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REVIEW ARTICLE
CORRESPONDING AUTHOR:
Tomas E. Matthews
Center for Music in the Brain,
Department of Clinical
Medicine, Aarhus University
Hospital, Nørrebrogade 44,
Building 1A, 8000 Aarhus C,
Denmark; Royal Academy of
Music, Skovgaardsgade 2C, DK-
8000 Aarhus C, Denmark
toma@clin.au.dk
KEYWORDS:
PLUMM; predictive processing;
groove; dopamine;
norepinephrine; pleasure;
curiosity; learning
TO CITE THIS ARTICLE:
Matthews, T. E., Stupacher,
J., & Vuust, P. (2023). The
Pleasurable Urge to Move to
Music Through the Lens of
Learning Progress. Journal of
Cognition, 6(1): 55, pp. 1–22.
DOI: https://doi.org/10.5334/
joc.320
The Pleasurable Urge to
Move to Music Through the
Lens of Learning Progress
TOMAS E. MATTHEWS
JAN STUPACHER
PETER VUUST
ABSTRACT
Interacting with music is a uniquely pleasurable activity that is ubiquitous across
human cultures. Current theories suggest that a prominent driver of musical pleasure
responses is the violation and confirmation of temporal predictions. For example, the
pleasurable urge to move to music (PLUMM), which is associated with the broader
concept of groove, is higher for moderately complex rhythms compared to simple
and complex rhythms. This inverted U-shaped relation between PLUMM and rhythmic
complexity is thought to result from a balance between predictability and uncertainty.
That is, moderately complex rhythms lead to strongly weighted prediction errors which
elicit an urge to move to reinforce the predictive model (i.e., the meter). However,
the details of these processes and how they bring about positive affective responses
are currently underspecified. We propose that the intrinsic motivation for learning
progress drives PLUMM and informs the music humans choose to listen to, dance to,
and create. Here, learning progress reflects the rate of prediction error minimization
over time. Accordingly, reducible prediction errors signal the potential for learning
progress, producing a pleasurable, curious state characterized by the mobilization
of attentional and memory resources. We discuss this hypothesis in the context of
current psychological and neuroscientific research on musical pleasure and PLUMM.
We propose a theoretical neuroscientific model focusing on the roles of dopamine
and norepinephrine within a feedback loop linking prediction-based learning, curiosity,
and memory. This perspective provides testable predictions that will motivate
future research to further illuminate the fundamental relation between predictions,
movement, and reward.
*Author affiliations can be found in the back matter of this article
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DOI: 10.5334/joc.320
The affective responses we derive from interacting with art forms such as music, dance, film,
and visual art permeate nearly every aspect of our lives, informing our relationships, our
identities, and how we spend our time. However, due to their abstract and subjective nature,
the psychological and neuroscientific underpinnings of such affective responses have proven
difficult to elucidate. A recent and promising approach has been to frame our interactions with
such art forms in terms of perceptual learning via probabilistic predictions. That is, such art
forms can generate positive affective responses such as pleasure because they provide a means
with which to improve the match between our internal models and external input, and thus to
generate more accurate predictions in the future (Brielmann & Dayan, 2022; Gold, Pearce, Mas-
Herrero, Dagher, & Zatorre, 2019; Mas-Herrero, Maini, Sescousse, & Zatorre, 2021; Van de Cruys,
2017; Vuust, Heggli, Friston, & Kringelbach, 2022). Predictive processes are particularly relevant
to music as it is often highly structured in both time and tonal space, unfolding in ways that
allow for predictions at multiple timescales (e.g., regarding the next note, the next phrase, the
next section). Indeed, many prominent theories of the affective responses to music emphasize
the role of prediction violations and confirmations (Belfi & Loui, 2020; Huron, 2006; Koelsch,
Vuust, & Friston, 2019; Meyer, 1956; Salimpoor, Zald, Zatorre, Dagher, & McIntosh, 2015; Vuust
et al., 2022).
A pervasive phenomenon within both music and aesthetics research is the inverted U-shaped
pattern of positive affective responses as a function of stimulus complexity (or familiarity;
Berlyne, 1971; Chmiel & Schubert, 2017; Hargreaves & North, 2010). Unlike visual art or film,
rhythmic music often elicits a motor response, characterized as the pleasurable urge to move
to music (PLUMM) which is associated with the broader concept of groove (Câmara & Danielsen,
2018; Duman, Snape, Toiviainen, & Luck, 2023; Janata, Tomic, & Haberman, 2012; Madison,
2006; Senn et al., 2019; Stupacher, Hove, Novembre, Schütz-Bosbach, & Keller, 2013). PLUMM
shows an inverted U-shaped pattern as a function of rhythmic complexity (Matthews et al.,
2019; Matthews, Witek, Thibodeau, Vuust, & Penhune, 2022; Sioros, Miron, Davies, Gouyon,
& Madison, 2014; Spiech, Sioros, Endestad, Danielsen, & Laeng, 2022; Stupacher et al., 2022;
Witek et al., 2014). Predictive processing accounts of this inverted U suggest that medium
complexity rhythms achieve the optimal balance between predictability and surprise which
results in the greatest PLUMM (Koelsch et al., 2019; Vuust et al., 2022; Vuust, Witek, Dietz, &
Kringelbach, 2018; Vuust & Witek, 2014).
However, several questions remain, particularly surrounding the pleasurable component of
PLUMM, its relation with the urge to move and other forms of musical pleasure, as well as the
role of individual differences in shaping the inverted U. Further, much work on music reward
processing has focused on either brief, intensely pleasurable response to music (Grewe, Nagel,
Kopiez, & Altenmüller, 2007; Laeng, Eidet, Sulutvedt, & Panksepp, 2016; Martínez-Molina,
Mas-Herrero, Rodríguez-Fornells, Zatorre, & Marco-Pallarés, 2016; Salimpoor et al., 2013) or
individuals who gain no pleasure from music (Belfi & Loui, 2020; Loui et al., 2017; Martínez-
Molina et al., 2016; Mas-Herrero, Zatorre, Rodriguez-Fornells, & Marco-Pallarés, 2014). While
studying these extreme cases has provided many useful insights, they do not represent the
relatively protracted (i.e., > 1 minute) and moderate intensity responses that likely characterize
most individuals’ regular interactions with music.
Here we focus on PLUMM as an illustrative case study, while drawing on research from music
reward processing more generally to inform and substantiate our proposal. By drawing
primarily on the learning progress hypothesis and integrating it with concepts such as curiosity
and creativity, we extend the predictive processing treatment of PLUMM, and music reward
processing more generally. We further outline a model of the potential neural mechanisms
underlying these processes, focusing on the role of dopamine and norepinephrine in linking the
predictive processes to learning, memory, and pleasure.
PREDICTIVE PROCESSING AND PLUMM
To elaborate an explanation of the pleasure and motor components of PLUMM and their relation
to each other, it is crucial to establish that although they are strongly coupled, they are in fact
separable components. Many early studies have focused on the urge to move (Madison, 2006;
Madison, Gouyon, & Ullen, 2009; Madison, Gouyon, Ullén, & Hörnström, 2011; Pressing, 2002)
while more recent work has shown that this urge is accompanied by positive affect (Janata,
3Matthews et al
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DOI: 10.5334/joc.320
et al., 2012; Matthews et al., 2019; Matthews, Witek, Lund, Vuust, & Penhune, 2020; Matthews
et al., 2022; Senn, Bechtold, Hoesl, & Kilchenmann, 2019; Senn, Kilchenmann, Bechtold, &
Hoesl, 2018; Senn, Rose, et al., 2019; Witek et al., 2014). In these studies, urge to move and
pleasure ratings are collected separately and both show an inverted U-shaped function with
rhythmic complexity. Although pleasure and urge to move ratings tend to be highly correlated,
these correlations tend not to be so high as to be considered colinear, particularly when the
ratings are made in two separate listening sessions (e.g., Matthews et al., 2020: r(54) = 0.62,
95% CI[0.36, 0.81]). Further, whereas rhythmic complexity affects both components directly,
harmonic complexity only affects the urge to move via its effect on pleasure (Matthews et
al., 2020). Together these results provide evidence that, although strongly linked, the two
components of PLUMM are in fact distinguishable and can be considered separately.
Through several influential review and perspective papers, the predictive processing framework,
along with active inference, has been deployed to interpret the inverted U-shaped relation
between PLUMM and rhythmic complexity (see Figure 1; Koelsch et al., 2019; Vuust et al., 2022,
2018; Vuust & Witek, 2014). The predictive processing framework proposes that the brain uses
approximate Bayesian inference to continuously make and update predictions about incoming
stimuli and internal states, based on generative internal models, that is, representations of
the hidden causes of these stimuli and states (Friston, 2010). Prediction errors, which reflect
mismatches between prediction and input, force either a refinement to the internal model or
an alteration to the input to better fit the model, e.g., via movement. However, the degree to
which prediction errors lead to model improvement depends on the certainty or precision of
the antecedent predictions. That is, more precise predictions, if violated, lead to more strongly
weighted, or salient, prediction errors, necessitating a stronger model-improving response.
Within this framework, moderately complex rhythms lead to greater PLUMM because they
maximize the number of strongly weighted prediction errors. Here, the internal model consists
of the beat and meter (henceforth, metrical model). The meter is the pattern of strong and weak
beats, including subdivisions, and represents the probability of a note occurring in any position
of the metrical grid (Lehrdahl & Jackendoff, 1983; Palmer & Krumhansl, 1990). In Bayesian
terms, metrical models reflect context-specific priors that are implicitly learned over years of
Figure 1 The predictive
processing account of PLUMM.
A) Rhythms with three
levels of syncopation lead
to meter-based predictions
whose uncertainty depend
on both the position in the
meter and the strength of the
metrical model. B) Moderately
syncopated rhythms maximize
the number of strongly
weighted prediction errors.
Adapted from Stupacher et
al., 2022.
4Matthews et al
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listening to and/or playing rhythmic music (Jacoby et al., 2021; Kaplan, Cannon, Jamone, &
Pearce, 2022). Prediction errors occur when note timings do not conform to the meter, i.e.,
occur on a position with low probability. For example, syncopations, when notes occur on weak
(low probability) metrical positions and are followed by a silence on strong (high probability)
metric positions, violate metrical expectations resulting in metrical uncertainty. The degree to
which a prediction error affects the metrical model (i.e., its weight), depends on its position in
the meter and the strength or certainty of the metrical model itself. Therefore, prediction errors
and metrical uncertainty form a feedback loop. As the number of strongly weighted prediction
errors increases, metrical uncertainty increases, leading to weaker predictions, indicating the
need for a new metrical model (e.g., a different time signature).
Rhythms with a moderate degree of syncopation are predictable enough to allow for relatively
strong beat and meter-based predictions, which, when violated elicit strongly weighted
prediction errors. However, these strong prediction errors do not invalidate the model but
rather indicate the need for modification, resulting in relatively fast and automatic model-
updating responses (Lumaca, Haumann, Brattico, Grube, & Vuust, 2019; Vuust et al., 2005).
Increasing the number of syncopations can impede the generation of a metrical model, thus
predictions are very imprecise, if they can be generated at all. Meanwhile, rhythms with few
or no syncopations allow for highly precise predictions, but there are no prediction errors to
challenge the model.
Within the predictive processing framework, there are two ways to minimize prediction errors
(Friston, 2010); 1) modify the model to better fit the input, e.g., by incorporating syncopations
into the model, by phase-shifting the beat and meter to better align with the rhythm (Fitch
& Rosenfeld, 2007), or more drastically, by switching to a different meter; 2) change the
input, e.g., by moving or tapping along with the purported meter, thus generating additional
proprioceptive and sensory inputs that reinforce this meter or satisfy its predictions. Temporal
predictions, particularly relative or beat-based predictions, rely on the motor system even
when no motor output is necessary or forthcoming (Chen, Penhune, & Zatorre, 2008; Grahn
& Brett, 2007; Kung, Chen, Zatorre, & Penhune, 2013; Merchant & Yarrow, 2016; Morillon &
Baillet, 2017; Schubotz, 2007; Schubotz, Friederici, & von Cramon, 2000; Teki, Grube, Kumar,
& Griffiths, 2011). Therefore, both methods of reducing prediction errors engage the motor
system which may underlie the urge to move. Indeed, listening to moderately syncopated,
high-groove rhythms, even when not moving, elicits or modulates activity in motor regions in
the brain (Matthews et al., 2020; Stupacher et al., 2013). Pleasure is also thought to result from
this balance between strong prediction errors and metrical uncertainty (Vuust & Witek, 2014;
Vuust et al., 2018), however, this explanation has not been thoroughly elaborated.
Of course, other rhythmic and non-rhythmic features (e.g., microtiming, dynamics,
instrumentation, timbre, etc.) can elicit prediction errors and drive affective responses. We
focus on syncopations as they are relatively well-studied and show a consistent relation with
both perceived complexity (Gómez, Thul, & Toussaint, 2007) and PLUMM (Matthews et al.,
2019; Matthews, et al., 2022; Sioros et al., 2014; Stupacher et al., 2022; Witek et al., 2014). It is
important to note that defining meter in terms of strong and weak accents or probabilities comes
from a western tradition of music analysis. However, similar statistical learning and predictive
processes are assumed to form in non-western music traditions (Jacoby & Mcdermott, 2017;
Kaplan et al., 2022). Other theoretical models, such as the Dynamic Attending and Neural
Resonance theories, have been proposed to account for rhythm and meter perception (Large &
Jones, 1999; Large & Snyder, 2009). These accounts also emphasize predictions and prediction
errors and therefore do not necessarily conflict with the predictive processing account. Indeed,
neural oscillations entrained by the rhythm may provide the neural substrate for the metrical
model (Large & Kolen, 1994; Large & Snyder, 2009; Tal et al., 2017). However, the neural
mechanisms underlying rhythm perception are beyond the scope of this paper.
MUSICAL PLEASURE
Like primary and secondary rewards, music is a highly motivating stimulus; humans will
expend large amounts of time and effort for music or music-related experiences. Although
neuroimaging data suggests that they rely on the same brain networks (e.g., Blood & Zatorre,
2001; Blood, Zatorre, Bermudez, & Evans, 1999; Cheung et al., 2019; Gold, Mas-Herrero, Dagher,
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& Zatorre, 2019; Salimpoor, Benovoy, Larcher, Dagher, & Zatorre, 2011; Shany et al., 2019),
aesthetic experiences, such as musical pleasure, are distinct from primary and secondary
rewards. Primary rewards, such as food and sex, are directly related to reproduction or the
physiological needs that maintain homeostasis. Secondary rewards, such as money, are
indirectly related to primary rewards via learned associations (Sescousse, Caldú, Segura, &
Dreher, 2013). Therefore, primary and secondary rewards are highly tractable (i.e., more juice
or money = more reward). Due to their abstraction and/or distal relation to primary rewards,
no such relation exists for music (Hansen, Dietz, & Vuust, 2017). Further, aesthetic experiences
are more susceptible to top-down and contextual influences (Brielmann, 2022) as well as
interindividual differences, such as cultural background or aesthetic sensitivity (e.g., Clemente
et al., 2022; Senn, Bechtold, et al., 2019). Another difference is that primary and secondary
rewards are thought of as extrinsic, while musical pleasure is considered to be driven by intrinsic
reward processes (Salimpoor et al., 2015). Finally, in computational models of reward-based
learning, predictions tend to be about the timing or magnitude of the reward (Schultz, Dayan,
& Montague, 1997; Sutton & Barto, 2018). In music, the predictive processes themselves are
thought to generate reward (Ferreri et al., 2019; Gold et al., 2019; Hansen et al., 2017; Huron,
2006; Meyer, 1956; Salimpoor et al., 2015; Sloboda, 1991). This highlights that music listening
is seen as an active process involving the continuous generation and updating of predictions
and that affective responses to music depend not only on the music itself but how we actively
engage with it (Mencke, Omigie, Quiroga-Martinez, & Brattico, 2022).
Despite these differences from primary and secondary rewards, prominent theories of affective
responses to music emphasize that these responses are rooted in the same fundamental
processes as ‘everyday emotions’ such as happiness, sadness, and surprise (Huron, 2006;
Juslin, 2013). For example, along with affective responses to music, prediction errors can lead
to a fearful startle response or spontaneous laughter at an unexpected punchline (Huron,
2006). It is also important to note that reward, including musical pleasure, is not considered a
unitary concept, but consists of three relatively distinct mechanisms (Berridge & Kringelbach,
2015): 1) liking, which refers to the hedonic pleasure of a consummatory experience, 2)
wanting, which refers to the motivation to seek out rewarding stimuli, and 3) learning, which
is the formation of associations between reward and a given stimuli. One theory suggests that
aesthetic experiences reflect liking without wanting, that is, the sensory or consummatory
reward mechanisms without the motivational component (Pearce et al., 2016; Scherer, 2004).
This applies to aesthetic evaluations, such as beauty or awe, and/or the formation of aesthetic
preferences. However, this does not seem to capture the active components of musical
pleasure and PLUMM that we are focusing on here, which go beyond, but likely contribute to,
‘mere’ aesthetic evaluations.
Here we propose that many positive affective responses to music, such as PLUMM, are
driven by the intrinsic motivation for learning progress (Oudeyer, Kaplan, & Hafner, 2007;
Oudeyer et al., 2016; Schmidhuber, 2010). In contrast to the motivation for maximizing
extrinsic rewards, intrinsic motivation reflects an internal drive towards activities or stimuli
that are themselves enjoyable (Barto & Şimşek, 2005; Ryan & Deci, 2000). This drive may
have evolved via its benefit to survival (Singh, Lewis, Barto, & Sorg, 2010). For example,
learning to detect and predict regularities in sounds could feasibly impart an evolutionary
advantage (Juslin, 2013). Therefore, we may be pre-wired to maximize learning progress,
but still must seek out and isolate the stimuli and activities that afford maximal learning
by engaging with our environment (Melnikoff, Carlson, & Stillman, 2022). In the context of
predictive processing, learning is understood as the improvement of an internal model, that
is, increasing the fit between model and input by refining the model or generating new input
that satisfies the model’s predictions (Friston, 2010). This reflects perceptual learning that
is implicit and automatic, occurring spontaneously as one engages with their environment
in intuitive ways without some explicit goal in mind, aside from maximizing pleasure or
fun (Schmidhuber, 2010). Note that in this context, we are not referring to the statistical
learning that forms or improves higher level schemas (e.g., Loui, 2022). Instead, we are
assuming that individuals come in with established metrical models (Kaplan et al., 2022)
and that learning reflects continual refinement of these established models to better match
the incoming input.
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THE LEARNING PROGRESS HYPOTHESIS
According to the learning progress hypothesis (LP), humans are intrinsically motivated to seek
out stimuli or activities that maximally afford model improvement (see Figure 2; Oudeyer et al.,
2007; Oudeyer et al., 2016; Schmidhuber, 2010). Therefore, given an internal model relevant
for a given stimuli space, e.g., relative to similar stimuli within an experimental task, or relative
to other music within a given genre, humans will prefer and actively seek out stimuli within
this space that optimally challenges this model. ‘Optimal challenge’ here refers to stimuli that
are ‘just beyond our predictive capacities’, meaning those that elicit prediction errors but are
not so complex as to be unlearnable (Oudeyer et al., 2016, pg., 9). In other words, humans
will seek out and preferentially engage with stimuli and activities that engender, not just any
strongly weighted prediction errors, but specifically those that are reducible via refinement
of the current model. For example, hearing scat singing in the middle of your favourite heavy
metal song would result in a large prediction error that would not be easily integrated into your
model of heavy metal music or of that particular song, and would thus likely be experienced as
aversive. Conversely, hearing scat singing in the middle of your favourite jazz standard may still
lead to a prediction error, but this error is more easily reduced by updating your current model
of that standard, or jazz in general, and will thus be less likely to cause aversion. Therefore, if
we cannot reduce prediction errors by refining our current model, or our current model fails to
generate predictions and needs to be abandoned altogether, the stimuli will not afford learning
and will be considered unpleasant or boring. Conversely, simple stimuli that align closely to
our model will not afford model improvement and thus will also lead to boredom. Accordingly,
the learning progress hypothesis predicts an inverted U-shaped relation between complexity
and positive affective responses. However, engaging with stimuli with moderate complexity is
not the goal in and of itself but an emergent property of the motivation to maximize learning
progress (Oudeyer et al., 2016). Further, ‘optimal complexity’ is not fixed but will be individual-
and context-specific. Indeed, learning progress itself may transform a stimulus from ‘too
complex’ to ‘just right’ over time.
According to LP, the detection of reducible prediction errors, and thus learning potential, leads
to the mobilization of resources. This includes increases in arousal, sensory gain, and effort to
maximally capitalize on the learning opportunity. An engaged, aroused state enhances the
integration of new input and thus the memory of the relevant stimuli or stimulus features. This
sets off a positive feedback loop wherein an increase in prediction error minimization promotes
further active engagement and motivation to seek out other ‘niches for learning progress’ (see
Figure 2; Oudeyer et al., 2016, p. 11). That is, as one learns, their predictive capacity increases,
thus continually redefining what constitutes learning and thus the nature of the stimuli that
is sought out. For example, participants will attend to more and more complex stimuli as they
gain experience with a given task (Forest, Siegelman, & Finn, 2021).
Curiosity is central to the learning progress hypothesis (Oudeyer et al., 2016). A common
definition of curiosity is the intrinsic motivation for information gain (Dubey & Griffiths, 2020;
Loewenstein, 1994), which highlights its overlap with learning progress. Curiosity can be framed
and studied in terms of epistemic (e.g., in trivia paradigms; Kang et al., 2009) or perceptual
information gain (e.g., with blurred or partially revealed images; Jepma, Verdonschot, van
Steenbergen, Rombouts, & Nieuwenhuis, 2012), with common psychological and neuroscientific
mechanisms. In addition, curiosity can be thought of in terms of a trait, that is, a relatively
stable part of our personality, or a state, wherein certain situations, environments, or stimuli,
temporarily increase the expectation of information gain (i.e., learning). Within LP, the aroused,
engaged state associated with detecting learning potential in the form of reducible prediction
Figure 2 The learning progress
hypothesis. Humans are
intrinsically motivated for
learning progress, which
is operationalized as the
rate of prediction error
minimization over time.
The detection of reducible
prediction errors mobilizes
resources associated with
state curiosity to maximally
capitalize on the learning
potential. Learning progress
is registered as pleasure and
enhances memory encoding,
which in turn facilitates further
learning progress, setting up a
feedback loop. Adapted from
Oudeyer et al., 2016.
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DOI: 10.5334/joc.320
errors is linked to state curiosity and, within the feedback loop, an increase in the minimization
of these prediction errors leads to further state curiosity. The engaged, curious state that
accompanies the detection of learning potential is thought to foster better memory retention.
This aligns with studies showing greater retention of information that elicit greater curiosity
(Marvin & Shohamy, 2016).
Certain theories of curiosity overlap particularly strongly with LP. For example, one theory
suggests that curiosity is driven by the urge to maximize the value of one’s current model/
knowledge, which can explain why curiosity is triggered by moderately complex stimuli in some
situations and novel stimuli in others (Dubey & Griffiths, 2020). Other theories emphasize the
role of prediction errors in spurring curiosity, as they indicate a gap between the input and
ones current model/knowledge and thus uncertainty about their model/knowledge, motivating
further engagement (Gruber & Ranganath, 2019). As in LP, not just any prediction errors will
do as curiosity is stronger as participants feel closer to the answer (Wade & Kidd, 2019), that
is, when they are in the ‘region of proximal learning’ (Metcalfe, Schwartz, & Eich, 2020). This
requires a metacognitive assessment of one’s current knowledge in relation to the input that
determines one’s curiosity about the answer to the trivia question or what the rest of the image
looks like. Conversely, the curiosity-learning cycle that is relevant in a music listening context
is likely to be too fast and automatic to involve such an explicit, metacognitive assessment.
However, as we discuss below, moving to music can provide an overt expression of, and thus
metacognitive access to, our meter-based predictive processes.
Despite its highly dynamic nature, learning progress can be simply operationalized as the
rate of reduction of prediction errors over time; the greater the negative slope of prediction
errors over time, the greater the reward (Oudeyer et al., 2016; Schmidhuber, 2010). In a recent
study, researchers modeled participants behaviour in a free-choice task using an algorithm
that included a linear combination of task performance over all trials and the improvement in
the later compared to earlier trials (Ten, Kaushik, Oudeyer, & Gottlieb, 2021). Models with both
variables best predicted both participants’ choice of task and time spent on each task. This
suggests that participants monitored their learning progress along with overall performance
to choose tasks that were not too easy or too complex but provided an optimal challenge.
Similarly, Brielmann and Dayan (2022) developed a computational model of the aesthetic
value of visual images. This approach involved two generative models, one for predicting the
next image (i.e., the immediate sensory environment) and one for predicting likely future
images in the long term. Participants’ ratings of images were then simulated based on the
degree to which an image aligns with their short-term model (i.e., sensory prediction error)
and the degree to which it improves the longer-term model (longer term learning). This model
accounted for participants’ ratings, including individual differences, as well as changes in ratings
over time (Brielmann, Berentelg, & Dayan, 2023; Brielmann & Dayan, 2022). Importantly, this
work highlights the roles of both predictive processing and learning in affective responses to
sensory stimuli.
LP AND PLUMM
Within the context of LP, we can reframe the inverted U-shaped relation between degree
of syncopation and PLUMM in terms of the reward elicited by an increase in prediction error
minimization and the resolution of metrical uncertainty. As described above, moderately
syncopated rhythms provide both prediction errors that indicate learning potential and
enough regularity to allow for a relatively strong metrical model which can be leveraged to
reduce these prediction errors. Accordingly, these rhythms lead to the greatest engagement
in terms of both affective and motor responses. Conversely, highly syncopated rhythms elicit
so much metrical uncertainty that minimizing or even detecting prediction errors is difficult or
impossible. Therefore, there is little or no potential for learning and thus boredom or aversion.
For simple rhythms with little or no syncopation, most if not all notes align with the metrical
model, therefore there is no prediction errors, no potential for model improvement, again
leading to boredom.
The inverted U associated with PLUMM results from operationalizing rhythmic complexity as the
weighted sum of its syncopations (Fitch & Rosenfeld, 2007; Longuet-Higgins & Lee, 1984; Witek
et al., 2014). However, this measure discounts the influence of individual syncopations and
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DOI: 10.5334/joc.320
how this influence may depend on, and contribute to, the rhythmic context (Sioros, Madison,
Cocharro, Danielsen, & Gouyon, 2022). Due to the relatively fast and dynamic nature of musical
rhythms (e.g., compared to harmonic progressions), responses to individual onsets are difficult
to measure. One approach is to exploit the high temporal resolution of electroencephalography.
For example, one study using this approach shows that fast neural indices of prediction errors
are smaller for more complex rhythms (Lumaca et al., 2019), which presumably give rise to
weaker metrical models. However, this approach cannot assess how individual notes influence
affective responses, how they contribute to modifications to the metrical model, or the relation
between these processes. Outside of rhythm, researchers have used a computational model to
show that curiosity and pleasure are highest for melodies and chord progressions that balance
prediction errors and uncertainty (Cheung et al., 2019; Gold et al., 2019; Omigie & Ricci, 2022).
These results suggest that in low uncertainty contexts in which listeners can form strong
predictions, opportunities for learning—in the form of prediction errors—are highly salient,
leading to greater state curiosity and pleasure. Conversely, when model uncertainty is high,
pleasure is driven by predictable notes that resolve uncertainty and thus reinforce the model.
Applying LP to PLUMM highlights that all three components of reward are likely in play while
engaging with music. That is, ‘wanting’ can be linked to the motivation to reduce prediction
errors and resolve the metrical uncertainty, while ‘liking’ reflects the pleasure resulting from
this process. The ‘learning’ component reflects the association formed between the pleasurable
state and the rhythm or song that facilitated the learning progress. This ‘learning’ can be
framed in terms of means-ends fusion, in which the pleasure associated with learning progress
(the end) gets ‘fused’ to the activity of listening to a particular piece of music (the means;
Melnikoff, Carlson, & Stillman, 2022; Szumowska & Kruglanski, 2020). This association may
also be extended to similar rhythms, songs, or genres, potentially contributing to higher level
preferences or schemas. In most studies investigating PLUMM, participants rate their pleasure
and urge to move while sitting and not moving. Therefore, the urge to move may be experienced
as a (pleasurable) tension (i.e., ‘wanting’; Witek, 2009), driven by metrical uncertainty, and
anticipation of the resolution of this uncertainty via overt or covert movement. This is similar to
the tension participants experience while waiting for the answer to a trivia question (Kang et
al., 2009). This highlights that only reducible prediction errors should elicit PLUMM. The role of
synchronous movement in reducing prediction errors, reinforcing the metrical model, and thus
reducing metrical uncertainty is a key tenet of the predictive processing account of PLUMM. This
has been supported by recent work showing that both PLUMM and PLUMM-related pleasure are
increased when tapping one’s foot to the beat (Spiech, Hope, et al., 2022).
In this context, moving to music externalizes our predictive processes allowing for the
metacognitive assessment of the gap between our current metrical model and the input,
and thus the potential for learning. Linking back to theories of epistemic curiosity (Gruber
& Ranganath, 2019; Metcalfe et al., 2020), movements give the listener explicit feedback
regarding their knowledge gap and whether they are in a ‘region of proximal learning’. Moving
can also expand the representation of the meter and/or draw focus to other aspects of it, thus
expanding these ‘regions’. For example, by embodying the beat, e.g., via foot taps, we offload
this representation, freeing up attention to other (e.g., faster) metrical levels which can then be
embodied by other bodily movements (Burger, Thompson, Luck, Saarikallio, & Toiviainen, 2014;
Mårup, Møller, & Vuust, 2022). However, this metacognitive access is limited by the temporal
dynamics of our perceptual and motor systems. For example, there is a limit to how fast humans
can move, thus limiting our ability to reduce prediction errors at very fast metrical levels (Repp,
2003). In addition, recent theoretical and empirical work suggests that our motor system
underlies and constrains the perception of regular auditory input (Morillon, Hackett, Kajikawa,
& Schroeder, 2015; Poeppel & Assaneo, 2020), which might limit the granularity of our metrical
models. Further, there is a difference between how synchronously we perceive ourselves to
be moving to the beat/meter, and how synchronously we are actually moving (Franěk, Radil,
Indra, & Lánsky, 1987). A recent study showed that perceived synchrony better predicts ratings
of PLUMM than objective measures of synchrony (Matthews et al., 2022), supporting the role of
movement-supported metacognitive assessment in PLUMM. This highlights the intrinsic aspect
of this affective response, i.e., ‘how much I am enjoying myself’ largely depends on how well I
think I am doing.
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A key feature of applying LP to PLUMM is that it centers the individual and their interaction
with the stimuli, rather than the stimuli itself (Oudeyer et al., 2016). For example, the level
of syncopation that will maximally potentiate learning will differ between individuals and
between contexts. This also aligns with predictive processing treatments of affective responses
to music (Schaefer, Overy, & Nelson, 2013). There is evidence that the shape of the inverted
U associated with PLUMM varies according to several inter-individual factors such as musical
training, age, and neurological health (Cameron, Caldarone, Psaris, Carrillo, & Trainor, 2023;
Matthews et al., 2019; Matthews et al., 2020; Matthews et al., 2022; O’Connell, Nave-blodgett,
Wilson, Hannon, & Snyder, 2022; Pando-Naude et al., 2023). According to an LP account of
PLUMM, the state of the metrical model should influence the level of rhythmic complexity that
will maximally afford learning and thus maximize PLUMM. Extensive musical training may lead
to more developed and refined metrical models (Palmer & Krumhansl, 1990; Vuust et al., 2005;
Zhao, Gloria Lam, Sohi, & Kuhl, 2017), thus altering the degree of syncopation that will fall
just beyond a musicians’ predictive capacity. Conversely, healthy aging and Parkinson’s disease
lead to a flattening of the inverted U (Pando-Naude et al., 2023), possibly due to weakening of
the metrical model. However, other factors are likely to contribute, including working memory
(Vuvan, Simon, Baker, Monzingo, & Elliott, 2020), trait curiosity (Galvan & Omigie, 2022),
stimulus familiarity, and preference (Madison & Schiölde, 2017; Senn, Bechtold, et al., 2019).
Memory is a key component of learning, which, in the current context, reflects long term
changes to metrical models to better account for expected future rhythms (Brielmann &
Dayan, 2022). A recent review suggests that the regularity of musical rhythms and the reward
derived from listening to them could improve learning and memory, including for features
that are incidental to the rhythm (e.g., speech; Fiveash et al., 2023). Although our proposal
suggests a different causal direction, i.e., that learning drives music-induced reward, a key part
of LP is that the detection of learning potential mobilizes resources including working memory
and long-term encoding (Oudeyer et al., 2016). Outside of music, there is a positive link
between curiosity and recall, even for stimulus features that are incidental to the information
gain (Gruber & Ranganath, 2019; Kang et al., 2009). Within music, there is evidence of better
recall of pleasurable melodies (Ferreri et al., 2021; Ferreri & Rodriguez-Fornells, 2022; Ferreri &
Rodriguez-Fornells, 2017) as well as a link between intrinsic motivation for learning and recall
(Ripollés et al., 2016). Meanwhile, studies on motor learning using musical sequences suggest a
strong connection between motivation, predictability, liking, and learning performance (Bianco,
Gold, Johnson, & Penhune, 2019; Fasano et al., 2020). Regular auditory rhythms facilitate
perceptual and cognitive performance (Morillon, Schroeder, Wyart, & Arnal, 2016; Stefanics et
al., 2010) which support learning, likely via the entrainment of attentional oscillations (Large &
Jones, 1999). Whereas adding irregularities may disrupt this process, rhythms that are complex
enough to potentiate learning are still regular enough to be accounted for by listeners’ current
metrical model. Therefore, these rhythms may balance the regularity necessary for conferring
perceptual advantages via entrainment, and the effects of intrinsic learning-based reward on
memory processes.
The highly structured nature of music, along with recently developed methods for tracking the
complexity of music in a way that aligns with perception (Pearce & Wiggins, 2012; Senn, 2023),
makes testing LP within musical contexts not only feasible but highly promising. One approach
could be to apply the computational approach of Brielmann et al., (2022; 2023) to rhythmic
stimuli. For example, one could simulate affective responses to rhythms of varying complexity
based on immediate and rhythm-level prediction errors (e.g., using Bayesian surprisal; Senn,
2023), along with the longer term influence on the metrical model (e.g., using Kullback-Liebler
divergence). This would account for both the detection of learning potential of individual
syncopations in the short term, as well as the tracking of learning progress in the longer term.
Another approach could be to test state curiosity directly following the approach of Omigie and
Ricci (2022). For example, participants could be asked to rate their curiosity regarding the way
rhythms of variying complexity will unfold. Depending on their musical training and familiarity
with the stimuli, participants would be expected to show greater curiosity for moderately
syncopated rhythms. These approaches could be combined with neuroimaging, physiological
measures, and/or pharmacological interventions to assess the purported neural mechanisms
underlying LP within PLUMM (see below).
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THE TIME-COURSE OF LP IN THE CONTEXT OF PLUMM
Given the dynamic nature of music, LP and the resulting affective responses, such as PLUMM,
can occur on multiple timescales, from onset to onset, to years of listening to the same song
(Brattico, Bogert, & Jacobsen, 2013). Within a single musical piece, three forms of prediction-
based learning can be considered (Brielmann et al., 2023); 1) each onset elicits a prediction
error or confirmation signal depending on its alignment with the metrical model, 2) these
signals are integrated over short epochs relevant to the meter (e.g., phrases or repetitions), 3)
to improve longer-term, more stable models thus reducing prediction errors encountered in
similar rhythms in the future. Therefore, the specific time-course over which learning progress
is monitored and leads to affective responses will need to be determined. There is evidence that
participants can form accurate aesthetic judgements of music within 500 or 750 ms, however,
these initial aesthetic responses are likely based on timbral or harmonic information (Belfi et al.,
2018). Conversely, PLUMM relies on temporal processes requiring at least one or two beat cycles
and reflects a low-level but protracted affective response rather than an aesthetic judgment.
Brief, more intense responses can also occur, for example resulting from a slow build up
and sudden resolution of metrical uncertainty, a common motif in electronic dance music.
Alternatively, a relatively low complexity rhythm may initially be misinterpreted with regards
to the type (e.g., 3/4 vs 4/4) or phase of the meter. Altering the meter or its phase can then
lead to a sudden reduction of prediction errors (Fitch & Rosenfeld, 2007) and thus an increase
in pleasure. These examples may correspond to rhythmic versions of an ‘aha moment’ like that
seen in epistemic curiosity where providing the answer to trivia question provides a sudden
resolution of uncertainty (Gruber & Ranganath, 2019). A similar example is found in atonal
music where the initial lack of perceived structure leads to uncertainty and an exploratory
mode of listening (Mencke et al., 2022; Mencke, Omigie, Wald-Fuhrmann, & Brattico, 2019).
Eventually, the underlying structure is discovered, leading to a sudden reduction in uncertainty
and a brief yet strong increase in pleasure (Mencke et al., 2022).
As discussed above, moving to a rhythm provides a way to decrease predictions errors while
revealing new avenues to learning progress. In addition, synchronous movements, or those
perceived as synchronous, can provide prediction confirmation signals, and thus a fast and
salient indication of learning progress. Therefore, through a decrease in prediction errors and an
increase in prediction confirmations, synchronous movement can increase pleasure, suggesting
a causal directionality. However, the urge to move, framed as the ‘wanting’ component of
reward, is itself potentially pleasurable. Further, refinement of the metrical model may
be necessary before one has the urge to move. For example, a prerequisite for moving to a
rhythm is perceiving a beat. Then, moving to the beat would lead to further pleasure as more
uncertainty regarding the metrical model is reduced. This suggests that the two components
of PLUMM are likely bidirectional, engaging both ‘wanting’ and ‘liking’ components of reward in
repeating and continuously evolving cycles.
Anecdotally, a given piece of music can induce PLUMM even after many years of regular listening.
This may result from the ‘learning’ component of reward and means-ends fusion, where a song
or rhythm becomes strongly associated with a motor or affective response even as learning
progress is exhausted. This component may also account for the fact that our tastes tend to
solidify at an early age when many such associations are being formed (Krumhansl & Zupnick,
2013). Music can be thought of as a multidimensional space that can be explored as it unfolds
over time. Due to our limited attentional capacities, we may focus on only a subset of this
space at a given moment or take a more holistic mode of listening (Brattico, Brattico, & Vuust,
2017). Therefore, repeated listening can continue to uncover new sources of learning progress
that are only apparent, or draw interest, as attention moves within this space. This implies
that the degree to which a given piece of music affords learning and induces pleasure over
repeated listens will depend on its complexity. Outside of PLUMM, there is evidence that more
complex melodies lead to a greater increase in liking over repeated listens (Smith & Cuddy,
1986), however, others have shown decreases in liking and/or no dependence on complexity
(Gold et al., 2019; Madison & Schiölde, 2017). Further, many songs rated high in PLUMM, such
as those from James Brown or The Meters (Janata et al., 2012), can be quite simple in structure
and relatively sparse in terms of instrumentation. One possibility is that relatively sparse,
repetitive music facilitates a highly detailed metrical model for which relatively small changes
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can have relatively large impacts. One example is microtiming, in which small deviations from
the metrical grid can lead to reducible prediction errors even within a simple repeating pattern.
In this way, new learning potential is uncovered as focus shifts to the finer grained details of a
rhythmic pattern. Therefore, the effects of familiarity are complex and likely depend on several
factors, including the range of complexity, the ecological validity of the stimuli and listening
contexts, genre familiarity, and inter-individual differences.
THE NEUROSCIENTIFIC UNDERPINNINGS OF LP IN THE CONTEXT
OF PLUMM
Theoretical and empirical work strongly implicate nigrostriatal dopamine within motor
corticostriatal networks as crucial for beat- and meter-based timing (Cameron, Pickett,
Earhart, & Grahn, 2016; Cannon & Patel, 2020; Grahn & Brett, 2009). This is in part based on
evidence from those with Parkinson’s disease, which is characterized by reduced nigrostriatal
dopamine, leading primarily to motor problems, but also changes to cognitive and affective
processes (Dauer & Przedborski, 2003). Participants with Parkinson’s show a reduced ability to
discriminate rhythms (Cameron et al., 2016; Grahn & Brett, 2009) and judge rhythms as more
complex compared to healthy controls (Vikene, Skeie, & Specht, 2019). There is also evidence
that musical training counteracts the effect of Parkinson’s on beat perception (Hsu, Ready, &
Grahn, 2022). The motor corticostriatal loop, which connects premotor cortical regions and
dorsal striatum and overlaps with the nigrostriatal dopamine pathway (Alexander, DeLong,
& Strick, 1986), is associated with motor learning (Graybiel & Grafton, 2015). Crucially, the
motor corticostriatal loop is strongly associated with processing predictable rhythmic auditory
patterns (Bengtsson et al., 2009; Grahn & Brett, 2007, 2009; Grahn & Rowe, 2013; Kung et al.,
2013; Matthews et al., 2020; Schubotz et al., 2000; Thaut, 2003). These results, along with the
flattening effect of Parkinson’s on the inverted U (Pando-Naude et al., 2023), suggest that the
nigrostriatal pathway and motor corticostriatal loop are crucial for the metrical models and
predictive processes thought to underlie PLUMM (see Figure 3A).
Along with its role in beat-based timing, dopamine within the mesolimbic pathway is known
to play a crucial role in the motivation for primary rewards (i.e., ‘wanting’) and the formation
of value-stimulus associations (i.e., ‘learning’; Berridge & Kringelbach, 2015). The mesolimbic
pathway involves dopaminergic neurons in the ventral tegmental area projecting to the ventral
striatum. The ventral striatum forms the limbic corticostriatal loop with ventromedial prefrontal
cortex (Alexander et al., 1986), and is implicated in the experience and anticipation of primary,
secondary (Berridge & Kringelbach, 2015; Schultz et al., 1997), and music-induced rewards
(Gold et al., 2019; Martinez-Molina, Mas-Herrero, Rodríguez-Fornells, Zatorre, & Marco-Pallarés,
2019; Martínez-Molina et al., 2016; Mas-Herrero, Maini, Sescousse, & Zatorre, 2021; Salimpoor
et al., 2011, 2013). Outside of music, mesolimbic dopamine is purported to encode reward
prediction errors (RPEs; Schultz, 2016a) which reflect the difference between the expected
magnitude of a reward and the actual reward received. In this context, dopamine does not
encode the consummatory experience of reward (‘liking’), which is likely controlled by the
endogenous opioid system, but is instead involved in the predictive processes necessary for
reward-based learning (Berridge & Kringelbach, 2015; Schultz, 2016b).
Recent evidence strongly implicates dopamine in music reward processing. Interestingly, this
includes the anticipation of peak pleasurable moments (i.e., chills) during music listening, the
motivation to buy preferred music, and the experience of musical pleasure (Ferreri et al., 2019;
Salimpoor et al., 2011). This suggests, that unlike primary and secondary rewards, dopamine’s
role in musical pleasure includes ‘liking’, along with ‘wanting’ and ‘learning’. Activity in the
ventral striatum was also linked to RPEs associated with chord progressions (Gold et al., 2019).
However, there is debate regarding how exactly music or musical features can be considered
better or worse than expected in terms of (extrinsic) reward (De Fleurian, Harrison, Pearce, &
Quiroga-Martinez, 2019; Gold et al., 2019; Hansen et al., 2017). Further, this still leaves open the
question of how music-induced reward comes about in the first place.
Along with dopamine’s role in beat-based timing (Cameron et al., 2016; Cannon & Patel, 2020;
Grahn & Brett, 2009), the above results suggest that dopamine is crucial to the proposed role
of LP in PLUMM. We propose that mesolimbic dopamine signals learning potential via reducible
prediction errors, triggering state curiosity, and the associated increase in arousal and attention
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(see Figure 3B). In this model, nigrostriatal dopamine within the motor corticostriatal loop
underlies the metrical model and its relative certainty via phasic pulses and tonic dopamine
signals, respectively (Cannon & Patel, 2020; Tomassini, Ruge, Galea, Penny, & Bestmann,
2016). Meanwhile, mesolimbic dopamine within limbic corticostriatal loop detects reducible
prediction errors (e.g., syncopations) leading to the mobilization of sensory and cognitive
(including memory) resources. This aligns with work implicating dopamine in the link between
intrinsic motivation, learning, liking, and memory retention in both language (Ripollés et al.,
2018) and melody tasks (Ferreri et al., 2021) via a circuit formed by the ventral tegmental
area, the hippocampus, and ventral striatum (Ripollés et al., 2018). In non-human primates,
dopamine is modulated by information gain even when this gain involves sacrificing a primary
reward (Bromberg-Martin & Hikosaka, 2009). Similarly, in humans, activity in ventral striatum
and dopaminergic midbrain increases along with curiosity about the answer to a trivia question,
but not when the answer is given (Gruber, Gelman, & Ranganath, 2014). Rather than reward
anticipation, this may reflect the reward that accompanies state curiosity. This proposed role
of dopamine within LP aligns well with theories linking dopamine to sensory prediction errors
and their certainty (Friston et al., 2014; Gershman & Uchida, 2019), rather than RPE’s per se.
On the other hand, framing dopamine’s role within LP may provide a bridge between these two
hypotheses. That is, since intrinsic reward is linked to learning progress, dopamine may increase
along with greater-than-expected learning progress and thus greater-than-expected reward.
The LP hypothesis implies a system in the brain that monitors learning progress derived from
a stimulus or activity. For the longer term modification of metrical models, this role may be
served by lateral prefrontal cortex (cf. Gruber & Ranganath, 2019). According to a recent
model, monitoring learning progress in the shorter term may be driven by the dorsal anterior
cingulate cortex (dACC; Silvetti, Vassena, Abrahamse, & Verguts, 2018). The ACC is linked with
state curiosity and the mobilization of resources in the face of an information gap (Gruber &
Ranganath, 2019). For example, ACC activity is positively associated with perceptual curiosity
(Jepma et al., 2012) and melodic prediction errors (Omigie et al., 2019). Further, the ACC is active
when listening to rhythms judged as beautiful (Kornysheva, Cramon, Jacobsen, & Schubotz,
2010). The dACC receives dopaminergic input from the ventral tegmental area (Silvetti et
al., 2018), which according to our model, signals reducible prediction errors relative to the
metrical model (see Figure 3B). At some threshold, dACC signals the locus coeruleus which
releases norepinephrine both back to the dACC, and more widely in the cortex. An increase in
norepinephrine leads to greater sensory gain, attention, arousal, and increased effort (Mather,
Clewett, Sakaki, & Harley, 2016), in other words, leads to the engaged, ready-to-learn state
associated with state curiosity.
Norepinephrine is strongly linked with pupil dilation, which has been used as an objective
measure of state arousal and effort (Wilhelm, Wilhelm, & Lüdtke, 1999). Recent studies have
shown that listening to music with higher PLUMM leads to greater pupil dilation (Bowling,
Ancochea, Hove, Fitch, & Madison, 2019). Another study showed greater pupil response for
rhythms considered low or medium in PLUMM, particularly when the isochronous hihat
was removed (Skaansar, Laeng, & Danielsen, 2019) which may increase reducible metrical
uncertainty to further potentiate learning. Finally, pupil dilation as well as drift in pupil dilation
over time show an inverted U-shaped function with rhythmic complexity (Spiech, Danielsen,
Figure 3 A neuroscientific
model of the LP account of
PLUMM. A) Phasic pulses of
nigrostriatal dopamine into
the dorsal striatum initiate
cycles of meter-based timing
mechanisms via excitatory
and inhibitory signals within
the motor corticostriatal
loop. Adapted from Cannon &
Patel, 2020. B) The detection
of reducible prediction errors
relative to the metrical model
leads to mesolimbic dopamine
signals to the hippocampus
to enhance memory, and
to the dACC which in turn
activates the LC to release
norepinephrine, leading to the
mobilization of attentional
resources. The PFC updates
metrical models along with
higher level schemas. Adapted
from Ripollés et al., 2016 and
Silvetti et al., 2018. dACC,
dorsal anterior cingulate
cortex; Hipp, hippocampus; LC,
locus coeruleus; NAc, nucleus
accumbens; PFC, prefrontal
cortex; SMA, supplementary
motor area; SN/VTA,
substantia nigra/ventral
tegmental area; VP, ventral
pallidum.
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Laeng, & Endestad, 2023; Spiech, Sioros, Endestad, Danielsen, & Laeng, 2022). This pattern
of pupil drift was only seen in participants with stronger beat perception, while weaker beat
perceivers showed a flattened pupil drift response, supporting the link between strength of the
metrical model and state curiosity. In the current context, a stronger metrical model would
lead to stronger prediction errors, resulting in greater dopamine signalling to the dACC-locus
coeruleus network, greater norepinephrine release, and thus arousal associated with state
curiosity. This is further supported by recent work showing greater pupil responses to pitch
deviants in more certain melodic contexts (Bianco, Ptasczynski, & Omigie, 2020) as well as a
link between pupil dilation and epistemic curiosity (Kang et al., 2009).
PERSPECTIVES ON LP, CREATIVITY, INTERPERSONAL
SYNCHRONY, AND PLUMM
In the following section, we suggest that, in addition to determining the stimuli that humans
seek out and enjoy, curiosity and an intrinsic motivation for learning progress may also drive
how they create and interact with music (e.g., via dancing). A generative model can only be
improved by finding its limits, which can then be expanded as new information is integrated.
Accordingly, one role of creative activities may be to self-generate stimuli that challenge our
predictive capacities and thus maximally potentiate learning (Schmidhuber, 2010). The classic
definition of a creative product has two components; 1. Originality, novelty, or innovation, and
2. Functionality, usefulness, utility, or fit (Runco & Jaeger, 2012). That is, a creative product or
act needs to be both novel and useful, however, in more abstract forms of art, such as music,
what constitutes utility is less obvious. Within LP, the utility of a creative act or product is the
degree to which it affords learning (Schmidhuber, 2010). That is, the novelty or surprise leads
to prediction errors, while the utility is determined by the reducibility of the prediction errors.
Within PLUMM, this suggests generating rhythms and music that optimally challenge our own
meter-based predictions to maximize our own learning progress, pleasure, and fun. As our
experience increases and our models improve, it requires more musical skill and knowledge
to generate rhythms that provide an optimal challenge. Therefore, in this context, the intrinsic
motivation to learn goes hand in hand with the intrinsic motivation for competence and
knowledge (Oudeyer & Kaplan, 2007).
The optimal balance between challenge and skill is also a crucial contributor to the psychological
construct of flow, which is characterized by a pleasurable, absorptive feeling of high fluency
in the context of a relatively difficult task (Csikszentmihalyi, 1990). Fluency of performance,
a dimension of flow (Engeser & Rheinberg, 2008), was shown to be positively associated
with motor synchrony with moderately and highly syncopated rhythms (Stupacher, 2019).
This finding suggests that moving to the beat will only induce flow if there is some challenge
(Abuhamdeh & Csikszentmihalyi, 2012). A recent model of flow suggests that an activity will
induce flow only insofar as it reduces uncertainty regarding the associated goal (Melnikoff et al.,
2022), which, in the current context, is learning progress. Therefore, moving to music will lead
to flow only if it affords learning, thus helping to narrow which music one will prefer to move to.
This can also be linked to music creation in the form of musical improvisation (Norgaard, Bales,
& Hansen, 2023; Pressing, 1988) which can be framed as self-reproducing the curious state via
the generation of reducible prediction errors. In this context, musical creativity can be seen as
a form of exploratory play wherein one can expand their predictive capacities within a uniquely
structured and multidimensional space.
This highly structured context also creates an ideal environment for collaborative activities which
can further amplify learning progress. Beat and meter allow for a shared, or extended, predictive
model across listeners and/or performers thus providing a common structure within which to
interact and create (Savage et al., 2021; Stupacher et al., 2022; Vuust et al., 2022; Witek, 2019).
Whether dancing or making music, this common metrical model allows for synchronization
between individuals as well as with the music, and thus a common space for learning progress
where others’ meter-based predictions and prediction errors are communicated via movement.
In this context, learning is amplified as people observe the externalization of others’ predictive
processes, incorporating and building upon these observations in a cyclic fashion to generate
positive feedback loops of learning (Oudeyer et al., 2016). Therefore, a social setting can
facilitate collective confirmation and violation of predictions, thus increasing learning progress
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beyond what would occur alone. For example, prediction errors from a complex rhythm far
beyond one’s predictive capacity may still be reduced by observing a more skilled dancer
moving to this rhythm (Foster Vander Elst, Vuust, & Kringelbach, 2021). Alternatively, a simple,
and thus potentially boring rhythm can be made more interesting as other dancers embellish
the rhythm with more complex movements. This suggests that the strong link between
interpersonal synchronization and social bonding (reviewed in Fiveash et al., 2023; Savage et
al., 2021) may be in part driven by shared learning progress.
Interpersonal rhythmic synchronization has been shown to increase affiliation among
participants (Kokal, Engel, Kirschner, & Keysers, 2011), including in young children and even
infants (Cirelli, Einarson, & Trainor, 2014; Kirschner & Tomasello, 2009). However, synchronization
alone is unlikely to drive the pleasure and affiliation from musical interactions such as dancing at
a club or collective improvisation if it does not facilitate learning. To increase learning potential,
reducible complexity needs to be injected into the activity. Evidence for this comes from a
study using the mirror game paradigm in which dyads try to synchronize their movements but
are not instructed with regards to the movements they make. How much the members of the
dyad like each other are not only driven by the degree of synchronization but also the degree
of complexity of the movements (Ravreby, Shilat, & Yeshurun, 2022). Indeed, participants
sacrificed synchrony to increase complexity, suggesting that they prioritized learning and thus
the fun of the activity, which in turn increased affiliation. Similarly, in the context of rhythmic
music, the perceived affiliation between virtual avatars depends not only on the synchrony with
the rhythm but also the complexity of the rhythm (Stupacher, Witek, Vuoskoski, & Vuust, 2020).
SUMMARY
Here we expand on predictive processing accounts to suggest that the intrinsic motivation for
learning progress is a crucial driver of PLUMM, providing a thorough and testable explanation of
this powerful and ubiquitous affective response to music. Crucially, this proposal accounts for
inter-individual and contextual influences on the inverted U-shaped relation between PLUMM
and rhythmic complexity. In addition, this proposal ties together prominent psychological and
neuroscientific theories of reward, motivation, and learning, with prediction as a fundamental
underlying principle. We suggest that the feedback loop linking learning progress, PLUMM,
and memory retention is subserved by dopaminergic and noradrenergic transmission within
loops connecting cortical, subcortical, and brainstem regions. This forms the link between
neural mechanisms underlying motor and reward processing, which together motivate
active, exploratory learning, and creative social interactions. The highly structured nature of
music provides an ideal testbed for individuals and groups to test and refine their predictive
processes and thus generate learning progress. Further, this structured, and thus tractable,
nature makes rhythmic music highly amenable for the investigation of the link between
curiosity, learning progress, aesthetic pleasure, and creativity. Along with new technological
developments and approaches, these investigations can provide exciting possibilities for
researchers to better understand the important role of music in our lives as well as its utility
in clinical settings.
ETHICS AND CONSENT
Ethical approval and/or consent was not required.
ACKNOWLEDGEMENTS
The authors would like to thank David Quiroga-Martinez for helpful discussions, and Jessica
Thompson, along with two anonymous reviewers for their comments on this manuscript.
FUNDING INFORMATION
The Center for Music in the Brain is funded by the Danish National Research Foundation
(DNRF117).
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COMPETING INTERESTS
The authors have no competing interests to declare.
AUTHOR AFFILIATIONS
Tomas E. Matthews orcid.org/0000-0003-4538-0898
Center for Music in the Brain, Department of Clinical Medicine, Aarhus University Hospital, Nørrebrogade
44, Building 1A, 8000 Aarhus C, Denmark; Royal Academy of Music, Skovgaardsgade 2C, DK-8000 Aarhus
C, Denmark
Jan Stupacher orcid.org/0000-0002-2179-2508
Center for Music in the Brain, Department of Clinical Medicine, Aarhus University Hospital, Nørrebrogade
44, Building 1A, 8000 Aarhus C, Denmark; Royal Academy of Music, Skovgaardsgade 2C, DK-8000 Aarhus
C, Denmark
Peter Vuust orcid.org/0000-0002-4908-735X
Center for Music in the Brain, Department of Clinical Medicine, Aarhus University Hospital, Nørrebrogade
44, Building 1A, 8000 Aarhus C, Denmark; Royal Academy of Music, Skovgaardsgade 2C, DK-8000 Aarhus
C, Denmark
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TO CITE THIS ARTICLE:
Matthews, T. E., Stupacher,
J., & Vuust, P. (2023). The
Pleasurable Urge to Move to
Music Through the Lens of
Learning Progress.
Journal of
Cognition,
6(1): 55, pp. 1–22.
DOI: https://doi.org/10.5334/
joc.320
Submitted: 09 December 2022
Accepted: 17 August 2023
Published: 13 September 2023
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