ORIGINAL RESEARCH ARTICLE
published: 03 January 2014
doi: 10.3389/fpsyg.2013.01008
Action-based effects on music perception
Pieter-Jan Maes 1*, Marc Leman 2 , Caroline Palmer 3 and Marcelo M. Wanderley 1
1
2
3
Department of Music Research, McGill University, Montreal, QC, Canada
Department of Musicology, Ghent University, Ghent, Belgium
Department of Psychology, McGill University, Montreal, QC, Canada
Edited by:
Adam M. Croom, University of
Pennsylvania, USA
Reviewed by:
Martin Lotze, University of
Greifswald, Germany
Michael Hove, Harvard Medical
School, USA
*Correspondence:
Pieter-Jan Maes, Department of
Music Research, McGill University,
527 Sherbrooke St. West, Montreal,
QC H3A 1E3, Canada
e-mail: pieter-jan.maes@
mail.mcgill.ca
The classical, disembodied approach to music cognition conceptualizes action and
perception as separate, peripheral processes. In contrast, embodied accounts of music
cognition emphasize the central role of the close coupling of action and perception. It
is a commonly established fact that perception spurs action tendencies. We present
a theoretical framework that captures the ways in which the human motor system
and its actions can reciprocally influence the perception of music. The cornerstone of
this framework is the common coding theory, postulating a representational overlap
in the brain between the planning, the execution, and the perception of movement.
The integration of action and perception in so-called internal models is explained as a
result of associative learning processes. Characteristic of internal models is that they
allow intended or perceived sensory states to be transferred into corresponding motor
commands (inverse modeling), and vice versa, to predict the sensory outcomes of planned
actions (forward modeling). Embodied accounts typically refer to inverse modeling to
explain action effects on music perception (Leman, 2007). We extend this account by
pinpointing forward modeling as an alternative mechanism by which action can modulate
perception. We provide an extensive overview of recent empirical evidence in support
of this idea. Additionally, we demonstrate that motor dysfunctions can cause perceptual
disabilities, supporting the main idea of the paper that the human motor system plays
a functional role in auditory perception. The finding that music perception is shaped
by the human motor system and its actions suggests that the musical mind is highly
embodied. However, we advocate for a more radical approach to embodied (music)
cognition in the sense that it needs to be considered as a dynamical process, in
which aspects of action, perception, introspection, and social interaction are of crucial
importance.
Keywords: embodied music cognition, common coding theory, sensory-motor association learning, dynamical
systems, internal model
1. INTRODUCTION
Music is known to be a powerful medium that evokes body movements in listeners, ranging from tapping the feet, shaking the
head, swaying the arms and hips, to more sophisticated forms of
free or stylized dance. Research has shown that these body movements often reflect the performer’s movements from which the
music originated (Leman et al., 2009; Godøy and Leman, 2010),
certain aspects of the melody, harmony, rhythm and timbre (Maes
et al., 2010; Naveda and Leman, 2010; Toiviainen et al., 2010;
Burger et al., 2013; Leman et al., 2013), or even the listeners’ mood
(Van Dyck et al., 2013). These and similar studies importantly
indicate that the listeners’ musical mind (attention, intention,
mood, feelings, etc.) can be accessed through body movement,
without the need for symbolic representations like language or
musical scores. However, despite the explicit focus on the human
body and body movements, these and similar studies do not consider the musical mind as being fundamentally embodied. The
findings do not exclude the possibility that movement responses
to music are mere peripheral epiphenomena resulting from central cognitive processes. Only recently have studies started to
www.frontiersin.org
emerge, demonstrating how the musical mind can be shaped
by the human motor system and the movements it produces
(Phillips-Silver and Trainor, 2005, 2007; Repp and Knoblich,
2009; Sedlmeier et al., 2011; Iordanescu et al., 2013; Loehr, 2013;
Manning and Schutz, 2013; Maes and Leman, 2013; Timm et al.,
2013). This line of research reflects an important paradigm shift
within cognitive science. The classical view, inspired by the developments of computer science and artificial intelligence in the
1950s–1960s, pertains to an “information processing” approach
that considers a strictly unidirectional information flow from perception (input) to cognition (central processing unit) to action
(output) (Neisser, 1967; Laske, 1974; Fodor, 1975; Pylyshyn and
Demopoulos, 1986; Massaro, 1990). Accordingly, sensory information received from the external world is perceived, translated
into a syntactic code of meaningful symbols, and processed
according to a systematic set of rules. Then, body movements
and other sorts of behavior are considered as mere outcomes of
these higher-level, formal symbol manipulations. Hence, in this
classical view of cognition, perception and action are completely
separated from each other, and are outside central cognition
January 2014 | Volume 4 | Article 1008 | 1
Maes et al.
[what Hurley (2001) describes as the “sandwich model of cognition”]. This classical model is obsolete, as research shows that
perception and action are strongly intertwined and can mutually
exert influence on each other. In what became the embodied cognition theory, the human body - with its perceptual and motor
systems - and its interaction with the outside world, became
central to human cognition (Varela et al., 1991; Leman, 2007;
Chemero, 2009; Krueger, 2009; Glenberg, 2010; Shapiro, 2010).
Within this framework of embodied cognition, the common coding theory (Prinz, 1990, 1997; Hommel et al., 2001) has been an
influential theory postulating a close coupling between perception and action. Although the theory is not readily falsifiable, it
provides a general framework for developing more detailed and
testable explanatory models (cf. Hommel et al., 2001). In essence,
the theory states that the planning or execution of an action, and
the mere perception of the (multi-)sensory consequences of that
action, are similarly represented (coded) in the brain, thereby
recruiting both sensory and motor brain areas. Important in this
theory is that the integration of motor and sensory representations leads to internal models of the relationship between the
body and the external environment, which can contain inverse
and forward components (Wolpert et al., 1995). Inverse models
represent an information flow from perception to action, in the
sense that they allow the system to estimate from incoming sensory information the corresponding motor commands required
to generate that specific sensory state [cf. Rizzolatti et al. (2001):
direct-matching hypothesis]. In contrast, forward models represent an information flow from action to perception, in the sense
that they allow to predict the likely sensory outcome of a planned
or executed action (Davidson and Wolpert, 2005; Bubic et al.,
2010; Waszak et al., 2012). Currently, the idea is gaining consensus
that the combination of inverse and forward modeling processes
guides people’s interaction with the external world, including
motor control and sensory processing.
In the present paper, we set the common coding theory, and
the related theory of internal models, as a theoretical framework for understanding action-based effects on music perception.
We conjecture that a focus on both inverse and forward modeling processes can provide a comprehensive view of how the
human motor system and its actions influence music perception.
In the domain of embodied music cognition, one typically refers
to inverse modeling processes to explain action-based effects on
music perception. Music spurs body movements that amount to
expressive qualities, intentions, inner feelings, etc. Many of the
musical elements that contribute to expressivity (e.g., dynamics, articulation, touch, phrasing, vibrato, rubato, etc.) directly
relate to physical aspects of movement and space. Inverse modeling processes enable us to render (or decode) perceived patterns
of musical expressivity into corresponding body movements. This
corporeal mirroring process is responsible for listeners’ tendency
to ascribe intentions, inner feelings, etc. to music (Godøy, 2003;
Leman, 2007; Cox, 2011). We want to extend this “traditional”
embodied perspective to the role of the human body in music
cognition with a focus on forward modeling processes. From
this perspective, it is not about how the body resonates with
the music, but rather about how predicted sensory outcomes of
planned or performed actions can be projected onto the perceived
Frontiers in Psychology | Theoretical and Philosophical Psychology
Action effects on music perception
music. Recently, there has been a proliferation of studies addressing the role of forward models in action-based effects on visual,
auditory, and somatosensory perception. In the domain of visual
perception, several papers review action-based effects on visual
perception (Schütz-Bosbach and Prinz, 2007; Shin et al., 2010;
Witt, 2011; Halász and Cunnington, 2012). Currently, such a
review of studies investigating action-based effects on auditory
perception does not exist. An important goal of the present paper
is to provide such a review of studies in support of the proposed
theories and principles.
The paper is structured as follows. In section 2, we argue
that sensory-motor association learning can be considered a central mechanism underlying the development of internal models.
Accordingly, we claim that the ability to predict the auditory consequences of one’s actions, which is one of the core mechanisms of
action-based effects on perception, depends on previous acquired
sensory-motor associations. Further in that section, we define
the concepts of temporal contiguity and probabilistic contingency
as two main principles underlying associative learning processes.
Additionally, we discuss musical instrument playing as a special
but highly illustrative case of sensory-motor association learning.
In section 3, we provide extensive empirical evidence for the claim
that the principle of motor resonance, inherent in inverse models (section 3.1), together with auditory predictions generated by
forward models (section 3.2), can modulate auditory perception.
Also, we demonstrate that deficits in the motor system may have
impaired auditory perception as a consequence (section 3.3). To
conclude, an extensive discussion is presented in which we advocate a radical approach to embodied music cognition based on
dynamical systems. Moreover, we pinpoint music as an ideal study
object to extend this approach based on dynamical systems to
embodied cognition, as it incorporates expressivity, introspection
(affect, motivation, intentions, metacognition, etc.), and social
interaction as crucial components.
2. ASSOCIATIVE LEARNING
Above, we outlined the common coding of action and perception as a core mechanism underlying people’s engagement with
music (motor control and sensory processing). However, this
account does not address the question of how action and perception become integrated. We advocate that this integration is
established, in large part, through associative learning processes.
The study of these processes can be traced back to the philosophy of Aristotle who stated that things that occur near each
other in time and/or space are readily associated (i.e., law of
contiguity). During the Enlightenment, these ideas were further
developed by the Associationist School (e.g., David Hume, John
Locke, John Stuart Mill, etc.). In the nineteenth century William
James stated, as an elementary law of association, that “when two
elementary brain-processes have been active together or in immediate succession, one of them, on reoccurring, tends to propagate
its excitement into the other” (James, 1890, p.566). In the late
1940s, this principle was paraphrased in Hebb’s law “neurons that
fire together wire together.” A more recent account is the theory of associative sequence learning (ASL) introduced by (Heyes
and Ray, 2000). The ASL theory suggests that imitation is mediated by associative processes that establish links between sensory
January 2014 | Volume 4 | Article 1008 | 2
Maes et al.
and motor representations. This theory has been applied to the
human mirror neuron system (MNS) in an attempt to reconsider its origin and function. The classical view on the MNS—as
originated in the work of Gallese et al. (1996); Rizzolatti et al.
(2001); Kohler et al. (2002)—is that it is an innate system, only
marginally influenced by sensory-motor experience, and inherently codes the meaning of actions (e.g., goals, intentions, etc.).
This view was soon adopted to explain various important psychological and social functions, such as action understanding,
learning by imitation, empathy, and social interaction. However,
critical voices have been raised in opposition to this classical view,
in particular to the idea that mirror neurons are adapted by evolution to directly and consistently encode action goals (Hickok,
2009; Heyes, 2010; Catmur, 2012). The alternative view—what
Heyes (2010) termed the associative hypothesis—states that the
development of the MNS is promoted by sensory-motor associative learning. Empirical evidence is provided in the context of
music and dance. Haslinger et al. (2005) compared expert pianists
with musically naive controls with fMRI while observing pianoplaying and non-piano-playing finger movements. The results
showed that the expert pianists exhibited stronger activation in
brain areas associated with the MNS (inferior fronto-parietotemporal region) compared to the control participants. Similarly,
in the context of dance, Calvo-Merino et al. (2005) showed that
activation in brain areas related with the MNS in expert dancers
(classical ballet and capoeira) was higher when they observed
a familiar dance style. In conclusion, the associative hypothesis
states that, through systematically repeated experiences, sensory
events are associated with particular motor acts and excitatory
links between both are created, resulting in the development of
“internal models.” Accordingly, when a sensory representation is
activated, the corresponding motor representation is automatically co-activated (inverse modeling), and vice versa: when an
action is merely planned or executed, the corresponding sensory
representation is automatically co-activated (forward modeling).
As will be explained further in Section 3, both inverse and forward modeling processes can contribute to action-based effects
on auditory perception.
An important challenge of future research is to further identify the neural substrates underlying associative learning processes. Studies pinpoint the cerebellum (Imamizu and Kawato,
2009; Timmann et al., 2010), the striatum—an input nucleus of
the basal ganglia—(Pasupathy and Miller, 2005; Williams and
Eskandar, 2006; Lalazar and Vaadia, 2008; Melcher et al., 2012),
prefrontal areas (Deiber et al., 1997; Bangert and Altenmüller,
2003; Pasupathy and Miller, 2005), the supplementary motor area
(Pasupathy and Miller, 2005), and the premotor cortex (Deiber
et al., 1997; Schubotz, 2007; Chen et al., 2009; Imamizu and
Kawato, 2009) as important neural structures underlying association learning leading to the development of internal models
and predictive mechanisms. In the field of music research, evidence suggests that the striatum is involved in prediction and
anticipation. Grahn and Rowe (2013) assessed the role of the
putamen—one of the two nuclei that make up the striatum—in
beat prediction. Their findings show that the putamen becomes
active only after having established a predictable sense of the
beat. Accordingly, they conclude that putamen activity reflects the
www.frontiersin.org
Action effects on music perception
process of internally generating a model of the stimulus rhythm.
In a study of Leaver et al. (2009), anticipatory/predictive imagery
of musical melodies was shown to be associated with activation in
a variety of cortical (frontal and parietal) and subcortical (basal
ganglia and cerebellum) structures. Interestingly, different neural substrates underlay different stages of development of learned
conditional associations between melodies (“moderately learned”
vs. “well-learned”). Findings show that the supplementary motor
area and the basal ganglia (putamen) are particularly important
in early and moderate stages of learning, while the frontal cortex
seems to dominate end stages (cf. Pasupathy and Miller, 2005).
These dynamics in neural activation involved in sensorimotor
association learning characterizes motor skill learning in general.
Studies have demonstrated that the recruitment of distributed
brain regions in the process of acquiring motor skills depends
on the type of motor task (motor sequence learning vs. motor
adaptation) and on the stage of learning (fast learning, slow learning, consolidation, automatization, retention) (Ungerleider et al.,
2002; Luft and Buitrago, 2005; Doyon et al., 2009).
2.1. CONTINUITY AND CONTINGENCY
Auditory-motor association learning—i.e., the acquisition of
knowledge of sound-movement relationships—is modulated
by both temporal “contiguity” and probabilistic “contingency”
(Cooper et al., 2012). “Contiguity” refers to the proximity of two
events (e.g., movement and sound) in time and space. The concept originates in Aristotle’s law of contiguity, stating that things
that occur near each other in time and/or space are readily associated. It is not, however, the case that association learning occurs
every time two events are linked together in time or space. Instead,
it is necessary that the relationship between the events is predictable. “Contingency” refers to this degree of probability or the
likelihood that two or more events belong together. In statistical
terms, contingency is related to covariance, being a measure of
how much two random variables change together.
Elsner and Hommel (2004) present two experiments in which
the role of contiguity and contingency were investigated in
the development of sensory-motor associations. Each experiment consisted of a training phase followed by a test phase.
In the training phase, participants learned action-effect associations by repeatedly pressing keys (action) triggering corresponding tones (effect). In the subsequent test phase, tones
were presented and participants were asked to make speeded
responses to these stimuli by pressing keys either in a consistent fashion (i.e., action-effect mapping as in the training phase)
or inconsistent fashion (i.e., other action-effect mapping as in
the training phase). If an action-effect association was established in the training phase, then participants were expected
to respond faster in an acquisition-consistent fashion than in
an acquisition-inconsistent fashion. In the training phase of
Experiment 1, the contiguity between action and effect was systematically manipulated by adding an increasing delay between
the two (50, 1000, and 2000 ms). In the test phase, participants
responded faster in acquisition-consistent test blocks compared
to acquisition-inconsistent test blocks when action-effects training delays were 50 or 1000 ms. Accordingly, association learning
seemed to be successful only with action-effect delays of up to
January 2014 | Volume 4 | Article 1008 | 3
Maes et al.
1000 ms, signaling an effect of contiguity in association learning. In the training phase of Experiment 2, the contingency
between action and effect was systematically manipulated by
varying the relative frequencies of the presence or absence of
tones with corresponding keypresses. Again, it was shown that
the acquisition-consistency effect in the test phase was affected
by the contingency of action and effect in the training phase.
Together, these findings show that both the contiguity and contingency between actions (here, keypresses) and auditory events
(here, sinusoidal tones, MIDI marimba/flute tones) are important
in the process of acquiring sensory-motor associations.
An interesting experimental paradigm in which contiguity
and contingency could be further investigated is the countermirror sensory-motor training paradigm (Cook et al., 2010).
In this paradigm, previously established associations between
motor and sensory events are manipulated by repeatedly pairing the observation of an action with the execution of another
action. One typically finds (e.g., by measuring neural responses,
or reaction times) that the original sensory-motor association
gets weakened, depending on the principles of contingency and
contiguity. This paradigm has been applied to visual-motor learning processes, but not yet to auditory-motor learning processes.
However, the paradigm offers unique possibilities to study for
instance how counter-mirror training can alter auditory-motor
links established in musical instrument playing.
Action effects on music perception
to play a musical instrument, auditory-motor linkages are developed as a result of that training (Pascal-Leone, 2001; Bangert and
Altenmüller, 2003; Lotze et al., 2003; Lahav et al., 2005; D’Ausilio
et al., 2006; Lahav et al., 2007; Hyde et al., 2009; Herholz and
Zatorre, 2012). Also studies have shown that during passive music
listening, trained musicians exhibit stronger auditory-motor couplings compared to non-musicians (Haueisen and Knösche, 2001;
Gaser and Schlaug, 2003; Baumann et al., 2007). This supports
the idea that auditory-motor linkages are established by intensive training which involves long-term skill acquisition and the
repetitive rehearsal of the same skills (Brown and Palmer, 2012,
2013).
It is evident that sensory-motor association processes are
important for voluntary action control, as in musical instrument
performance (Hommel, 1997, 2003; Elsner and Hommel, 2001).
However, more important in the light of the present paper is
the idea that sensory-motor relationships, and the integration of
these relationships into internal models, may influence perceptual
processes and accordingly shape the musical mind. In the following sections, we will discuss empirical evidence demonstrating
that sensory-motor association learning, with musical instrument
training as a special case, may lead to action-based effects on
auditory perception.
3. EMPIRICAL EVIDENCE: A REVIEW
3.1. INVERSE MODEL: PERCEPTION → ACTION
2.2. MUSICAL INSTRUMENT LEARNING
Learning to play an instrument can be considered a special, highly
illustrative case of sensory-motor association learning in which
action and perception become intricately interwoven. The act
of playing an instrument can be considered as a goal-directed,
intentional act (Dalla Bella and Palmer, 2011). Ultimately, the
goal of playing a musical instrument is to produce a certain
sound. However, in order to reach that goal, one first needs
to obtain knowledge about the relationship between the actions
afforded by the musical instrument, and the auditory consequences of these actions. This knowledge is gradually acquired
by exploring and manipulating the possibilities afforded by the
instrument using (at first) arbitrary actions that lead to (at first)
unexpected auditory events (Hommel, 2003). In that process of
exploration and interaction, one systematically and repeatedly
associates performed actions with heard sounds, and internal
models are developed as a result, capturing the relationship
between actions and sound. For example, in the case of the
piano, one starts to understand that the key-to-pitch mapping
is functionally organized (left-right motion corresponds to lowhigh pitch), or that depressing the sustain pedal creates a legato
effect. At that point, playing a musical instrument may become
a goal-directed act, in the sense that performers have the ability to intentionally produce certain sounds by performing certain
actions. Additionally, it must be noted that the process of exploration in which action and perception mutually interact, is a
continuous process throughout the life of a music performer.
It incorporates aspects of creativity, intuition and surprise, and
can in itself be a “raison d’être” of playing an instrument (cf.
Sudnow, 1978).
A large body of empirical studies exist that support these ideas.
For example, it has been shown that when people are trained
Frontiers in Psychology | Theoretical and Philosophical Psychology
Inverse models enable us to predict the motor commands that
are required to achieve a desired sensory state. It is obvious that
this is of utmost importance when playing a musical instrument.
But inverse models hold an important role in music perception
as well, as they allow to predict and simulate the physical aspects
of motion and space implied in the music. There is ample evidence that merely listening to sounds or music automatically
triggers motor responses, as a function of their previously established associations [motor resonance (Schütz-Bosbach and Prinz,
2007), perceiving action (Hurley, 2008), etc.]. This has been
shown in neurophysiological studies (Haueisen and Knösche,
2001; Bangert and Altenmüller, 2003; Gaser and Schlaug, 2003;
Lahav et al., 2005, 2007; D’Ausilio et al., 2006; Baumann et al.,
2007; Chen et al., 2008). Additionally, results from behavioral
studies show that motor responses to sounds are typically faster
when the specific sounds and actions have been repeatedly and
consistently paired on previous occasions (Elsner and Hommel,
2001; Rusconi et al., 2006; Lidji et al., 2007; Trimarchi and
Luzzatti, 2011; Stewart et al., 2013a,b). These findings provide
support for the idea that an action becomes automatically activated (or, primed) as a result of the mere perception of the
auditory consequences normally associated with that action. 1
Other studies have focused on overt body movements that people
make in response to music for music presented in visual form, or
via motion imagery (Eitan and Granot, 2006; Leman et al., 2009;
Caramiaux et al., 2010; Godøy, 2010; Kozak et al., 2012; Bernardi
et al., 2013; Küssner, 2013; Lotze, 2013). These studies show that
people can consistently translate acoustic properties of sound and
music into body movements, although Küssner (2013) reports
1 see
Cox and Hasselman (2013) for some critical remarks on typical effectpriming studies.
January 2014 | Volume 4 | Article 1008 | 4
Maes et al.
that musicians are more consistent (i.e., less varying) in visualizing sound and music by means of drawings. More important in
the scope of the present article is the idea that the power of music
to induce body movements in listeners implies that merely listening to music becomes a kinaesthetic experience. Musical groove is
a relevant example of a musical quality that induces body movements in listeners (Janata et al., 2012; Stupacher et al., 2013).
The notion of music-induced body movement may be related to
two ideas showing how inverse models, and the related concept
of motor resonance (or, motor simulation), can shape people’s
engagement with music and by extension the “musical mind.”
First, the recruitment of the body into the process of music
listening causes a connection to be made between the music and
the expressive qualities inherent to the movements that the music
induces. The human body acts thereby as a mediator between
physical phenomena (sensory and motor processes) and subjective, mental states (Leman, 2007). An interesting model to capture
the subtle qualities of movement expressivity is the Effort/Shape
model that originated in the Laban Movement Analysis (LMA)
method (Laban, 1947; Laban and Ullmann, 1966). This model
is particularly appropriate, as it provides an integrated conceptual system connecting a set of physical movement properties
with expressive qualities (e.g., weight, flow, space, time, etc.). The
model has been used in research to show how music-induced
body movements correlate with verbal descriptors used by people
to describe their perception of the music (Maes et al., 2014).
Second, it is interesting to note that music-induced body
movements may instigate a sense of imagined participation with
the production of the sound. This idea of imagined participation is addressed in a broad range of musicological studies with
different terminology, such as imagined activity (Maus, 1988),
kinaesthetic empathy (Mead, 1999), imaginary agency (Levinson,
2006), simulated control (Leman, 2007), and active perception
(Krueger, 2009). What these accounts have in common is their
reference to a direct, sensory-motor engagement with music,
to how music literally “moves” people, and to how people feel
immersed in, and resonate with, the physical sound energy. In
that sense, motor resonance may create the illusion of taking
part in the actual skillful production of the music, which would
be impossible in real life. Musical motion, however, is not limited to purely physical movements of the human body. Schubotz
(2007) provides an answer to the question of how people can simulate or anticipate events that could not be readily reproduced by
their own motor system (e.g., rhythm of ocean waves, the flight
of a mosquito, or an unfolding sequence of abstract stimuli on a
computer screen). Schubotz demonstrates and explains that even
abstract events—including auditory events—recruit our motor
system (in particular the premotor cortex and its parietal projection areas) in order to support simulation and prediction
processes (see also Southgate, 2013). Accordingly, the micro and
macro dynamics and subtleties inherent in the musical textures
and structures, as for instance in the “Clocks and Clouds” (1973)
of György Ligeti or in electronic music productions (e.g., Infected
Mushroom, Aphex Twin, etc.), can evoke a fascinating continuum
of spatial imagery and motion, with which the listener may float
along. Accordingly, motor resonance may generate an experience
of flow, being a state of heightened focus and immersion, typically
www.frontiersin.org
Action effects on music perception
accompanied with intense feelings of enjoyment and creativity
(Csikszentmihalyi, 1988). This aspect of motor resonance is an
essential component of musical aesthetic experiences and is fundamental for shaping the “musical mind.” Additionally, it may be
a factor that explains the ability of music to alter people’s experience of space and time (Schäfer et al., 2013), and to contribute to
people’s general well-being (Croom, 2011).
3.2. FORWARD MODEL: ACTION → PERCEPTION
As explained, forward internal models represent an information
flow from action to perception, in the sense that they allow the
prediction of the likely sensory outcome of a planned or executed action [cf. “perceptual resonance” (Schütz-Bosbach and
Prinz, 2007), “active perception” (Hurley, 2008), etc.]. Research
has pinpointed the cerebellum as a crucial locus for internal
forward models (Wolpert et al., 1998; Blakemore et al., 2001;
Knolle et al., 2012a; Ebner, 2013), presumably in interaction with
other brain structures [e.g., prefrontal areas (Lappe et al., 2013)].
In this context, it is important to note that different predictive
mechanisms exist which are supported by different brain systems.
O’Reilly et al. (2013) for example differentiate between statistical and dynamic predictive models. Statistical models capture the
stochastic probability that two or more events are associated—
for example an action event and a reward or sensory event—and
are developed over a history of discrete events. Alternatively,
in dynamic forward models, the relation between two events is
deterministic and predictions are computed via explicit reference
to pre-learned environmental dynamics.
Studies have shown that predictive models are important for
motor control (Wolpert et al., 1995; Hommel, 1997), as well as
for the processing of sensory information coming from the external environment (Halász and Cunnington, 2012). In the present
study, we focus on the latter in the context of auditory perception.
We will discuss how sensory predictions generated by forward
models may influence the perception of sound and music. It will
be shown that sensory predictions can either attenuate, facilitate,
or disambiguate auditory perception (cf. Halász and Cunnington,
2012).
3.2.1. Attenuation
Performing an action for which one can predict the sensory
consequences attenuates the perception of the actual sensory outcome, as reflected in self-reports and neuronal responses. In the
domain of auditory perception, this phenomenon was first studied in speech production (Houde et al., 2002; Heinks-Maldonado
et al., 2006). Later on, studies appeared in which the phenomenon
of motor-induced suppression (MIS) was studied with tones generated by keypresses. Despite the fact that the tones and the
actions that produce them (i.e., action-to-pitch mapping) are
highly simple, a parallel can be drawn with musical instrument
playing, like playing the piano, trumpet, etc.
A study conducted by Aliu et al. (2009) demonstrates that
the auditory response to tones generated by self-produced keypresses is attenuated relative to the response following passive
listening to the same tones. However, because self- and externallygenerated tones were presented in separate blocked conditions,
it could not be ruled out that the observed attenuation effect
January 2014 | Volume 4 | Article 1008 | 5
Maes et al.
was modulated by differences in contextual task demands (e.g.,
allocation of attention, arousal, etc.). To clarify this matter, Baess
et al. (2011) mixed self- and externally-generated tones within
blocks. The results of this study yielded an even larger attenuation effect for self-generated tones than that observed in the
blocked condition. Also, Timm et al. (2013) conducted a study
to further investigate the relationship between attention and the
effects of motor prediction in perceiving auditory stimuli. The
study adapted the mixed paradigm of Baess et al. (2011) and additionally incorporated different conditions in which attention was
allocated to either the sound, the motor act, or to visual stimuli. Findings of this study demonstrated that an attenuation effect
for self-generated sounds was independent from the allocation of
attention. Other studies investigated whether the attenuation of
auditory action effects occurs when actions are merely observed,
instead of being self-generated. Sato (2008) hypothesized that, if
there is a human mirror neuron system that codes a bidirectional
association between action execution and action perception, then
the mere observation of (well-learned) actions leading to a certain
auditory event should bring about a similar auditory attenuation effect as when the action is self-generated. The results of this
study confirmed this hypothesis, as similar auditory attenuation
was observed for self-generated and merely observed soundproducing actions. However, this finding was later contradicted in
a study by Weiss and Schütz-Bosbach (2012), using a comparable
experimental protocol as in Sato (2008). They compared auditory
action effects for self-generated actions, observed unanticipated
actions, and observed anticipated actions. The results showed that
the attenuation of a sound is significantly higher when the soundproducing action is self-generated compared to merely observed.
Moreover, this effect was shown to be independent of whether the
observed action could be anticipated or not. This finding raises
questions about the role that forward internal models play in
the prediction mechanisms underlying action effects on auditory
attenuation (cf. Sato, 2008). More research is needed to clarify this
point. In a last study we address here, Knolle et al. (2012b) examined whether auditory attenuation is a function of the degree of
predictability of the self-generated sound. The result of this study
indicated a lowering of the attenuation effect when self-generated
sounds deviate from the expected outcome.
Together, these and similar studies (Baess et al., 2008; Hughes
et al., 2013a,b; Jones et al., 2013; Loehr, 2013; Sanmiguel et al.,
2013) provide strong evidence in support of the existence of an
internal, motor-based prediction mechanism that can modulate
auditory perception. Planning or executing an action causes a
copy of the motor command to be made (i.e., “efferent copy,”
or “corollary discharge”), which enables a prediction of the auditory outcome of that motor command. A comparison between
the prediction and the actual auditory input (“reafference input”)
leads to a small prediction error, and subsequently to a minimal response in the auditory cortex reflecting an attenuated
perception (Aliu et al., 2009). This mechanism enables to discriminate between auditory inputs that are a consequence of
our own actions and those that reach us from the external
world. It is important to consider that this mechanism requires
(learned) internal models about the relationship between sensory
and motor representations. Only recently, studies have started
Frontiers in Psychology | Theoretical and Philosophical Psychology
Action effects on music perception
to unveil the neural substrates of motor-based sensory prediction (Nelson et al., 2013; Roussel et al., 2013). However, more
research is needed in order to obtain a full picture of the neural
mechanisms underlying the action effect of auditory attenuation.
3.2.2. Facilitation
Manning and Schutz (2013) examined to what extent “moving to
the beat” objectively improves timing perception. They presented
participants with sequences of 16 isochronous tones divided into
groups of four followed by a probe tone. In the last group, the
second, third, and fourth “tones” were silent (i.e., timekeeping
segment). The probe tone was “on-time” (i.e., sounding after
the same inter-onset interval), slightly early, or slightly late. The
task of the participant was to judge whether the final probe tone
sounded “on-time.” In one condition, participants were asked to
tap along with the beat, while they remained still in the other
condition. The results show that late offsets were better detected
when participants could move during the timekeeping segment.
Additionally, it was found that “better” tappers (i.e., less variability) performed better on the detection task overall. In general,
these findings confirm that movement may improve time perception. Iordanescu et al. (2013) obtained similar results using a standard temporal-bisection paradigm. Participants were presented
with sequences of three brief clicks with the location of the second click randomly varied. Participants had to judge whether the
second click was temporally closer to the first or the third click. In
the “active” condition, participants initiated each trial themselves
by pressing the space bar, while trials were externally generated in
the “passive” condition. Again, in line with the results of Manning
and Schutz (2013), people in the active condition demonstrated a
higher auditory sensitivity to temporal intervals. Moreover, it was
shown that this effect was not attributable to the tactile sensation from a keypress. It is interesting to note that the finding that
body movement can enhance time perception has been picked up
by research in the domain of human-computer interaction (HCI)
design. Maes et al. (2012, 2013) present a dance application and
a music conducting application aiming to enhance users’ understanding of temporal musical structures by teaching them how
to articulate these temporal structures into corresponding body
movements (dancing, conducting).
In another study, Brown and Palmer (2012) investigated how
motor and auditory learning contribute to auditory memory
for music. Pianists were asked to learn melodies on a Musical
Instrument Digital Interface (MIDI) piano keyboard in each of
four conditions (auditory only, motor only, strongly coupled
auditory-motor [i.e., normal performance], or weakly coupled
auditory-motor [i.e., performing along with auditory recordings
(acoustically similar or varying) without hearing their own feedback]). After learning, participants heard melodies (half target,
half foils) in a subsequent recognition test and were instructed
to indicate which melodies they had encountered in the learning conditions. It was found that motor learning (combined with
strongly coupled auditory learning) enhanced auditory recognition beyond auditory learning alone. Results were explained by
the ability of sensory-motor associations formed during learning to provide additional retrieval cues and to shape auditory
perception through mental simulation of action plans.
January 2014 | Volume 4 | Article 1008 | 6
Maes et al.
3.2.3. Disambiguation
Music may have a certain degree of ambiguity in terms of perceptual and/or affective content. As discussed below, studies indicate
that it is possible for a listener to disambiguate this content by
planning or executing body movements during listening. Forward
models provide an appropriate explanation for this disambiguation effect (Halász and Cunnington, 2012). The planning or
execution of body movements enables one to automatically predict the sensory consequences of these actions. Consequently,
these predicted sensory states can be projected onto the auditory or musical material, which may guide (i.e., disambiguate)
the corresponding perception. Some additional remarks need to
be made, however. First, planning body movements does not
only generate predictions of sensory states, but equally of subjective mental states related to affect and expressivity (e.g., valence,
arousal, etc.). In that sense, it is equally possible that subjective states are attributed to the music (Thompson et al., 2005;
Juchniewicz, 2008; Sedlmeier et al., 2011; Maes and Leman, 2013).
Second, auditory or musical material doesn’t necessarily need to
be ambiguous in order for body movements to guide our perception in a specific direction. Music presents the performer and
listener with a flood of different auditory cues and accents. Body
movements can help selectively direct attention to certain cues,
and accordingly to impose a certain structure onto the music.
According to Urista (2003); Pierce (2007), body movements can
help to isolate and explore musical elements as melody, beat, and
structural levels. Hence, cue selection (and, cue identification)
facilitated by body movement may refine music listening in general, and shape our perception and understanding of the music.
Third, studies show that merely observing body movements,
instead of actually planning or executing them, may equally influence perceptual and aesthetic judgments of the produced music
(Thompson et al., 2005; Schutz and Lipscomb, 2007; Juchniewicz,
2008). Fourth, it is possible that executed or observed body movements modulate auditory perception instantaneously, i.e., at the
moment one listens to the music (Thompson et al., 2005; Schutz
and Lipscomb, 2007; Juchniewicz, 2008; Repp and Knoblich,
2009; Sedlmeier et al., 2011). Additionally, it is also possible that
when one repeatedly pairs body movements to music, the resulting action-based effects on music perception may endure for a
longer period of time, in the sense that the specific way of perceiving music may retain when merely listening to the music
without the need to intentionally plan or execute the corresponding body movements (Phillips-Silver and Trainor, 2005, 2007;
Maes and Leman, 2013). So by sensory-motor associative learning
processes, music may become integrated with actions and more
importantly, with the sensory and affective states inherent to these
actions. It is a form of “evaluative conditioning” leading to effects
of disambiguation and cue selection (Juslin and Västfjäll, 2008;
Maes and Leman, 2013). Moreover, depending on the nature of
the learning process (e.g., duration, continuity, contingency, etc.),
these effects can be retained for different amounts of time.
In the following section, we discuss several studies that illustrate these effects of disambiguation and cue selection. PhillipsSilver and Trainor (2005, 2007) addressed the interaction between
body movement and the perception of musical rhythm. The procedures of the experiments conducted in these studies contained
www.frontiersin.org
Action effects on music perception
a training phase and a subsequent test phase. In the training phase, infants were passively bounced (Phillips-Silver and
Trainor, 2005), or adults bounced actively by bending their knees
(Phillips-Silver and Trainor, 2007) on every second (duple) vs.
every third (triple) beat of an ambiguous musical rhythm pattern. In the subsequent test phase, infants’ listening preferences
were tested for two auditory versions of the rhythm pattern (duple
and triple form) (Phillips-Silver and Trainor, 2005). In PhillipsSilver and Trainor (2007), the adults were asked to listen to two
auditory rhythms (duple and triple rhythm) and to select the one
they thought matched what they heard during the training phase.
The results showed that the preferences and interpretations were
oriented toward the auditory stimulus that matched the metrical
form of their movement training.
In a study by Naveda and Leman (2009) it was shown
that Samba music has a polymetric ambiguity, whereas Samba
dance patterns typically have binary tendencies. Accordingly, the
authors suggest that “perception of samba may be movementbased in the sense that through self-movement (of the dancer
in response to music) musical patterns get rhythmically
disambiguated.”
In a study by Sedlmeier et al. (2011), it was shown that real or
imagined body movements during music listening may codetermine music preferences. The experimenters activated or inhibited
specific muscles of the participants whose innervations have been
shown to be associated with positive and negative emotions. This
was realized by instructing the participants to perform three specific kinds of body movements or actions (activating/inhibiting
“smiling muscles,” vertical/horizontal head movements, and flexion/extension of the arms). Activation of the positively associated
muscle groups during listening to music led to higher preference
ratings for that music than activation of the negatively associated
ones. This suggests that body movements, both real and imagined, may play an important role in the development of music
preferences.
Su and Pöppel (2012) tested the hypothesis that the use of body
movement is not merely a reaction to hearing rhythmic input,
but could actively assist the processing of temporal structures in
auditory events. They suggest that a self-initiated movement frequency, which is not tuned-in at first, could be attracted to one
of the underlying periodicities of the presented sequence. Doing
so guides the listener to start “hearing” the pulse at that level,
forming a positive audio-motor feedback loop. The authors show
that in the absence of overt movement, by contrast, this tuning process must then rely on the internal motor entrainment
and/or the ability to analyze the sequence. Unlike musicians, nonmusicians seemed to lack an effective internal motor simulation
that entrained to the pulse when it was not regularly present at
the rhythmic surface, nor did they possess additional musical
knowledge as a compensatory strategy.
A study by Iversen et al. (2009) investigated how the perception of a simple rhythmically ambiguous phrase (i.e., a repeating
series of two tones followed by a rest) depends upon its intrinsic metrical interpretation. Participants were asked to mentally
place the downbeat on either the first or the second tone of
the rhythmical phrase. Using magnetoencephalography (MEG) it
was shown that different metrical interpretations evoked different
January 2014 | Volume 4 | Article 1008 | 7
Maes et al.
neural responses, specifically in the upper beta range (20–30 Hz).
This led the authors—given the suggested role of beta in motor
processing—to the hypothesis that the motor system influences
metrical interpretation of sound, even in the absence of overt
movement. In another study, Maes and Leman (2013) addressed
the question of whether expressive body movements can condition children’s perception of musical expressiveness. They trained
children with a happy or a sad choreography in response to music
that had an expressively ambiguous character. Afterwards, the
children’s perception of musical expressiveness in terms of valence
and arousal was assessed. The results suggested that the expressive qualities of the movements they learned to associate with the
music had a significant impact on how children perceived musical
expressiveness.
In a study by Repp and Knoblich (2009), participants were
asked to play pairs of octave-ambiguous (Shepard) tones which
were a tritone apart. Although each tone of a pair is characterized by a specific pitch class (e.g., C - F#), they are ambiguous in
pitch height. Participants were asked to play the pairs of tones by
pressing corresponding piano keys or computer keyboard keys,
either in left-to-right or right-to-left direction. Consecutively,
they had to judge whether each pitch interval was rising or
falling. Results showed that the participants gave significantly
more “rising” responses when the order of keypresses was leftto-right than when it was right-to-left. Moreover, this effect
was larger for pianists compared to non-pianist musicians, most
likely because the specific pitch-to-sound mapping is stronger in
pianists (Experiment 1). Additionally, the same effect was found
when pianists merely observed another person pressing keys on a
piano keyboard (Experiment 2).
Other studies have shown that merely observing musician’s
body movements can alter perceptual and aesthetic judgments of
the produced music. Schutz and Lipscomb (2007) examined to
what extent visual information of a marimba player’s gestures can
influence the perception of the duration of the produced tone. For
the experiment, video recordings were made of a marimba player
performing a series of tones using two stroke types (“long” gesture
and “short” gesture). The tones that were produced by both stroke
types were acoustically indistinguishable. The visual and auditory
components were separated from each other and fully crossed
in order to create realistic musical stimuli. Then, participants
were asked to indicate perceived tone duration by means of a
101-point slider. In an audio-only condition, no significant differences occurred between the ratings. However, in the audio-visual
condition, participants rated the tones produced with “long”
gestures as significantly longer than the tones produced with
“short” gestures. In another study Thompson et al. (2005) showed
that facial expressivity and expressive hand gestures of music
performers (i.e., vocal and guitar performance) can influence listeners’ auditory perception of musical dissonance, melodic interval size, and affective valence. Similar findings are provided by
Juchniewicz (2008), showing that the type of physical movement
exhibited by a piano player while performing a musical excerpt
(i.e., “no movement,” “head and facial movement,” and “full
body movement”) alters listeners’ judgments of the piano performance in terms of phrasing, dynamics, rubato and overall musical
performance.
Frontiers in Psychology | Theoretical and Philosophical Psychology
Action effects on music perception
3.3. MOTOR DISORDERS
The previously discussed action-based effects on auditory perception were rooted in learned auditory-motor associations. Apart
from that, another category of action-based effects can be distinguished. Several studies have shown that motor dysfunction
leads to considerable changes in individuals’ perception and
recognition of auditory and musical features. Pazzaglia et al.
(2008) claimed a causative link between auditory recognition and
action execution. Working with apraxia patients (limb apraxia,
buccofacial apraxia, or both), they showed that deficits in performing gestures are causally linked to the patients’ inability
to recognize these gestures by their mere sounds. In the study,
apraxia patients were asked to listen to a sound and then choose
from among four pictures the one corresponding to the heard
sound. Limb and buccofacial apraxia patients were impaired in
recognizing sounds linked to respectively limb and buccofacial
human actions. The authors advocated that lesions in frontal and
parietal brain areas, which are actively associated with deficits
in execution tasks, were responsible for the observed gesturecomprehension deficits. Also, studies indicate that the perception
of musical features is impaired by motor dysfunctions. Beste
et al. (2011) demonstrated effects of movement deterioration on
rhythm processing in Huntington’s disease patients. While listening to music, patients exhibited weaker activations overall in brain
areas involved in the assessment of musical rhythms (cerebellar
structures). Also, a study of Parkinson’s disease patients by Grahn
and Brett (2009) found that basal ganglia dysfunction results in
an impairment of the processing of rhythms that have a beat.
However, as the authors discuss, it cannot be excluded that pathological factors other than movement deterioration may contribute
to impaired rhythm processing. For instance dopamine depletion,
typical for Parkinson’s disease, has been shown to affect emotional
processing (Lotze et al., 2009), which may further modulate the
processing of rhythms. In another study by Lucas et al. (2013),
impaired temporal information processing in Parkinson’s disease
patients has been ascribed to a deficit in the process of sensorimotor integration. These and other studies (see e.g., Grahn, 2012
for a review) demonstrate that rhythm perception involves a close
link between auditory and motor processes. The existence of such
links has been exploited for motor rehabilitation purposes in the
domain of Parkinson’s disease, Huntington’s disease, and stroke.
In this context, musical activities involving movement (control)
and rhythm (perception) have been shown to improve general
motor performance in Parkinson’s disease patients (Nombela
et al., 2013a,b) and stroke patients (Altenmüller et al., 2009).
It would be interesting to investigate further to what extent
improvements in motor skill benefit performance on perceptual
tasks.
4. DISCUSSION
Traditionally, body movements—whether performed by a music
performer or by a listener—were considered as the mere output of internal cognitive processes that involved a system of
symbolic representations. Only recently, empirical evidence has
begun to appear indicating that the human motor system and its
actions may actually modulate people’s experience, perception,
and understanding of sound and music. The present article
January 2014 | Volume 4 | Article 1008 | 8
Maes et al.
was intended to provide a theoretical framework in which
action-based effects on auditory perception may be understood.
Additionally, the article serves as a review in which we investigate
how the theory applies to recent empirical findings. The presented
theoretical framework is centered around the common coding
theory (Prinz, 1990; Hommel et al., 2001). The basic assertion of
this theory is that the planning or execution of an action recruits
the same sensory-motor brain areas as the mere perception of the
sensory consequences of that action. We have argued that associative learning, in which actions and sensory states are repeatedly
experienced together, are of crucial importance in order for action
and perception to become integrated, and to form so-called internal models. These internal models contain inverse and forward
components. Inverse models allow incoming sensory information to activate the motor codes associated with the production
of that sensory state (cf. direct-matching hypothesis Rizzolatti
et al., 2001). In contrast, forward models allow the sensory outcomes to be predicted from planned actions (Waszak et al., 2012).
The combination of inverse and forward models regulate goaldirected motor control (Wolpert et al., 1995; Hommel, 1997),
as well as the processing of sensory information coming from
the external environment (Halász and Cunnington, 2012). We
explained that both inverse and forward models contribute to
action-based effects on auditory perception. Inverse models allow
that mere listening to music results in the activation of motor
codes, which is often manifested in overt movement responses
(cf. motor simulation, motor resonance, action mirroring, etc.).
These body movements are experienced and understood as intentionally, expressively, and semantically meaningful, and cause the
music to be experienced and understood accordingly. Forward
models have an impact on music perception in a different way.
They allow us to make predictions about the auditory outcomes
of planned or executed actions, which guide and shape the perception of sound and music. Predictions may either attenuate,
facilitate, or disambiguate the perception of sound and music.
Together, these findings show that the human motor system and
its actions have an impact on music perception and cognition. It
is tempting to conclude based on this evidence that the “musical mind” is fundamentally embodied. However, according to
Wilson and Golonka (2013), the assertion that (music) cognition is embodied has more radical and far-ranging implications.
They claim that “embodiment is not simply another factor acting on otherwise disembodied cognitive processes.” This would
retain the traditional Cartesian view that the brain is in control and, in the case of people’s engagement in musical activities,
literally “runs the show.” Instead, “radical embodiment” encompasses a perspective on the body, the mind, and the environment as substantial elements of a dynamical system (Chemero,
2009). In essence, the term “dynamical system” points to a system of elements which are coupled, mutually interactive, and
evolve over time (Thelen and Smith, 1998). An important feature of dynamical systems is the ability to self-organize. Order
and coherence appear out of the mutual interactions of the elements of the system without the use of explicit instructions,
representations, or symbols. The dynamical system approach can
be applied to motor control and development (Turvey, 1990;
Kelso, 1995; Thelen and Smith, 1998; Warren, 2006), as well as to
www.frontiersin.org
Action effects on music perception
cognition (Port and Van Gelder, 1995; Van Gelder, 1998; Beer,
2000; Chemero, 2009; McClelland et al., 2010; Shapiro, 2013).
Music seems especially relevant as many musical activities—
e.g., music production, dance, music listening—provide an ecological setting in which the intrinsic dynamics of action and
perception can be studied (Bader, 2013a,b). Moreover, it is interesting to note that people’s engagement with music involves
not only sensory and motor components but also other components, such as “introspection”—referring to internal states
that include affect, motivation, intentions, metacognition, etc.
(Barsalou, 2009)—and “social interaction.” Currently, research
on internal models focuses almost exclusively on sensory and
motor processes. However, to explain people’s interaction with
music, and by extension with the world in general, it is necessary to include aspects of introspection and social interaction into
theories on internal models. The integration of these aspects into
the present theoretical framework can deepen our understanding
of music, and of the musical mind as fundamentally embodied. In the following paragraphs, we briefly discuss these two
components.
4.1. MUSICAL EXPRESSIVITY
An important aspect of people’s engagement with music—
whether in listening to music or the actual production of music—
is musical expressivity. The musical elements that are said to constitute musical expressivity are manifold: dynamics, articulation,
touch, phrasing, vibrato, etc. In the case of music production,
musical expressivity is often—but not exclusively—related to the
contents of the composition, and the main task of the musician is
to render the composition into sound. Of course there is always
a certain degree of interpretation and expressivity from the performer’s side. Music performance however does not necessarily
rely on a pre-composed score, as in the case of improvisation or
jam sessions, where music may be created for the sake of exploring different sounds, rhythms, dynamics, etc. Apart from whether
music is the result of playing a composition or improvisation,
what is conspicuous about many of the various elements contributing to musical expressivity is that they directly relate to their
physical origin, namely the body movements that produced the
music (Repp, 1993; Shove and Repp, 1995; Johnson, 1997; Godøy,
2003; Leman, 2007; Cox, 2011). Accordingly, musical expressivity
can be said to appeal to, at least to some extent, kinaesthetic sensations related to the effort and shape of body movements (Laban,
1947; Laban and Ullmann, 1966). Further, this kinaesthetic sensitivity may be associated with subjective phenomena like feeling, emotion, intentionality, etc. (Leman, 2007; Cochrane, 2010;
Sievers et al., 2013). In that sense, the human body has been considered as a mediator between sensory and motor processes and
mental states (Leman, 2007). A similar role has been attributed to
the body in the context of music listening. A listener is assumed
to be able to decode—i.e., identify, imagine, or even physically
render—the elements of musical expressivity that relate to physical motion and space, based on their own action repertoire and
notion of space. This kinaesthetic sensitivity may be related to
subjective mental aspects of feeling, emotion, intentionality, etc.
In the same way as planning or executing an action enables people
to make predictions of the sensory consequences of that action, it
January 2014 | Volume 4 | Article 1008 | 9
Maes et al.
is possible to make predictions of the consequences on a mental level (e.g., feeling, emotion, intentionality). Accordingly, it is
reasonable to assume that the predictions of mental states modulate the perception of musical expressivity. It is only recently that
empirical support for this idea emerged (Sedlmeier et al., 2011;
Maes and Leman, 2013). Also, it has been shown that the visual
observation of performers’ body movements influences people’s
perception of musical expressivity (Davidson, 1993; Thompson
et al., 2005; Juchniewicz, 2008). These findings provide support
for including expressivity in theories of forward modeling applied
to music perception and cognition. According to current theories
of internal models, we have reason to believe that the relationship
between mental states and action works in the opposite direction as well (cf. inverse models). In that sense, a subjective state
coupled to music is assumed to modulate motor responses to
music. Support for this idea is given in a study of Van Dyck et al.
(2013). According to the current view, internal models guide goaldirected behavior as well as sensory processing. In that sense,
internal models are the basic constituents of people’s interaction
with the outside world. We advocate that this view should be
broadened by integrating other aspects of introspection (affect,
motivation, intentions, metacognition, etc.). Musical behaviors
provide opportunities to study interactions between sensory,
motor and introspective processes, and the way these components
become associated with each other. The current view of embodied
music cognition considers introspection as a result of motor simulation processes (Leman, 2007). In other words, music induces
body movements, which consequently trigger subjective aspects
of feeling, emotion, intentionality, etc. We advocate that the relationship between body and mind may be bidirectional, as aspects
of introspection may also influence motor responses to music.
Action effects on music perception
(predict) the sensory consequences of one’s own and other’s
actions.
Our discussion of the components of “introspection” and
“social interaction” indicates that musical activities involve a
high-dimensional dynamical system in which the body, the mind,
and the external environment are continually and mutually interacting. In the case of musical instrument playing, music can
be considered as the result of a dynamical interaction between
the musicians’ motor and sensory system, the constraints and
opportunities of the pre-composed musical notation, the musical instruments and the social environment, and the musicians’
intentions, personality, mental states, etc. The system in which
these components interact is an open system, in the sense that
no individual component has causal priority in generating the
music (Thelen and Smith, 1998). It is possible, however, that
the weight of the individual components on the produced sound
varies depending on the specific musical activity (e.g., musical
improvisation, historical informed music performance, jam session with an emphasis on social interaction, etc.). Similarly, music
listening can be considered as a dynamical process, in which the
experience, the perception, and the understanding of music is
guided and shaped by the intrinsic dynamics of the body, the
mind, and the external environment. In conclusion, adopting a
fundamental embodied approach to music cognition requires us
to consider music performance—involving motor coordination,
control, and development—and music cognition as dynamical
processes. The integration of theories on internal models and
theories on dynamical systems can thereby enhance our understanding of how our body, mind, and the external environment
interact in our engagement with the act of music.
ACKNOWLEDGMENTS
4.2. SOCIAL INTERACTION
In daily life, much of what we do and experience happens in a
social context. A paramount example is people’s engagement with
music, as in music ensemble playing (Bastien and Hostager, 1988;
Seddon, 2005), or when people dance together in a club or festival. These activities can be considered as forms of joint action
involving coordinated actions, shared intention, shared attention, shared representations, etc. (Keller, 2008; Goebl and Palmer,
2009; Loehr and Palmer, 2011; Obhi and Sebanz, 2011; Pacherie,
2012; Phillips-Silver and Keller, 2012). Joint action in the context of music playing and dance has been shown to promote
social behavior (Kirschner and Tomasello, 2010) and to establish
a heightened sense of agency and a sense of we-ness (Pacherie,
2012). Also, studies show that the social context may modulate
people’s experience and perception of music (Egermann et al.,
2011; Liljeström et al., 2012). Currently, a major line of research is
devoted to the study of joint action in order to unveil the underlying mechanisms. Accumulating evidence suggests that these
mechanisms are similar to the ones involved in individual voluntary motor control and information processing. Accordingly,
internal models containing an inverse and forward component
may explain how people manage to dynamically adapt to changes
in each other’s behavior. Inverse models are important for rendering desired joint action sensory outcomes into particular action
plans. Supplementary forward models facilitate to anticipate
Frontiers in Psychology | Theoretical and Philosophical Psychology
This work was funded by a Natural Sciences and Engineering
Research Council of Canada (NSERC) grant 298173 to C. Palmer
and by the Methusalem project on ‘Embodied music cognition
and mediation technologies for cultural and creative applications’
to M. Leman.
REFERENCES
Aliu, S. O., Houde, J. F., and Nagarajan, S. S. (2009). Motor-induced suppression of the auditory cortex. J. Cogn. Neurosci. 21, 791–802. doi:
10.1162/jocn.2009.21055
Altenmüller, E., Marco-Pallares, J., Münte, T., and Schneider, S. (2009). Neural
reorganization underlies improvement in stroke-induced motor dysfunction by music-supported therapy. Ann. N.Y. Acad. Sci. 1169, 395–405. doi:
10.1111/j.1749-6632.2009.04580.x
Bader, R. (2013a). Nonlinearities and Synchronization in Musical Acoustics
and Music Psychology, Vol. 2 of Current Research in Systematic Musicology.
Heidelberg: Springer.
Bader, R. (2013b). “Synchronization and self-organization as basis of musical performance, sound production, and perception,” in Sound-perceptionperformance, Vol. 1 of Current Research in Systematic Musicology, ed R. Bader
(Heidelberg: Springer), 3–42.
Baess, P., Horváth, J., Jacobsen, T., and Schröger, E. (2011). Selective suppression of
self-initiated sounds in an auditory stream: an ERP study. Psychophysiology 48,
1276–1283. doi: 10.1111/j.1469-8986.2011.01196.x
Baess, P., Jacobsen, T., and Schröger, E. (2008). Suppression of the auditory N1 event-related potential component with unpredictable self-initiated
tones: evidence for internal forward models with dynamic stimulation. Int. J.
Psychophysiol. 70, 137–143. doi: 10.1016/j.ijpsycho.2008.06.005
January 2014 | Volume 4 | Article 1008 | 10
Maes et al.
Bangert, M., and Altenmüller, E. (2003). Mapping perception to action in piano
practice: a longitudinal DC-EEG study. BMC Neurosci. 4:26. doi: 10.1186/14712202-4-26
Barsalou, L. W. (2009). Simulation, situated conceptualization, and prediction.
Philos. Trans. R. Soc. B Biol. Sci. 364, 1281–1289. doi: 10.1098/rstb.2008.0319
Bastien, D. T., and Hostager, T. J. (1988). Jazz as a process of organizational
innovation. Commun. Res. 15, 582–602. doi: 10.1177/009365088015005005
Baumann, S., Koeneke, S., Schmidt, C. F., Meyer, M., Lutz, K., and Jancke, L. (2007).
A network for audio–motor coordination in skilled pianists and non-musicians.
Brain Res. 1161, 65–78. doi: 10.1016/j.brainres.2007.05.045
Beer, R. D. (2000). Dynamical approaches to cognitive science. Trends Cogn. Sci. 4,
91–99. doi: 10.1016/S1364-6613(99)01440-0
Bernardi, N. F., De Buglio, M., Trimarchi, P. D., Chielli, A., and Bricolo, E. (2013).
Mental practice promotes motor anticipation: evidence from skilled music
performance. Front. Hum. Neurosci. 7:451. doi: 10.3389/fnhum.2013.00451
Beste, C., Schüttke, A., Pfleiderer, B., and Saft, C. (2011). Music perception and
movement deterioration in Huntington’s disease. PLoS Curr. 3:RRN1252. doi:
10.1371/currents.RRN1252
Blakemore, S.-J., Frith, C. D., and Wolpert, D. M. (2001). The cerebellum is involved
in predicting the sensory consequences of action. Neuroreport 12, 1879–1884.
doi: 10.1097/00001756-200107030-00023
Brown, R. M., and Palmer, C. (2012). Auditory-motor learning influences auditory
memory for music. Mem. Cogn. 40, 567–578. doi: 10.3758/s13421-011-0177-x
Brown, R. M., and Palmer, C. (2013). Auditory and motor imagery modulate learning in music performance. Front. Hum. Neurosci. 7:320. doi:
10.3389/fnhum.2013.00320
Bubic, A., Von Cramon, D. Y., and Schubotz, R. I. (2010). Prediction, cognition and
the brain. Front. Hum. Neurosci. 4:25. doi: 10.3389/fnhum.2010.00025
Burger, B., Thompson, M. R., Luck, G., Saarikallio, S., and Toiviainen, P. (2013).
Influences of rhythm-and timbre-related musical features on characteristics
of music-induced movement. Front. Psychol. 4:183. doi: 10.3389/fpsyg.2013.
00183
Calvo-Merino, B., Glaser, D. E., Grèzes, J., Passingham, R. E., and Haggard,
P. (2005). Action observation and acquired motor skills: an fMRI study
with expert dancers. Cereb. Cortex 15, 1243–1249. doi: 10.1093/cercor/
bhi007
Caramiaux, B., Bevilacqua, F., and Schnell, N. (2010). “Towards a gesture-sound
cross-modal analysis,” in Gesture in embodied communication and humancomputer interaction (LNCS), Vol. 5934, eds S. Kopp and I. Wachsmuth (Berlin:
Springer-Verlag), 158–170.
Catmur, C. (2012). Sensorimotor learning and the ontogeny of the mirror neuron
system. Neurosci. Lett. 540, 21–27. doi: 10.1016/j.neulet.2012.10.001
Chemero, A. (2009). Radical Embodied Cognitive Science. Cambridge, MA: MIT
press.
Chen, J. L., Penhune, V. B., and Zatorre, R. J. (2008). Listening to musical
rhythms recruits motor regions of the brain. Cereb. Cortex 18, 2844–2854.
doi: 10.1093/cercor/bhn042
Chen, J. L., Penhune, V. B., and Zatorre, R. J. (2009). The role of auditory and
premotor cortex in sensorimotor transformations. Ann. N.Y. Acad. Sci. 1169,
15–34. doi: 10.1111/j.1749-6632.2009.04556.x
Cochrane, T. (2010). A simulation theory of musical expressivity. Austr. J. Philos.
88, 191–207. doi: 10.1080/00048400902941257
Cook, R., Press, C., Dickinson, A., and Heyes, C. (2010). Acquisition of automatic
imitation is sensitive to sensorimotor contingency. J. Exp. Psychol. Hum. Percept.
Perform. 36, 840–852. doi: 10.1037/a0019256
Cooper, R. P., Cook, R., Dickinson, A., and Heyes, C. M. (2012). Associative (not
Hebbian) learning and the mirror neuron system. Neurosci. Lett. 540, 28–36.
doi: 10.1016/j.neulet.2012.10.002
Cox, A. (2011). Embodying music: principles of the mimetic hypothesis. Music
Theory Online 17, 1–24.
Cox, R. F., and Hasselman, F. (2013). The case of Watson vs. James: effectpriming studies do not support ideomotor theory. PLoS ONE 8:e54094.
doi: 10.1371/journal.pone.0054094
Croom, A. M. (2011). Music, neuroscience, and the psychology of well-being: a
précis. Front. Psychol. 2:393. doi: 10.3389/fpsyg.2011.00393
Csikszentmihalyi, M. (1988). “The flow experience and its significance for
human psychology,” in Optimal Experience: Psychological Studies of Flow in
Consciousness, eds M. Csikszentmihalyi and S. Csikszentmihalyi (Cambridge,
UK: Cambridge University Press), 15–35.
www.frontiersin.org
Action effects on music perception
Dalla Bella, S., and Palmer, C. (2011). Rate effects on timing, key velocity, and
finger kinematics in piano performance. PLoS ONE 6:e20518. doi: 10.1371/journal.pone.0020518
D’Ausilio, A., Altenmüller, E., Olivetti Belardinelli, M., and Lotze, M. (2006). Crossmodal plasticity of the motor cortex while listening to a rehearsed musical piece.
Eur. J. Neurosci. 24, 955–958. doi: 10.1111/j.1460-9568.2006.04960.x
Davidson, J. W. (1993). Visual perception of performance manner in
the movements of solo musicians. Psychol. Music
21, 103–113.
doi: 10.1177/030573569302100201
Davidson, P. R., and Wolpert, D. M. (2005). Widespread access to predictive
models in the motor system: a short review. J. Neural Eng. 2, S313–S319.
doi: 10.1088/1741-2560/2/3/S11
Deiber, M.-P., Wise, S., Honda, M., Catalan, M., Grafman, J., and Hallett,
M. (1997). Frontal and parietal networks for conditional motor
learning: a positron emission tomography study. J. Neurophysiol. 78,
977–991.
Doyon, J., Bellec, P., Amsel, R., Penhune, V., Monchi, O., Carrier, J., Lehéricy,
S., and Benali, H. (2009). Contributions of the basal ganglia and functionally related brain structures to motor learning. Behav. Brain Res. 199, 61–75.
doi: 10.1016/j.bbr.2008.11.012
Ebner, T. J. (2013). “Cerebellum and internal models,” in Handbook of the
Cerebellum and Cerebellar Disorders, eds M. Manto, J. D. Schmahmann, F.
Rossi, D. L. Gruol, and N. Koibuchi (Heidelberg: Springer), 1279–1295.
doi: 10.1007/978-94-007-1333-8_56
Egermann, H., Sutherland, M. E., Grewe, O., Nagel, F., Kopiez, R., and Altenmüller,
E. (2011). Does music listening in a social context alter experience? A physiological and psychological perspective on emotion. Musicae Scientiae 15, 307–323.
doi: 10.1177/1029864911399497
Eitan, Z., and Granot, R. Y. (2006). How music moves: musical parameters and listeners images of motion. Music Percept. 23, 221–248.
doi: 10.1525/mp.2006.23.3.221
Elsner, B., and Hommel, B. (2001). Effect anticipation and action control. J. Exp.
Psychol. Hum. Percept. Perform. 27, 229–240. doi: 10.1037/0096-1523.27.1.229
Elsner, B., and Hommel, B. (2004). Contiguity and contingency in actioneffect learning.
Psychol. Res. 68, 138–154. doi: 10.1007/s00426-0030151-8
Fodor, J. A. (1975). The Language of Thought. Cambridge, MA: Harvard University
Press.
Gallese, V., Fadiga, L., Fogassi, L., and Rizzolatti, G. (1996). Action recognition in
the premotor cortex. Brain 119, 593–609. doi: 10.1093/brain/119.2.593
Gaser, C., and Schlaug, G. (2003). Brain structures differ between musicians
and non-musicians. J. Neurosci. 23, 9240–9245. doi: 10.1016/S1053-8119(01)
92488-7
Glenberg, A. M. (2010). Embodiment as a unifying perspective for psychology.
Wiley Interdiscipl. Rev. Cogn. Sci. 1, 586–596. doi: 10.1002/wcs.55
Godøy, R. I. (2003). Motor-mimetic music cognition. Leonardo 36, 317–319. doi:
10.1162/002409403322258781
Godøy, R. I. (2010). “Gestural affordances of musical sound,” in Musical Gestures:
Sound, Movement, and Meaning, eds R. I. Godøy and M. Leman (New York, NY:
Routledge), 103–125.
Godøy, R. I., and Leman, M. (2010). Musical Gestures: Sound, Movement, and
Meaning. New York, NY: Routledge.
Goebl, W., and Palmer, C. (2009). Synchronization of timing and
motion among performing musicians. Music Percept. 26, 427–438. doi:
10.1525/mp.2009.26.5.427
Grahn, J. A. (2012). Neural mechanisms of rhythm perception: current findings and future perspectives. Top. Cogn. Sci. 4, 585–606. doi: 10.1111/j.17568765.2012.01213.x
Grahn, J. A., and Brett, M. (2009). Impairment of beat-based rhythm discrimination in Parkinson’s disease. Cortex 45, 54–61. doi: 10.1016/j.cortex.2008.01.005
Grahn, J. A., and Rowe, J. B. (2013). Finding and feeling the musical beat: striatal
dissociations between detection and prediction of regularity. Cereb. Cortex 23,
913–921. doi: 10.1093/cercor/bhs083
Halász, V., and Cunnington, R. (2012). Unconscious effects of action on perception.
Brain Sci. 2, 130–146. doi: 10.3390/brainsci2020130
Haslinger, B., Erhard, P., Altenmüller, E., Schroeder, U., Boecker, H., and CeballosBaumann, A. O. (2005). Transmodal sensorimotor networks during action
observation in professional pianists. J. Cogn. Neurosci. 17, 282–293. doi:
10.1162/0898929053124893
January 2014 | Volume 4 | Article 1008 | 11
Maes et al.
Haueisen, J., and Knösche, T. R. (2001). Involuntary motor activity in
pianists evoked by Music Perception. J. Cogn. Neurosci. 13, 786–792. doi:
10.1162/08989290152541449
Heinks-Maldonado, T. H., Nagarajan, S. S., and Houde, J. F. (2006).
Magnetoencephalographic evidence for a precise forward model in speech production. Neuroreport 17, 1375–1379. doi: 10.1097/01.wnr.0000233102.43526.e9
Herholz, S. C., and Zatorre, R. J. (2012). Musical training as a framework
for brain plasticity: behavior, function, and structure. Neuron 76, 486–502.
doi: 10.1016/j.neuron.2012.10.011
Heyes, C. M. (2010). Where do mirror neurons come from? Neurosci. Biobehav. Rev
34, 575–583. doi: 10.1016/j.neubiorev.2009.11.007
Heyes, C. M., and Ray, E. D. (2000). What is the significance of imitation in animals?
Adv. Study Behav. 29, 215–245. doi: 10.1016/S0065-3454(08)60106-0
Hickok, G. (2009). Eight problems for the mirror neuron theory of action
understanding in monkeys and humans. J. Cogn. Neurosci. 21, 1229–1243.
doi: 10.1162/jocn.2009.21189
Hommel, B. (1997). Toward an action-concept model of stimulus-response compatibility. Adv. Psychol. 118, 281–320. doi: 10.1016/S0166-4115(97)80041-6
Hommel, B. (2003). “Acquisition and control of voluntary action,” in Voluntary
Action: Brains, Minds, and Sociality, eds S. Maasen, W. Prinz, and G. Roth
(Oxford, UK: Oxford University Press), 34–48.
Hommel, B., Müsseler, J., Aschersleben, G., and Prinz, W. (2001). The theory of
event coding (TEC): a framework for perception and action planning. Behav.
Brain Sci. 24, 849–878. doi: 10.1017/S0140525X01000103
Houde, J. F., Nagarajan, S. S., Sekihara, K., and Merzenich, M. M. (2002).
Modulation of the auditory cortex during speech: an MEG study. J. Cogn.
Neurosci. 14, 1125–1138. doi: 10.1162/089892902760807140
Hughes, G., Desantis, A., and Waszak, F. (2013a). Attenuation of auditory N1
results from identity-specific action-effect prediction. Eur. J. Neurosci. 37,
1152–1158. doi: 10.1111/ejn.12120
Hughes, G., Desantis, A., and Waszak, F. (2013b). Mechanisms of intentional
binding and sensory attenuation: the role of temporal prediction, temporal
control, identity prediction, and motor prediction. Psychol. Bull. 139, 133–151.
doi: 10.1037/a0028566
Hurley, S. (2001). Perception and action: alternative views. Synthese 129, 3–40.
doi: 10.1023/A:1012643006930
Hurley, S. (2008). The shared circuits model (SCM): how control, mirroring, and
simulation can enable imitation, deliberation, and mindreading. Behav. Brain
Sci. 31, 1–21. doi: 10.1017/S0140525X07003123
Hyde, K. L., Lerch, J., Norton, A., Forgeard, M., Winner, E., Evans, A. C.,
and Schlaug, G. (2009). The effects of musical training on structural
brain development. Ann. N.Y. Acad. Sci. 1169, 182–186. doi: 10.1111/j.17496632.2009.04852.x
Imamizu, H., and Kawato, M. (2009). Brain mechanisms for predictive control by
switching internal models: implications for higher-order cognitive functions.
Psychol. Res. 73, 527–544. doi: 10.1007/s00426-009-0235-1
Iordanescu, L., Grabowecky, M., and Suzuki, S. (2013). Action enhances auditory but not visual temporal sensitivity. Psychon. Bull. Rev. 20, 108–114.
doi: 10.3758/s13423-012-0330-y
Iversen, J. R., Repp, B. H., and Patel, A. D. (2009). Top-down control of rhythm perception modulates early auditory responses. Ann. N.Y. Acad. Sci. 1169, 58–73.
doi: 10.1111/j.1749-6632.2009.04579.x
James, W. (1890). The Principles of Psychology. Vol. 1. New York, NY: Henry Holt.
doi: 10.1037/11059-000
Janata, P., Tomic, S. T., and Haberman, J. M. (2012). Sensorimotor coupling in
music and the psychology of the groove. J. Exp. Psychol. Gen. 141, 54–75.
doi: 10.1037/a0024208
Johnson, M. L. (1997). Embodied musical meaning. Theory Pract. 22, 95–102.
Jones, A., Hughes, G., and Waszak, F. (2013). The interaction between attention and motor prediction. An ERP study. Neuroimage 83, 533–541. doi:
10.1016/j.neuroimage.2013.07.004
Juchniewicz, J. (2008). The influence of physical movement on the
perception of musical performance. Psychol. Music 36, 417–427.
doi: 10.1177/0305735607086046
Juslin, P. N., and Västfjäll, D. (2008). Emotional responses to music: the
need to consider underlying mechanisms. Behav. Brain Sci. 31, 559–621.
doi: 10.1017/S0140525X08005293
Keller, P. E. (2008). “Joint action in music performance,” in Enacting
Intersubjectivity: a Cognitive and Social Perspective on the Study of Interactions,
Frontiers in Psychology | Theoretical and Philosophical Psychology
Action effects on music perception
eds F. Morganti, A. Carassa, and G. Riva (Amsterdam: IOS press),
205–221.
Kelso, J. A. (1995). Dynamic Patterns: The Self Organization of Brain and Behaviour.
Cambridge, MA: MIT Press.
Kirschner, S., and Tomasello, M. (2010). Joint music making promotes prosocial behavior in 4-year-old children. Evol. Hum. Behav. 31, 354–364.
doi: 10.1016/j.evolhumbehav.2010.04.004
Knolle, F., Schröger, E., Baess, P., and Kotz, S. A. (2012a). The cerebellum generates motor-to-auditory predictions: ERP lesion evidence. J. Cogn. Neurosci. 24,
698–706. doi: 10.1162/jocn_a_00167
Knolle, F., Schröger, E., and Kotz, S. A. (2012b). Prediction errors in
self-and externally-generated deviants. Biol. Psychol. 92, 410–416.
doi: 10.1016/j.biopsycho.2012.11.017
Kohler, E., Keysers, C., Umilta, M. A., Fogassi, L., Gallese, V., and Rizzolatti, G.
(2002). Hearing sounds, understanding actions: action representation in mirror
neurons. Science 297, 846–848. doi: 10.1126/science.1070311
Kozak, M., Nymoen, K., and Godøy, R. I. (2012). “Effects of spectral features of
sound on gesture type and timing,” in Gesture and Sign Language in HumanComputer Interaction and Embodied Communication (LNCS), Vol. 7206, eds E.
Efthimiou, G. Kouroupetroglou, and S.-E. Fotinea (Berlin: Springer-Verlag),
69–80. doi: 10.1007/978-3-642-34182-3_7
Krueger, J. (2009). Enacting musical experience. J. Conscious. Stud. 16, 2–3.
doi: 10.1093/acprof:oso/9780199654888.003.0014
Küssner, M. B. (2013). Music and shape. Lit. Linguist. Comput. 28, 472–479.
doi: 10.1093/llc/fqs071
Laban, R. (1947). Effort. London: MacDonald & Evans.
Laban, R., and Ullmann, L. (1966). Choreutics. Hampshire: Dance Books Ltd.
(Edition published in 2011).
Lahav, A., Boulanger, A., Schlaug, G., and Saltzman, E. (2005). The power of listening: auditory-motor interactions in musical training. Ann. N.Y. Acad. Sci.
1060, 189–194. doi: 10.1196/annals.1360.042
Lahav, A., Saltzman, E., and Schlaug, G. (2007). Action representation of sound:
audiomotor recognition network while listening to newly acquired actions. J.
Neurosci. 27, 308–314. doi: 10.1523/JNEUROSCI.4822-06.2007
Lalazar, H., and Vaadia, E. (2008). Neural basis of sensorimotor learning: modifying internal models. Curr. Opin. Neurobiol. 18, 573–581.
doi: 10.1016/j.conb.2008.11.003
Lappe, C., Steinsträter, O., and Pantev, C. (2013). Rhythmic and melodic deviations
in musical sequences recruit different cortical areas for mismatch detection.
Front. Hum. Neurosci. 7:260. doi: 10.3389/fnhum.2013.00260
Laske, O. E. (1974). The information-processing approach to musical cognition. J.
New Music Res. 3, 109–136.
Leaver, A. M., Van Lare, J., Zielinski, B., Halpern, A. R., and Rauschecker, J. P.
(2009). Brain activation during anticipation of sound sequences. J. Neurosci.
29, 2477–2485. doi: 10.1523/JNEUROSCI.4921-08.2009
Leman, M. (2007). Embodied music Cognition and Mediation Technology.
Cambridge, MA: MIT Press.
Leman, M., Desmet, F., Styns, F., Van Noorden, L., and Moelants, D. (2009). Sharing
musical expression through embodied listening: a case study based on Chinese
guqin music. Music Percept. 26, 263–278. doi: 10.1525/mp.2009.26.3.263
Leman, M., Moelants, D., Varewyck, M., Styns, F., van Noorden, L., and Martens, J.P. (2013). Activating and relaxing music entrains the speed of beat synchronized
walking. PLoS ONE 8:e67932. doi: 10.1371/journal.pone.0067932
Levinson, J. (2006). Contemplating Art: Essays in Aesthetics. New York, NY: Oxford
University Press. doi: 10.1093/acprof:oso/9780199206179.001.0001
Lidji, P., Kolinsky, R., Lochy, A., and Morais, J. (2007). Spatial associations for musical stimuli: a piano in the head? J. Exp. Psychol. Hum. Percept. Perform. 33,
1189–1207. doi: 10.1037/0096-1523.33.5.1189
Liljeström, S., Juslin, P. N., and Västfjäll, D. (2012). Experimental evidence of
the roles of music choice, social context, and listener personality in emotional reactions to music. Psychol. Music 41, 577–597. doi: 10.1177/0305735612
440615
Loehr, J. D. (2013). Sensory attenuation for jointly produced action effects. Front.
Psychol. 4:172. doi: 10.3389/fpsyg.2013.00172
Loehr, J. D., and Palmer, C. (2011). Temporal coordination between
performing
musicians.
Q.
J.
Exp.
Psychol.
64,
2153–2167.
doi: 10.1080/17470218.2011.603427
Lotze, M. (2013). Kinesthetic imagery of musical performance. Front. Hum.
Neurosci. 7:280. doi: 10.3389/fnhum.2013.00280
January 2014 | Volume 4 | Article 1008 | 12
Maes et al.
Lotze, M., Reimold, M., Heymans, U., Laihinen, A., Patt, M., and Halsband, U.
(2009). Reduced ventrolateral fmri response during observation of emotional
gestures related to the degree of dopaminergic impairment in parkinson disease.
J. Cogn. Neurosci. 21, 1321–1331. doi: 10.1162/jocn.2009.21087
Lotze, M., Scheler, G., Tan, H.-R., Braun, C., and Birbaumer, N. (2003).
The musician’s brain: functional imaging of amateurs and professionals during performance and imagery. Neuroimage 20, 1817–1829. doi:
10.1016/j.neuroimage.2003.07.018
Lucas, M., Chaves, F., Teixeira, S., Carvalho, D., Peressutti, C., Bittencourt, J.,
et al. (2013). Time perception impairs sensory-motor integration in Parkinson’s
disease. Int. Arch. Med. 6, 1–7. doi: 10.1186/1755-7682-6-39
Luft, A. R., and Buitrago, M. M. (2005). Stages of motor skill learning. Mol.
Neurobiol. 32, 205–216. doi: 10.1385/MN:32:3:205
Maes, P.-J., Amelynck, D., and Leman, M. (2012). Dance-the-Music: an educational
platform for the modeling, recognition and audiovisual monitoring of dance
steps using spatiotemporal motion templates. EURASIP J. Adv. Signal Process.
2012, 1–16. doi: 10.1186/1687-6180-2012-35
Maes, P.-J., Amelynck, D., Lesaffre, M., Leman, M., and Arvind, D. (2013). The
“Conducting Master”: an interactive, real-time gesture monitoring system
based on spatiotemporal motion templates. Int. J. Hum. Comput. Interact. 29,
471–487. doi: 10.1080/10447318.2012.720197
Maes, P.-J., and Leman, M. (2013). The influence of body movements on children’s perception of music with an ambiguous expressive character. PLoS ONE
8:e54682. doi: 10.1371/journal.pone.0054682
Maes, P.-J., Leman, M., Lesaffre, M., Demey, M., and Moelants, D. (2010). From
expressive gesture to sound: the development of an embodied mapping trajectory inside a musical interface. J. Multimodal User Interfaces 3, 67–78. doi:
10.1007/s12193-009-0027-3
Maes, P.-J., Van Dyck, E., Lesaffre, M., Leman, M., and M, K. P. (2014). The coupling of action and perception in musical meaning formation. Music Percept.
(forthcoming).
Manning, F., and Schutz, M. (2013). “Moving to the beat” improves timing
perception. Psychon. Bull. Rev. 20, 1133–1139. doi: 10.3758/s13423-013-0439-7
Massaro, D. W. (1990). “An information-processing analysis of perception and
action,” in Relationships between Perception and Action: Current Approaches,
eds O. Neumann and W. Prinz (Berlin: Springer-Verlag), 133–166. doi:
10.1007/978-3-642-75348-0_6
Maus, F. (1988). Music as drama. Music Theory Spectr. 10, 56–73. doi:
10.2307/745792
McClelland, J. L., Botvinick, M. M., Noelle, D. C., Plaut, D. C., Rogers, T. T.,
Seidenberg, M. S., et al. (2010). Letting structure emerge: connectionist and
dynamical systems approaches to cognition. Trends Cogn. Sci. 14, 348–356. doi:
10.1016/j.tics.2010.06.002
Mead, A. (1999). Bodily hearing: physiological metaphors and musical understanding. J. Music Theory 43, 1–19. doi: 10.2307/3090688
Melcher, T., Winter, D., Hommel, B., Pfister, R., Dechent, P., and Gruber,
O. (2012). The neural substrate of the ideomotor principle revisited: evidence for asymmetries in action-effect learning. Neuroscience 231, 13–27. doi:
10.1016/j.neuroscience.2012.11.035
Naveda, L., and Leman, M. (2009). A cross-modal heuristic for periodic pattern analysis of samba music and dance. J. New Music Res. 38, 255–283. doi:
10.1080/09298210903105432
Naveda, L., and Leman, M. (2010). The spatiotemporal representation of dance
and music gestures using topological gesture analysis (TGA). Music Percept. 28,
93–111. doi: 10.1525/mp.2010.28.1.93
Neisser, U. (1967). Cognitive Psychology. New York, NY: Appleton-Century-Crofts.
Nelson, A., Schneider, D. M., Takatoh, J., Sakurai, K., Wang, F., and Mooney, R.
(2013). A circuit for motor cortical modulation of auditory cortical activity. J.
Neurosci. 33, 14342–14353. doi: 10.1523/JNEUROSCI.2275-13.2013
Nombela, C., Hughes, L. E., Owen, A. M., and Grahn, J. A. (2013a). Into the
groove: can rhythm influence parkinson’s disease? Neurosci. Biobehav. Rev. 37,
2564–2570. doi: 10.1016/j.neubiorev.2013.08.003
Nombela, C., Rae, C., Grahn, J., Barker, R., Owen, A., Rowe, J., et al. (2013b). How
often does music and rhythm improve patients’ perception of motor symptoms
in Parkinson’s disease? J. Neurol. 260, 1404–1405. doi: 10.1007/s00415-0136860-z
Obhi, S. S., and Sebanz, N. (2011). Moving together: toward understanding the
mechanisms of joint action. Exp. Brain Res. 211, 329–336. doi: 10.1007/s00221011-2721-0
www.frontiersin.org
Action effects on music perception
O’Reilly, J. X., Jbabdi, S., Rushworth, M. F., and Behrens, T. E. (2013). Brain systems for probabilistic and dynamic prediction: computational specificity and
integration. PLoS Biol. 11:e1001662. doi: 10.1371/journal.pbio.1001662
Pacherie, E. (2012). “The phenomenology of joint action: self-agency vs. jointagency,” in Joint Attention: New Developments, ed A. Seemann (Cambridge, MA:
MIT Press), 343–389.
Pascal-Leone, A. (2001). The brain that plays music and is changed by it. Ann. N.Y.
Acad. Sci. 930, 315–329. doi: 10.1111/j.1749-6632.2001.tb05741.x
Pasupathy, A., and Miller, E. K. (2005). Different time courses of learning-related
activity in the prefrontal cortex and striatum. Nature 433, 873–876. doi:
10.1038/nature03287
Pazzaglia, M., Pizzamiglio, L., Pes, E., and Aglioti, S. M. (2008). The sound of
actions in apraxia. Curr. Biol. 18, 1766–1772. doi: 10.1016/j.cub.2008.09.061
Phillips-Silver, J., and Keller, P. E. (2012). Searching for roots of entrainment and
joint action in early musical interactions. Front. Hum. Neurosci. 6:26. doi:
10.3389/fnhum.2012.00026
Phillips-Silver, J., and Trainor, L. J. (2005). Feeling the beat: movement influences infant rhythm perception. Science 308, 1430. doi: 10.1126/science.
1110922
Phillips-Silver, J., and Trainor, L. J. (2007). Hearing what the body feels:
auditory encoding and rhythmic movement. Cognition 105, 533–546.
doi: 10.1016/j.cognition.2006.11.006
Pierce, A. (2007). Deepening Musical Performance through Movement: the Theory
and Practice of Embodied Interpretation. Bloomington, IN: Indiana University
Press.
Port, R. F., and Van Gelder, T., editors (1995). Mind as Motion: Explorations in the
Dynamics of Cognition. Cambridge, MA: MIT Press.
Prinz, W. (1990). “A common coding approach to perception and action,”
in Relationships Between Perception and Action: Current Approaches, eds O.
Neumann and W. Prinz (Heidelberg: Springer), 167–201. doi: 10.1007/978-3642-75348-0_7
Prinz, W. (1997). Perception and action planning. Eur. J. Cogn. Psychol. 9, 129–154.
doi: 10.1080/713752551
Pylyshyn, Z. W., and Demopoulos, W., editors (1986). Meaning and Cognitive
Structure: Issues in the Computational Theory of Mind. Norwood, NJ: ABLEX
Publishing Company.
Repp, B. H. (1993). “Musical motion: some historical and contemporary perspectives,” in Proceedings of the Stockholm Music Acoustics Conference (SMAC), eds A.
Friberg, J. Iwarsson, E. Jansson, and J. Sundberg (Stockholm: Kgl. Musikaliska
Akademin), 128–135.
Repp, B. H., and Knoblich, G. (2009). Performed or observed keyboard actions
affect pianists’ judgements of relative pitch. Q. J. Exp. Psychol. 62, 2156–2170.
doi: 10.1080/17470210902745009
Rizzolatti, G., Fogassi, L., Gallese, V., et al. (2001). Neurophysiological mechanisms
underlying the understanding and imitation of action. Nat. Rev. Neurosci. 2,
661–669. doi: 10.1038/35090060
Roussel, C., Hughes, G., and Waszak, F. (2013). A preactivation
account of sensory attenuation. Neuropsychologia 51, 922–929. doi:
10.1016/j.neuropsychologia.2013.02.005
Rusconi, E., Kwan, B., Giordano, B. L., Umilta, C., and Butterworth, B. (2006).
Spatial representation of pitch height: the SMARC effect. Cognition 99, 113–129.
doi: 10.1016/j.cognition.2005.01.004
Sanmiguel, I., Todd, J., and Schröger, E. (2013). Sensory suppression effects to selfinitiated sounds reflect the attenuation of the unspecific N1 component of the
auditory ERP. Psychophysiology 14, 1–11. doi: 10.1111/psyp.12024
Sato, A. (2008). Action observation modulates auditory perception of the
consequence of others’ actions. Conscious. Cogn. 17, 1219–1227. doi:
10.1016/j.concog.2008.01.003
Schäfer, T., Fachner, J., and Smukalla, M. (2013). Changes in the representation of space and time while listening to music. Front. Psychol. 4:508. doi:
10.3389/fpsyg.2013.00508
Schubotz, R. I. (2007). Prediction of external events with our motor system: towards a new framework. Trends Cogn. Sci. 11, 211–218. doi:
10.1016/j.tics.2007.02.006
Schutz, M., and Lipscomb, S. (2007). Hearing gestures, seeing music: vision
influences perceived tone duration. Perception 36, 888–897. doi: 10.1068/p5635
Schütz-Bosbach, S., and Prinz, W. (2007). Perceptual resonance: actioninduced modulation of perception. Trends Cogn. Sci. 11, 349–355. doi:
10.1016/j.tics.2007.06.005
January 2014 | Volume 4 | Article 1008 | 13
Maes et al.
Seddon, F. A. (2005). Modes of communication during jazz improvisation. Br. J.
Music Educ. 22, 47–61. doi: 10.1017/S0265051704005984
Sedlmeier, P., Weigelt, O., and Walther, E. (2011). Music is in the muscle:
how embodied cognition may influence music preferences. Music Percept. 28,
297–306. doi: 10.1525/mp.2011.28.3.297
Shapiro, L. (2010). Embodied Cognition. Boca Raton, FL: Taylor & Francis.
Shapiro, L. (2013). Dynamics and cognition. Minds Mach. 23, 353–375. doi:
10.1007/s11023-012-9290-2
Shin, Y. K., Proctor, R. W., and Capaldi, E. (2010). A review of contemporary
ideomotor theory. Psychol. Bull. 136, 943–974. doi: 10.1037/a0020541
Shove, P., and Repp, B. H. (1995). “Musical motion and performance: theoretical and empirical perspectives,” in The Practice of Performance, ed J. Rink
(Cambridge, UK: Cambridge University Press), 55–83.
Sievers, B., Polansky, L., Casey, M., and Wheatley, T. (2013). Music and movement
share a dynamic structure that supports universal expressions of emotion. Proc.
Natl. Acad. Sci. U.S.A. 110, 70–75. doi: 10.1073/pnas.1209023110
Southgate, V. (2013). Do infants provide evidence that the mirror system
is involved in action understanding? Conscious. Cogn. 22, 1114–1121. doi:
10.1016/j.concog.2013.04.008
Stewart, L., Verdonschot, R. G., Kajihara, T., and Sparks, J. (2013a).
Action-perception coupling in violinists. Front. Hum. Neurosci. 7:349.
doi: 10.3389/fnhum.2013.00349
Stewart, L., Verdonschot, R. G., Nasralla, P., and Lanipekun, J. (2013b). Action–
perception coupling in pianists: learned mappings or spatial musical association of response codes (SMARC) effect? Q. J. Exp. Psychol. 66, 37–50. doi:
10.1080/17470218.2012.687385
Stupacher, J., Hove, M. J., Novembre, G., Schütz-Bosbach, S., and Keller,
P. E. (2013). Musical groove modulates motor cortex excitability: a
TMS investigation. Brain Cogn. 82, 127–136. doi: 10.1016/j.bandc.2013.
03.003
Su, Y.-H., and Pöppel, E. (2012). Body movement enhances the extraction of
temporal structures in auditory sequences. Psychol. Res. 76, 373–382. doi:
10.1007/s00426-011-0346-3
Sudnow, D. (1978). Ways of the Hand. Cambridge, MA: MIT Press.
Thelen, E., and Smith, L. B. (1998). “Dynamic systems theories,” in Handbook
of Child Psychology, Theoretical Models of Human Development, 6th Edn.,
eds W. Damon and R. M. Lerner (Chichester: Wiley Online Library),
258–312.
Thompson, W. F., Graham, P., and Russo, F. A. (2005). Seeing music performance:
visual influences on perception and experience. Semiotica 156, 203–227. doi:
10.1515/semi.2005.2005.156.203
Timm, J., SanMiguel, I., Saupe, K., and Schröger, E. (2013). The N1-suppression
effect for self-initiated sounds is independent of attention. BMC Neurosci. 14:2.
doi: 10.1186/1471-2202-14-2
Timmann, D., Drepper, J., Frings, M., Maschke, M., Richter, S., Gerwig, M.,
and Kolb, F. (2010). The human cerebellum contributes to motor, emotional and cognitive associative learning: a review. Cortex 46, 845–857. doi:
10.1016/j.cortex.2009.06.009
Toiviainen, P., Luck, G., and Thompson, M. R. (2010). Embodied meter: hierarchical eigenmodes in music-induced movement. Music Percept. 28, 59–70. doi:
10.1525/mp.2010.28.1.59
Trimarchi, P. D., and Luzzatti, C. (2011). Implicit chord processing and motor
representation in pianists. Psychol. Res. 75, 122–128. doi: 10.1007/s00426-0100292-5
Frontiers in Psychology | Theoretical and Philosophical Psychology
Action effects on music perception
Turvey, M. T. (1990). Coordination. Am. Psychol. 45, 938–953. doi: 10.1037/0003066X.45.8.938
Ungerleider, L. G., Doyon, J., and Karni, A. (2002). Imaging brain plasticity during motor skill learning. Neurobiol. Learn. Mem. 78, 553–564. doi:
10.1006/nlme.2002.4091
Urista, D. J. (2003). Beyond words: the moving body as a tool for musical
understanding. Music Theory Online 9.
Van Dyck, E., Maes, P.-J., Hargreaves, J., Lesaffre, M., and Leman, M. (2013).
Expressing induced emotions through free dance movement. J. Nonverbal
Behav. 37, 175–190. doi: 10.1007/s10919-013-0153-1
Van Gelder, T. (1998). The dynamical hypothesis in cognitive science. Behav. Brain
Sci. 21, 615–628.
Varela, F. J., Thompson, E. T., and Rosch, E. (1991). The Embodied Mind: Cognitive
Science and Human Experience. Cambridge, MA: MIT Press.
Warren, W. H. (2006). The dynamics of perception and action. Psychol. Rev. 113,
358–389. doi: 10.1037/0033-295X.113.2.358
Waszak, F., Cardoso-Leite, P., and Hughes, G. (2012). Action effect anticipation:
neurophysiological basis and functional consequences. Neurosci. Biobehav. Rev.
36, 943–959. doi: 10.1016/j.neubiorev.2011.11.004
Weiss, C., and Schütz-Bosbach, S. (2012). Vicarious action preparation does not
result in sensory attenuation of auditory action effects. Conscious. Cogn. 21,
1654–1661. doi: 10.1016/j.concog.2012.08.010
Williams, Z. M., and Eskandar, E. N. (2006). Selective enhancement of associative
learning by microstimulation of the anterior caudate. Nat. Neurosci. 9, 562–568.
doi: 10.1038/nn1662
Wilson, A. D., and Golonka, S. (2013). Embodied cognition is not what you think
it is. Front. Psychol. 4:58. doi: 10.3389/fpsyg.2013.00058
Witt, J. K. (2011). Action’s effect on perception. Curr. Dir. Psychol. Sci. 20, 201–206.
doi: 10.1177/0963721411408770
Wolpert, D. M., Ghahramani, Z., and Jordan, M. I. (1995). An internal model
for sensorimotor integration. Science 269, 1880–1882. doi: 10.1126/science.
7569931
Wolpert, D. M., Miall, R. C., and Kawato, M. (1998). Internal models in the
cerebellum. Trends Cogn. Sci. 2, 338–347. doi: 10.1162/jocn.2009.21055
Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Received: 07 October 2013; paper pending published: 31 October 2013; accepted: 17
December 2013; published online: 03 January 2014.
Citation: Maes P-J, Leman M, Palmer C and Wanderley MM (2014) Action-based
effects on music perception. Front. Psychol. 4:1008. doi: 10.3389/fpsyg.2013.01008
This article was submitted to Theoretical and Philosophical Psychology, a section of the
journal Frontiers in Psychology.
Copyright © 2014 Maes, Leman, Palmer and Wanderley. This is an open-access
article distributed under the terms of the Creative Commons Attribution License
(CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No
use, distribution or reproduction is permitted which does not comply with these
terms.
January 2014 | Volume 4 | Article 1008 | 14