of
silence: an EEG study of how musicians time pauses in individual and joint music
performance. Soc. Cogn. Affect. Neurosci. 16 (1–2), 31–42. https://doi.org/
10.1093/scan/nsaa096.
Zamm, A., Palmer, C., Bauer, A.-K.R., Bleichner, M.G., Demos, A.P., Debener, S., 2021.
Behavioral and neural dynamics of interpersonal synchrony between performing
musicians: a wireless EEG hyperscanning study. Front. Hum. Neurosci. 15 https://
doi.org/10.3389/fnhum.2021.717810. 〈https://www.frontiersin.org/article/10.
3389/fnhum.2021.717810〉.
Zamm, A., Pfordresher, P.Q., Palmer, C., 2015. Temporal coordination in joint music
performance: effects of endogenous rhythms and auditory feedback. Exp. Brain Res.
233 (2), 607–615. https://doi.org/10.1007/s00221-014-4140-5.
Zamm, A., Wellman, C., Palmer, C., 2016. Endogenous rhythms inuence interpersonal
synchrony. J. Exp. Psychol.: Hum. Percept. Perform. 42 (5), 611–616. https://doi.
org/10.1037/xhp0000201.
Zatorre, R.J., Chen, J.L., Penhune, V.B., 2007. When the brain plays music:
auditory–motor interactions in music perception and production. Article 7. Nat. Rev.
Neurosci. 8 (7) https://doi.org/10.1038/nrn2152.
S.F. Abalde et al.
Neuroscience and Biobehavioral Reviews 167 (2024) 105816
24
