pupil response reflects processing of polyrhythm and microtiming in musical grooves… · 2018. 1....
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Pupil response reflects processing of
polyrhythm and microtiming in musical
grooves: A study of musicians and non-
musicians
Jo Fougner Skaansar
Submitted as cand.psychol. thesis
Department of Psychology
UNIVERSITY OF OSLO
October 2017
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Pupil response reflects processing of polyrhythm and microtiming in musical grooves: A study of musicians and non-musicians
By Jo Fougner Skaansar
Submitted as cand.psychol. thesis
Department of Psychology
UNIVERSITY OF OSLO
October 2017
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© Jo Fougner Skaansar
2017
Pupil response reflects processing of polyrhythm and microtiming in musical grooves:
A study of musicians and non-musicians
Author: Jo Fougner Skaansar
Supervisors: Bruno Laeng & Anne Danielsen
http://www.duo.uio.no
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Abstract Author: Jo Fougner Skaansar. Title: Pupil response reflects processing of polyrhythm and
microtiming in musical grooves: A study of musicians and non-musicians. Supervisors:
Bruno Laeng & Anne Danielsen.
Background and objectives: Instances of polyrhythm (cf. Vuust et al., 2006) and
microtiming (cf. Danielsen, Jensenius, & Haugen, 2015) appear commonly in groove-based
music, where they may challenge the listener’s internal framework of timing regularities,
often referred to as meter. Two influential theories, The Dynamic Attending Theory (DAT;
Jones & Boltz, 1989) and Predictive Coding Theory (PC; Vuust & Witek, 2014, cf. Friston
2005) hypothesize that attentional effort is recruited when metrical frameworks are
challenged. The present study addresses this hypothesis by asking: During listening to
musical groove-excerpts, are instances of polyrhythm and microtiming asynchrony related to
an increase in ‘mental effort’ (as indexed by pupillometry; Kahneman, 1973), as well as a
decrease in quality of sensorimotor synchronisation (as indexed by reduced finger tapping
accuracy)? In addition, the common assumption that microtiming promotes groove
experience is investigated. Of special interest is looking at effects of musical expertise on the
processing of polyrhythmic and microtiming events.
Method: Two experiments were designed by the author with help from the
supervisors. Data collection was done by the author. In Experiment 1, professional jazz
musicians (N = 16) and non-musicians (N = 16), matched demographically, were exposed to
a groove-based musical excerpt with a 4 against 3 polyrhythmic event that was contrasted
with a similar but non-polyrhythmic excerpt (stimuli borrowed from Vuust et al., 2006). In
Experiment 2, participants were exposed to original recordings of double bass and drum kit-
grooves of varying structural complexity, manipulated into five distinct microtiming
bass/drums-asynchrony conditions (-80 ms < X < 80 ms). All musical stimuli were presented
in a passive condition (‘listening only’) and an active tapping condition (‘synchronising with
the beat’). We recorded pupil diameter sizes and participants gave 1) their subjective ratings
after listening to each clip in the passive condition, and 2) tapping responses during the active
condition by pressing a key on the keyboard of a PC.
Main results: In Experiment 1, as expected, exposure to polyrhythm was related to
larger pupil sizes (more effort) and lower tapping accuracy compared to the control
condition. In Experiment 2, magnitudes of bidirectional bass/drums-microtiming
asynchronies were positively related to pupil dilation and negatively related to tapping
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accuracy. In both experiments, tapping the beat while listening yielded higher
psychophysiological effects than when listening only. Thus, the main effects of polyrhythm
and microtiming on pupil response and tapping accuracy supported both DAT and PC
accounts. Neither instances of polyrhythm or microtiming generated significant differences in
pupil response between musicians and non-musicians. However, professional jazz musicians
consistently showed superior tapping accuracy compared to non-musicians, which reflects
their enhanced expertise in rhythm perception and performance. On subjective ratings of
groove, participants generally preferred the on-the-grid grooves more than the grooves with
microtiming. Musicians showed a greater response than non-musicians on these ratings,
demonstrating their enhanced sensitivity to microtiming features in musical groove contexts.
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Acknowledgements For quite a few years I have been a psychology student during the day time and a jazz
musician at night. By doing a research project in music cognition, leading to this thesis, I had
a great opportunity to merge my two areas of interest. Throughout my musical career, timing,
microtiming, polyrhythm and groove, are all concepts I have had an intense, but still rather
tacit relationship with for many years. Thanks to outstanding support from my supervisors
Bruno Laeng (Department of Psychology, ‘RITMO Centre for Interdisciplinary Studies of
Rhythm, Time and Motion’) and Anne Danielsen (Department of Musicology, ‘RITMO
Centre for Interdisciplinary Studies of Rhythm, Time and Motion’), I was able to take a deep
dive into psychological aspects of rhythm and translate my musical and academic curiosity
into intriguing research questions, experimental design and, eventually, some very interesting
results. I owe both my supervisors great thanks; this project would not have been possible
without their extraordinary help and enthusiasm. Thanks to Bruno and Anne, I was also
fortunate to present a poster based on this thesis at the ‘Conference on Music & Eye-
Tracking’ that took part at the Max Planck Institut für Empirische Ästhetik, Frankfurt, 16-19
August, 2017.
I want to thank the participants who offered their time for participating in this study. I
would also like to express gratitude to my colleague and friend Jakop Filip Janssønn Hauan
for playing the drums on my microtiming stimuli, Martin Torvik Langerød at the Department
of Musicology for engineering the double bass recordings and preparing the experimental
sound clips, Kristian Nymoen at the Departments of Musicology/Informatics for preparation
of tapping data with custom MATLAB analyses, Gorm Helfjord for graphic aid on some of
the figures, and Rebekah Oomen for language consultation. Moreover, Dag-Erik Eilertsen at
the Department of Psychology, University of Oslo, as well as Peter Vuust and Maria Witek at
the ‘Center for Music in The Brain’ at Aarhus University, Denmark, gave me some valuable
ideas and comments on the project. A last thank you goes to my family, friends and Hedda
for invaluable support throughout this year.
The following are credited in the text with their initials:
-Anne Danielsen (AD)
-Martin Torvik Langerød (MTL)
-Kristian Nymoen (KN)
-Jakop Filip Janssønn Hauan (JJH)
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Table of Contents 1 Introduction.............................................................................................................................12 Theoreticalfoundation........................................................................................................42.1 Musicperceptionisenabledbyenculturationandrefinedbymusicaltraining..42.2 Perceptionofrhythm,beatandmeter................................................................................52.2.1 MeterperceptionI:MetricEncodingTheoryandDynamicAttendingTheory.............62.2.2 MeterperceptionII:PredictiveCodingTheory..........................................................................82.2.3 Developmentandneuralbasisofbeatandmeterperception.............................................8
2.3 Synchronisingtomusicalrhythm.........................................................................................92.4 Groove.........................................................................................................................................102.4.1 Twostructuralpropertiesofgroove-basedmusic.................................................................11Polyrhythm...........................................................................................................................................................................12Microtiming..........................................................................................................................................................................13
2.5 Pupillometry:ameasureofcognitiveprocessing........................................................152.5.1 Mentaleffortinthestudyofrhythmandmeterperception..............................................17
3 Thepresentstudy...............................................................................................................183.1 Hypothesesandpredictions................................................................................................183.2 Methods.......................................................................................................................................203.2.1 Participants..............................................................................................................................................20MusicalEarTest(MET)...................................................................................................................................................21
3.2.2 Stimuli........................................................................................................................................................22Experiment1:ThePolyrhythmExperiment..........................................................................................................22Experiment2:TheMicrotimingExperiment.........................................................................................................22
3.2.3 Setupandprocedure...........................................................................................................................243.2.4 Dataprocessing......................................................................................................................................27
4 Results....................................................................................................................................294.1 MusicEngagementQuestionnaire.....................................................................................294.2 Ageneralpupillarydriftingeffect......................................................................................294.3 Experiment1:ThePolyrhythmExperiment..................................................................304.3.1 Pupilresponses......................................................................................................................................304.3.2 Tappingaccuracy..................................................................................................................................30
4.4 Experiment2:TheMicrotimingExperiment..................................................................314.4.1 Pupilresponses......................................................................................................................................314.4.2 Tappingaccuracy..................................................................................................................................334.4.3 Ratingscales............................................................................................................................................35
5 GeneralDiscussion.............................................................................................................385.1 GeneraleffectsonpupilresponsesandtappingaccuracysupportDATandPC385.2 Effectsofmusicalexpertise..................................................................................................395.2.1 Pupillaryresponsewasunaffectedbymusicalexpertise....................................................405.2.2 Musiciansoutperformednon-musiciansintappingaccuracy...........................................42
5.3 Microtimingandtheexperienceofgroove.....................................................................435.4 Grooveexperience,effortandcomplexity......................................................................455.5 Limitationsandmethodologicalconsiderations..........................................................485.6 Futurestudies...........................................................................................................................49
6 Conclusion.............................................................................................................................507 Bibliography.........................................................................................................................517.1 References..................................................................................................................................517.2 Discography...............................................................................................................................63
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7.3 Filmography..............................................................................................................................648 Appendix................................................................................................................................688.1 FiguresA1-A7............................................................................................................................688.2 TablesA1-A5.............................................................................................................................71
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1 Introduction Music is a significant characteristic of human beings (Brown, 2004), found in all cultures
today and throughout history (Blacking, Byron, & Nettl, 1995). Listening to music may
enrich us with strong aesthetic experiences and emotions, recall of pleasant memories and the
urge to move our bodies. Music psychology traces back to the end of the 19th century and is
the fast-growing interdisciplinary
scientific field studying brain processes,
subjective experience and behaviour in
relation to the vibrations and sounds that
are categorized as music (Bonde, 2009).
Covering the field’s depth and width,
The Oxford Handbook of Music
Psychology (Hallam, Cross, & Thaut,
2016) presents theory and empirical
findings on cognitive perspectives,
aesthetics and emotional responses to
music, performance, improvisation,
music therapy and music education, to
name a few. Musicologists,
psychologists, philosophers,
anthropologists and computer scientists are among the scholars involved today in music
psychological research (Figure 1).
Music principally consists of melody, harmony, timbre/colour and rhythm. The
present study investigates the perception of rhythm; specifically, by placing under scrutiny
two rhythmic aspects, namely polyrhythm and microtiming. Polyrhythms can be defined as
(at least) two metric reference structures elapsing simultaneously within a single rhythmic
texture (Danielsen, 2006; Vuust, Roepstorff, Wallentin, Mouridsen, & Østergaard, 2006).
Microtiming refers to subtle timing asynchronies in music (Danielsen, 2015; Keil & Feld,
1994; Kilchenmann & Senn, 2015). Polyrhythm and microtiming are commonly occurring in
groove-based genres, including rock, jazz and hip-hop (Roholt, 2014), funk (Danielsen,
2006) and electronic dance music (EDM; Butler, 2006). Grooves are repetitive musical
patternsthat have a danceable quality to them. Moreover, they often contain certain features
of structural complexity (Pressing, 2002; Witek, 2017) that are assumed to contribute to a
Figure 1. Scope of the scientific discipline of music psychology. From Eagle (1996).
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groove experience. A groove experience refers to our urge to move our bodies and
synchronise with the music (Davies, Madison, Silva, & Gouyon, 2013), as well as pleasant
affect (Janata, Tomic, & Haberman, 2012).
Rhythm is experienced in the interaction of sounding events and underlying,
organizing and dynamic mental models (Danielsen, Jensenius, & Haugen, 2015). Theoretical
accounts of the relations between rhythmic input and temporal cognitive structures have been
addressed by Dynamic Attending Theory (DAT; Jones, 2016; Large & Jones, 1999; Large &
Snyder, 2009) and Predictive Coding Theory (PC; Vuust & Witek, 2014). The rhythmic
complexity added by polyrhythm and microtiming features may challenge listeners’
fundamental cognitive structures for musical timing (meter), a process that according to DAT
and PC demands attentional resources (effort; Danielsen et al., 2015; Vuust, Gebauer, &
Witek, 2014). Hence, the main objective of the present study, is to empirically test the
following theoretical prediction derived by DAT- and PC-accounts: Listening exposure to
rhythmic features (i.e., polyrhythm and microtiming asynchrony) in musical groove contexts
that challenge listeners’ metric framework, is 1) positively related to a psychophysiological
measure of mental effort like pupillometry, and 2) negatively related to a behavioural
measure of sensorimotor synchronisation quality, like finger tapping accuracy. According to
several cognitive psychologists, effort reflects the intensity of processing demands in the
brain (Just, Carpenter, & Miyake, 2003; Kahneman, 2011) and is often measured by phasic
pupil size diameter changes with the psychophysiological method of pupillometry, commonly
based on eye-tracking technology (Laeng, Sirois, & Gredeback, 2012). Finger-tapping
paradigms remain popular in investigating mechanisms of sensorimotor synchronisation
(Repp, 2005; Repp & Su, 2013). Sensorimotor synchronisation refers to ‘the coordination of
rhythmic movement with an external rhythm’ (Repp & Su, 2013, p. 403). Fluctuations in
intra-individual tapping accuracy have been found to reflect variation of external rhythmic
complexity (Chen, Penhune, & Zatorre, 2008), while inter-individual tapping accuracy
differences may reflect variation in musical/rhythmic expertise (e.g., Hove, Keller, &
Krumhansl, 2007).
In addition to testing the prediction above specifically, we have three questions of
interest that ‘tap’ into ongoing scientific endeavours. First, we will examine to what extent
microtiming influences the experience of groove and quality of sensorimotor synchronisation
(Danielsen et al., 2015; Davies et al., 2013; Kilchenmann & Senn, 2015). Second, we aim to
explore possible systematic relations between effort and groove experience, moderated by
rhythmic structural complexities and features. Third, by comparing professional musicians
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with non-musicians on the cognitive processing of polyrhythm and microtiming, the study
contributes to documenting effects of music exposure, music-related activity and music
training on brain plasticity, as manifested in behaviour, cognition, brain structure and
function (Aheadi, Dixon, & Glover, 2010; Angulo-Perkins et al., 2014; Gaser & Schlaug,
2003; Hansen, Wallentin, & Vuust, 2012; Hove et al., 2007).
The present study involves two experiments that were designed and carried out by the
author and supervisors. Both experiments feature a paradigm where music clips were
presented auditorily to demographically-matched groups of musicians and non-musicians.
Infrared eye-tracking equipment recorded participants’ pupil sizes during music listening. In
addition, in some conditions, the participants tapped the perceived beat on a key of the
computer keyboard and gave subjective ratings after the trials.
Following this brief introduction, we will continue by defining and elaborating on
concepts and terms necessary for a thorough discussion of the topics at hand. We look at
‘enculturation’ and active musical experience as premises for enabling and refining music
perception and prediction in general. Afterwards, we will exclusively focus on musical
rhythm and its relevant concepts. Theoretical frameworks on rhythm, beat and meter
perception are presented, before we examine research and concepts related to movement to
music, groove and the rhythmical features of polyrhythm and microtiming. The present study,
its goals and purpose, as well as the specific hypotheses, will be clarified in more detail,
followed by presentation of methods and results. Finally, we will discuss our results in light
of our research questions, existing theory and empirical data.
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2 Theoretical foundation 2.1 Music perception is enabled by enculturation
and refined by musical training ‘Engaging with music is the brain’s equivalent to a full body workout’. The words are taken
from a recent TED-education movie (Collins, 2014), summing up cognitive neuroscience
research of music from the last decades. Indeed, music listening constitutes the processing of
a continuous stream of complex auditory signals that apparently activates ‘the whole’ brain,
in particular the auditory association areas in the temporal lobe, auditory working memory
areas in the frontal lobe, and the emotional centres within the limbic system (Peretz &
Zatorre, 2005). Music performance is an even more demanding endeavour, termed one of the
most cognitively challenging tasks the brain can undertake – a complex and precise interplay
between auditory, somatosensory, motor, premotor and prefrontal areas (Klein, Liem,
Hänggi, Elmer, & Jäncke, 2016; Zatorre, Chen, & Penhune, 2007). The latter is also
considered a typically studied domain of expertise at both the cognitive and behavioural
levels (besides such activities as chess-playing, reading and sports; Ericsson, 1996).
The framework for understanding incoming local auditory events as meaningful musical
units develops from the very beginning of our life, as we (perhaps in concert with genetic
pre-dispositions) gain basic music perception skills from passive culture-specific music
exposure (enculturation; Hannon & Trainor, 2007). Thousands of hours of attentive but also
inattentive, incidental musical experience leaves us with an arsenal of tacit knowledge,
schemas and – importantly – predictions, concerning how our culture’s music normally
unfolds over time. For example, which tone combinations are most probable to occur, how
chord changes are tending to proceed, how rhythmic patterns are usually built up, and so on
(Huron, 2006; Pearce, Ruiz, Kapasi, Wiggins, & Bhattacharya, 2010). Exposure to these
statistical regularities of music ‘enculturates’ us into the music’s ‘grammar’, a process similar
to acquiring our mother tongue on the basis of being passively exposed to language (Sloboda,
1985). In addition, the research literature has extensively documented how active music
engagement changes the way music is perceived. It is well established that musical training
enhances domain-specific music perception, with evidence from behavioural studies (Hove et
al., 2007; Rammsayer & Altenmüller, 2006; Tervaniemi, Just, Koelsch, Widmann, &
Schröger, 2005) and brain imaging (Alluri et al., 2017; Fujioka, Trainor, Ross, Kakigi, &
Pantev, 2005; Vuust et al., 2005). Positive effects of musical training when dealing with
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auditory, non-musical tasks have also been reported (Aleman, Nieuwenstein, Böcker, & de
Haan, 2000; Martens, Wierda, Dun, De Vries, & Smid, 2015). In fact, musicians’ brains do
look different in connectivity and local cortical size than non-musicians’ brains (Gaser &
Schlaug, 2003), but remarkably, changes in neural connectivity in novices has been detected
already after a few months of attending regular drumming lessons (Amad et al., 2017).
Finally, music listening alone may have an enhanced effect on non-musical activities, like
spatial tasks (Aheadi et al., 2010), keeping perceptual awareness over time (Olivers &
Nieuwenhuis, 2005), and social cooperation (Anshel & Kipper, 1988).
2.2 Perception of rhythm, beat and meter Like the perception of all complex stimuli in our environment, perceiving music involves a
delicate interaction between bottom-up and top-down processes in our brains. Moving on to
the field of rhythm perception, the bottom-up/top-down interplay has been given considerable
research attention, especially between rhythm and meter. To understand the distinction
between the two, we observe that music is a temporal phenomenon and rhythm is often
defined as patterns of distinct time duration (‘inter-onset-intervals’) that are actually present
in sounding music (London, 2012). Its perception is assumed to be aided by innate capacities
like general gestalt principles of grouping, for example proximity, similarity and continuity
(Clarke, 1999; Jones, 2016). An organizing temporal mental framework in rhythm perception
is the one of sensed meter, which is a perceptual and cognitive phenomenon (Jones, 2016;
London, 2012; Palmer & Krumhansl, 1990). Sensitivity to meter is evident when we
spontaneously (and pre-attentively; Damsma & van Rijn, 2017) stomp – usually isochronous
– beats along with music, and forms our experience of timing regularities, including periodic
shifting between strong and weak beats. More extensively defined, meter may be understood
as a rhythmic virtual reference structure or point of departure, providing listeners and
performers with, in principle, any possible scaffolding aspect to make sense of the rhythmic
structure (Danielsen, 2018; Danielsen et al., 2015). This includes idiosyncratic patterns of
individual top-down matrices that may also involve non-isochronous temporal relations. To
clear up the terminology, we will in the following understand meter as an internal framework
when attending to rhythm, consisting of hierarchically grouped beats with related (also non-
isochronous) subdivisions. A related concept to meter perception, beat perception (Honing,
2012; also termed beat induction), addresses the inferring of beats only – a detection of a
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regular pulse in an auditory signal (Winkler, Háden, Ladinig, Sziller, & Honing, 2009),
without subdivisions or hierarchical groupings.
An experience of beat and meter is fundamental to our capability to synchronise to
musical rhythm. Different metrical frameworks of the same rhythmic material represent
different perceptual experiences (Iversen, Repp, & Patel, 2009) and an absence of a sense of
meter contributes to the feeling of ‘being lost’, i.e., not being able to grasp what is
rhythmically going on. Importantly, the internal metric framework does not need to actually
be played to be comprehended by the listener; it may simply be evoked by cues within the
rhythmic texture (Honing, 2012).
The perception of beats has been linked in neuroscience to activations within basal
ganglia and cerebellum, primary, premotor and supplementary motor areas (Grahn & Brett,
2007), while the processing of metric aspects is especially associated with activity in
language areas of the inferior frontal gyrus (BA47; Vuust, Wallentin, Mouridsen, Østergaard,
& Roepstorff, 2011). There have also been experimental attempts to reveal neural substrates
related to the processing of syntactical complexity (syntax here refers to the ways in which
the notes of the pattern are rhythmically combined) in rhythmic structure (i.e., rhythmic
patterns; more related to bottom-up than to top-down processes). In a recent study aimed at
investigating brain response to increase in syntactical complexity in a drum groove, brain
areas were found to include the left cerebellum, the right inferior frontal gyrus (comprising
BA47) and superior temporal gyri on both sides (Danielsen, Otnæss, Jensen, Williams, &
Østberg, 2014). Interestingly, it has been suggested that syntactical processing in language
and music partly shares neural resources (Patel, 2003).
2.2.1 Meter perception I: Metric Encoding Theory and Dynamic
Attending Theory Several cognitive theories have accounted for our ability to infer and experience meter from
sounding rhythm. The Metric Encoding Theory (Povel & Essens, 1985) suggests that
listeners sense of meter arises from cues in the actual pattern of sounds, often regular accents
in the subdivisions. A series of ‘mental beats’ are induced – what the authors call an ‘internal
clock’ (Abernethy, 1988; Povel & Essens, 1985). This inner series of beats will continue to
be endorsed, until the sounding music’s accents contain too many counter-evidence events,
i.e., too little clock-fit. At this threshold, the listener’s sense of meter is given up in favour of
another reference structure, or possibly a feeling of confusion or just ‘float by’. The Metric
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Encoding Theory has been criticized on several points: for its ‘rigidity’ in describing inner
beats as a stable clock with fixed lengths between beats (not adjusting to timing variations),
in taking into consideration too little the use of the body in the process, and in not positing
multiple time level relationships as aids in meter perception (for an extensive review of
critique, see Jones, 2016).
The Dynamic Attending Theory (DAT; Jones, 2016; Jones & Boltz, 1989) addresses
all of the above aspects. With support from neuroscientific evidence, DAT claims that as
external rhythmic events are paid attention to, neural population oscillations are set into
action, defined as rhythmic or repetitive activity (Fujioka, Trainor, Large, & Ross, 2009;
Large, Herrera, & Velasco, 2015; Lehmann, Arias, & Schönwiesner, 2016; Nozaradan,
Peretz, Missal, & Mouraux, 2011). These neural oscillations are closely connected with overt
body movement, as will be addressed later, and correspond to what in DAT is termed the
attending rhythm. Specifically, the oscillations represent multiple time levels of what is heard
– rhythm – or imagined through a metric framework (Iversen, Repp, & Patel, 2005).
Furthermore, being self-persistent, the oscillations ‘expect’ stability. Their cyclic nature
entails anticipations concerning the placement of the next pulse beat and hence, cues to the
temporal allocation of attentional energy (Jones & Boltz, 1989). Such anticipations help us
direct our attention adaptively, so that the efficiency of the processing of the incoming
stimulus is increased (Greenberg & Larkin, 1968). DAT proposes that high synchronisation
of the oscillations with the external sound source sharpens the attentional focus, giving a
higher degree of expectancy violations when perturbations occur. Expectancy violations, e.g.,
small timing asynchronies, are continuously taken into account, widening the attentional
focus for each perceived beat in order to encompass deviations or onsets, or adjusting the
oscillation phases (Danielsen et al., 2015). Importantly, although not stated explicitly by the
theorists, we may assume that such adjustments demand attentional effort.
The hypothesis of metric binding extends DAT (Jones, 2016). When different
oscillations are internally co-activated over time, association links are formed, binding the
oscillations together. This contributes to the listener’s internalizing of, and familiarity with
different meter and rhythm categories. The persistence of these ‘binded’ oscillations is
dependent on their phase relationship fit and the listener’s ability to predict the following
rhythmic events. Some oscillation phase ratios, for instance 2:1, are more stable than 4:3 (a
polyrhythm, see section 2.4.1) and require less time for the nesting to take place. Extensive
musical experience helps listeners both mentally activate complex oscillation ratios
(Stupacher, Wood, & Witte, 2017), and flexibly alternating their attention between
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oscillations at different time levels (Palmer & Krumhansl, 1990) – also when oscillations are
not ‘supported’ in the external music. It may be assumed that activating and maintaining
complex oscillation ratios generally demands attentional effort on the part of the listener. As
its name implies, the Dynamic Attending Theory is indeed a dynamic one, emphasizing the
top-down/bottom-up interaction of perception of rhythm, with continuous adapting to the
nature of the external input.
2.2.2 Meter perception II: Predictive Coding Theory As it was suggested both in the sections on enculturation and DAT, anticipation is an
important part of music perception. In fact, Huron (2006), building on Leonard B. Meyer’s
classic theory (1956), places the anticipations, fulfilments and violations of these at the very
heart of our aesthetic experience and emotional response to music. The broadly scoped
Predictive Coding Theory (PC; originally proposed by Friston, 2005) takes the notion of
prediction further in explaining rhythm and meter perception (Vuust & Witek, 2014). The
main assumption is that the brain generally operates with the ultimate goal of ‘predicting the
future’. Taken into a rhythm context, incoming (rhythmic) stimuli are continuously compared
or matched against a mental model (meter) formed from enculturation and formal musical
training. Mental models give rise to anticipations of what will happen next in the meeting of
sensory stimuli and contextual information. Different models might compete in making sense
of the incoming rhythms, and the internal model that is favoured tends to be that which best
anticipates incoming data. In other words, PC claims that meter is the internal cognitive
model that ‘minimizes the error’ between the brain’s predictions and the incoming rhythmic
surface structure. As in DAT, mental models are dynamic and open for in-the-moment
updates with the incoming musical information, and the prediction error and following
adjustments and updates are assumed to demand effort.
2.2.3 Development and neural basis of beat and meter perception With evidence from brain activity measures, Winkler et al., (2009) showed that newborn
infants are able to perceive the beat from incoming rhythm. Similarly, experiments
employing habituation/dishabituation-paradigms that associate auditory sequences with
visual fixation times have demonstrated that infants of only two months of age discriminate
between series of isochronous beats with small tempo differences (Baruch & Drake, 1997)
and by 7 months infer metrical frameworks from rhythmic patterns (Hannon & Johnson,
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2005). On the basis of findings like these, it has been argued that innate predisposition alone
can account for the perception of beats (Honing, 2012). However, it is also likely that sources
of enculturation may provide substantial contributions; unquestionably, when it comes to
meter perception skills, experience seems to be a crucial component for development and
shaping. Important enculturation factors are pre- and post-natal exposure (imprinting) to
basic biological rhythms such as breathing (0.1-1 Hz), walking (1-3 Hz) or the rhythm of
speech (3-10 Hz) in caregivers and others (Hannon & Trainor, 2007; Iyer, 2002), as well as
culture-specific aspects. Effects of enculturation have been found in empirical investigations.
For example, according to Hannon & Trehub (2005), by six months of age, infants respond
similarly to disruptions of rhythms from different cultures (Western versus Balkan music),
but by one year of age they respond more readily according to their own culture’s rhythmic
systems. Furthermore, Phillips-Silver & Trainor (2005) demonstrated the influence of
multimodal caregiving on meter perception. Infants that over a period of time had been
rocked by their mothers, but only according to every second or every third beat in a non-
accentuated pulse train, later showed a preference for accents that corresponded to their
‘rocking’ rhythm pattern (every second or third beat).
If enculturation shapes the perception of meter, does extensive musical training enhance
it? Some results have suggested that elementary (Western) meter perception is independent of
musical sophistication (Bouwer, Van Zuijen, & Honing, 2014; Damsma & van Rijn, 2017),
while others suggest that musicianship and expertise fundamentally develop metrical
frameworks enhancing perception of rhythm (Chen et al., 2008; Drake, Penel, & Bigand,
2000; Geiser, Sandmann, Jäncke, & Meyer, 2010; Matthews, Thibodeau, Gunther, &
Penhune, 2016; Stupacher et al., 2013, 2017; Vuust et al., 2005). When it comes to more
complex rhythmic structure (after Western standards), musicians have indeed been found to
be more sensitive than non-musicians in perceiving and/or superior in synchronising to
complex meters (Snyder, Hannon, Large, & Christiansen, 2006), musical syncopations
(Ladinig et al., 2009), polyrhythmic musical texture (Jones, Jagacinski, Floyd, & Klapp,
1995) and onset (microtiming) asynchronies (Hove et al., 2007).
2.3 Synchronising to musical rhythm Music and movement are strongly interrelated dimensions and music typically co-exists with
dance (Brown, 2004). As mentioned in the introduction, sensorimotor synchronisation is the
coordination of rhythmic movement with an external rhythm (Repp & Su, 2013), observed
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even in toddlers (Kirschner & Tomasello, 2009), and mostly considered an ability of humans
(Wallin & Merker, 2001; but for evidence in other animals see: Fitch, 2013; Patel, Iversen,
Bregman, & Schulz, 2009). The concept of entrainment is built on DAT and provides a
theoretical account, as it refers to the process where two different oscillations are
synchronised – often the internal attending rhythm (i.e., neural oscillations) with an external
source (i.e., sounding music; Jones, 2016). Some authors theoretically separate mental
models and body movement, describing how bodies are coordinated with the external sounds
but synchronised with the internal model that predicts the beat and meter of the music (Repp
& Su, 2013). An embodied perspective on cognition, on the other hand, places body activity
at the heart of rhythm perception (Iyer, 2002). According to this perspective, perception and
cognition are assumed to be shaped by body activity. There are even (philosophical)
proponents, claiming that rhythm is experienced and grasped actively, non-analytically and
non-cognitively, within the body itself (Roholt, 2014). As dance and movement to music
involves the whole body, there is a growing scientific enthusiasm in quantifying body
movement trajectories with, for example, motion sensors (Jensenius, 2009). However, most
research on sensorimotor synchronisation has so far been done in the form of finger-tapping
paradigms (see Repp & Su, 2013 for a review of recent findings), where the participant is
typically asked to intentionally tap a finger in synchrony with isochronous beats.
2.4 Groove The concept of groove captures the three aspects of sounding rhythmic properties,
embodiment and pleasure (Witek, 2017). First, grooves are musical patterns that have a
certain rhythmic, symmetrical, continuously repeating and danceable quality to them
(Pressing, 2002). Groove-based genres include the ‘Black Atlantic rhythms’ (jazz, soul,
reggae, hip-hop, funk; Pressing, 2002) and contemporary computer-programmed styles (e.g.
electronic dance music, EDM; Butler, 2006). Second, groove can also be seen as a
psychological construct, being a subjective, affective and sensorimotor response to groove-
based music. One definition, ‘wanting to move some part of the body in relation to some
aspect of the sound pattern’ (Madison, 2006, p. 201), treats groove purely as a sensorimotor
phenomenon, whereas another, ‘the groove is that aspect of the music that induces a pleasant
sense of wanting to move along with the music’ (Janata et al., 2012, p. 56), adds in the third
aspect: the affective component. Related to the latter, Danielsen (2006) argues that presence
and pleasure are important characteristics of a groove experience, while Roholt (2014) on his
11
side, discusses how groove may evoke feelings of tension and relief, depending on how
rhythms are experienced in the body.
Groove-based music is intended to make us entrain to the beat, especially activating
the locomotion (‘walking’) part of the sensorimotor system (Iyer, 2002). A strong link has
been found between a feeling of being ‘in the groove’ and the wish to synchronise body
movement to music (although movement constraints do not necessarily affect groove ratings;
Janata et al., 2012). It has similarly been reported that people move more (head and feet) to
high-groove tunes than low-groove tunes (Janata et al., 2012). However, the relation may not
be one-to-one, since Kilchenmann & Senn (2015) found that expert musicians moved more to
a rhythm excerpt that they rated less groovy. The authors suggested that musicians in this
situation used their body primarily to compensate for a fuzzy rhythmic situation and not to
respond to a groove experience. Despite the fact that the groove term is especially used in
relation to Afro-American and derived music heritage, a proposal has been made that
experience of groove is nevertheless a cross-cultural phenomenon (Madison, 2006). There
may, however, be individual and culture-specific differences in being able to perceive
particular grooves in particular musical styles (Roholt, 2014), again emphasising an
important role of culture-specific enculturation.
2.4.1 Two structural properties of groove-based music Which are the metrical, rhythmic and microrhythmic features of a groove that are actually
making it groove? The laborious work of unlocking the structural secrets behind groovy
music has begun to pay off. It turns out that some elements are related to structure that
affords successful entrainment, including temporal information that increases rhythmic
predictability, such as a ‘locomotion-friendly’ tempo (Janata et al., 2012), repetition
(Danielsen, 2006), structural ‘low-level’ features like event density (‘the density of sound
events between beats generally’; Madison et al., 2011, p. 1579), and beat salience ('the degree
of repetitive rhythmical patterning around comfortable movement rate'; Madison et al., 2011,
p. 1581). At the same time, however, it seems essential that certain forms of complexity that
challenge the listener’s perception of, and create tension in relation to, the sensed meter, are
also some of the ingredients. Regarding this, researchers have investigated and discussed, for
example, the role of syncopation density (‘changes in rhythmic emphasis from metrically
strong to metrically weak beats’; Witek et al., 2014, p. 2: Sioros, Miron, Davies, Gouyon, &
Madison, 2014), polyrhythm (Danielsen, 2006; Vuust et al., 2014) and microtiming
12
(Butterfield, 2010; Danielsen et al., 2015; Iyer, 2002). The latter two – polyrhythm and
microtiming – are especially dealt with in the subsequent sections, as they are central topics
of the present study.
Polyrhythm
Polyrhythm refers to two (or more) beat levels, played by one or more performers, elapsing
simultaneously within a single rhythmic texture, for example 3:2 (‘3 against 2’), 2:3, 4:3, 3:4,
5:3 and 5:4. Of several types of polyrhythm, one is cross-rhythmic counter-rhythms
(Danielsen, 2016, p. 62; from now on termed 'cross-rhythms') where two (or more)
competing pulses appear in a single musical context; when experienced within a metric
framework, one is perceived as the main beat level and the other as the counter-rhythm.
Cross-rhythms are extremely common in groove-based music. In West African traditions for
example, continuously competing reference structures appear as a rule. The characteristic
12/8 rhythms can be understood in cycles of 3, 4, 6 or 8 (e.g., in Salif Keita’s 1987-tune
Wamba). In contemporary EDM, integrated two-bar 4:3 cross-rhythmic patterns are typical,
for example in Till The World Ends (2011) by Britney Spears or Where Are Ü Now (2015) by
Skrillex. Similarly occurring multiple double- and half-time-feels are also common, as in
Destiny’s Child’s Nasty Girl (2001; analysed by Danielsen, 2015). Also in jazz, cross-
rhythms are an essential part of the musical language. Vuust & Roepstorff (2008) gave an
extensive analysis of polyrhythm in Miles Davis’ 1960s quintet.
There are clear analogies in perceptual
effects between auditory cross-rhythms and
some forms of so-called ‘illusory’ perception,
for example, bistable visual percepts (Jastrow,
1899). The picture shown in Figure 2 may be
seen either as rabbit or a duck, giving rise to two
completely different, and mutually exclusive,
gestalts. Similarly, cross-rhythms are bistable
and ambiguous auditory percepts, that will be
experienced differently depending on which
internal metric framework is endorsed; that is, which beat level is perceived as the main level
and the counter-rhythm, respectively (Vuust & Witek, 2014). Although it is possible to hear
both competing pulses at the same time, or alternate between them, simple polyrhythms are
often heard as a merged rhythmic texture, not as two separate streams (Deutsch, 1983; Kurtz
Figure 2. A bistable percept. From Jastrow (1899).
13
& Lee, 2003). Keeping and sticking to a given metrical model is possible both when the
acoustic salience is on the counter-rhythm, and even when the meter is not acoustically
actualized at all. The latter, however, is considered a cognitively demanding task, since it
forces a beat level that is not present in the music, to be internally maintained.
Microtiming
The tones, chords and hits played by musicians are typically not in full temporal synchrony
(even if musical training greatly helps; Gérard & Rosenfeld, 1995). Asynchronies emerging
from limitations in perceptual, cognitive and motor faculties have been characterized as
‘motor noise’ and ‘discrepancies’ (Rasch, 1988). However, certain forms of microtiming,
subtle timing asynchronies in music, are also systematically and intentionally (although not
always consciously) applied for expressive purposes (Bengtsson, Gabrielsson, & Thorsen,
1969; Clarke, 1989; Collier & Collier, 1996; Danielsen et al., 2015; Iyer, 2002; Keil, 1987).
The musician or ensemble may play rhythmically ‘outside’ a presumed norm (the norm most
commonly constitutes an isochronous beat series/metric grid), there may occur subtle tempo
changes or there may be asynchronies between the onsets of the player’s or players’ tones.
For example, in some pianists’ solo performances (Palmer, 1996) and string trios (Rasch,
1988), the melody line is often played 5-50 ms ahead of the rest of the parts. Jazz players can
be distinguished by their personal rhythmic signature – ‘feel’ – that gives to each of them a
coherent and recognizable delivery of notes and phrases (Iyer, 2002; Pressing, 2002). In jazz
drumming, microtiming of cymbal swing ratios (short-long eighth-note patterns) has been
extensively studied (Friberg & Sundström, 2002). Consistent timing asynchronies have been
discovered between piano, drums and bass in typical swing accompaniments (Collier &
Collier, 1996). Also, ‘trade-off’-effects in terms of the relative lengths of the beats in 4/4 bars
(hierarchical groupings of four and four beats) are common when jazz bands perform swing
pieces (Ross, 1989, from Collier & Collier, 1996); some beats (1st and 3rd) being consistently
shorter and some beats (2nd and 4th) consistently longer. In R&B music, artists like D’Angelo
have taken microtiming to an extreme level, including systematic timing asynchronies
between the drums and electric bass of up to 90 ms (Danielsen, 2010a; Danielsen et al.,
2015). Also, in contemporary groove based computer-made music, microtiming has become
an integral part of the rhythmic fabric, where timing nuances can be programmed in Digital
Audio Workstations, without expert instrumentalists having to play them (Danielsen, 2010b,
2015). Interestingly, in some traditions, for example drumming music from Mali (Polak,
14
2010) and Norwegian fiddle music (Haugen, 2015), a temporal hierarchic reference structure
is lacking and microtiming ‘deviations’ constitute the actual norm.
Remarkably, despite being a seemingly essential component of musical rhythm,
microtiming is often not consciously perceived (Hove et al., 2007). Still, there is reason to
believe that notes containing subtle timing discrepancies will stand out from notes placed on
the rhythmic grid, tune our attention towards them, and our perceptual system will treat them
as something worth a closer analysis (Iyer, 2002). Musically, this might help reaching a
greater perceptual salience for some structural aspects of the music, like melodic material or
even single tones (Iyer, 2002; Palmer, 1996). The Participatory Discrepancy Theory (Keil,
1987; Keil & Feld, 1994) suggests that subtle timing asynchronies are also one of the active
components contributing to the experience of groove. Keil (1987, p. 277) writes: ‘It is the
little discrepancies between hands and feet within a jazz drummer’s beat, between bass and
drums, between rhythm section and soloist, that create the swing [e.g., groove] and invite us
to participate’. This is in line with for example jazz musicians’ first-hand experience (see
Collier & Collier, 1996), often discussing rhythmic nuances like ‘pushing’, ‘pulling’, or
playing ‘laid back’ or ‘on the beat’ when playing groove-based music. Similar to Keil, Witek
(2017) suggests that rhythmic complexity (mainly discussing musical syncopations) requires
a certain degree of active participation on the part of the listener to bodily ‘fulfil’ the groove.
The same can perhaps be said for microtiming (and also polyrhythm), as it sets up patterns of
tension and equilibrium toward listeners’ anticipations of a metrical pulse (Roholt, 2014).
This may have behavioural implications, as Danielsen and colleagues (2015) found a
qualitative change in bodily entrainment with a rhythm when a microtiming pattern was
introduced: bodily synchronisation was drawn towards the added asynchrony-onset.
Moreover, they saw a more pronounced entrainment to the half note level of the rhythm,
probably because more complex microtiming obscured the exact location of beats at several
quarter note positions. Both were assumed to be signs of the metric adjustment following the
challenging of the listeners’ fine-grained temporal expectations. Importantly, though, despite
extensive theorizing and anecdotal evidence pointing to the causal link between the structural
element of microtiming and groove experience, experimental studies investigating this notion
have revealed inconsistent results (Butterfield, 2010; Davies et al., 2013; Frühauf, Kopiez, &
Platz, 2013; Kilchenmann & Senn, 2015; Senn, Kilchenmann, von Georgi, & Bullerjahn,
2016).
15
2.5 Pupillometry: a measure of cognitive processing Until now, we gave a short introduction on enculturation and musical training’s effects on
music perception in general, before we narrowed the focus to musical rhythm. We looked at
rhythm, beat and meter perception as well as body movement to music, the concept of groove
and two structural features of groove-based music, namely polyrhythm and microtiming. As
was discussed, both DAT and PC imply some degree of mental effort when perceptually
processing (or playing) rhythm in relation to a corresponding meter, especially when the
metric model is challenged and there is need for internal adjustments. It was also discussed
how such a pressure of metric models, generated by certain rhythmic complexities, in some
instances seem to promote the groove experience. Finally, many findings have pointed to the
more sensitive and refined rhythm and meter perception skills of musicians. Such skills may
possibly influence the mental effort recruited when perceiving rhythm and rhythmic
complexity in relation to metric frameworks. Therefore, it may be very relevant to investigate
‘musical effort’ in musicians and non-musicians, related to the processing of polyrhythmic
and microtiming events.
In his classic book Attention and Effort, Kahneman (1973) first proposed the
psychological concept of mental effort, a dimensional construct that he claimed was most
precisely measured by monitoring diameter changes of the eye pupil – a method typically
called pupillometry (Laeng et al., 2012; see also Kahneman, 2011, chapter 2). At the time
Kahneman’s book was released, psychological experiments had already been using
pupillometry for about two decades. The very first studies applied the methodology to
measure people’s pupillary responses in relation to affective arousal, mainly from pictures
(Hess & Polt, 1960). Later it became clear that changes in pupillary size could also reflect the
‘intensive’ side of attention, the capacity facet rather than the selection facet (Hess & Polt,
1964; Laeng et al., 2012), namely mental effort in Kahneman’s terms of allocation of
cognitive resources to a current mental task or behavior. Implicit in this view is that brain
processing resources are continuously allocated via top-down attentional processes towards
task-relevant stimuli in order to meet current demands. Mental effort refers to variation in
those processing demands (e.g., Alnæs et al., 2014). In other words, increased pupil size
reflects an increase in mental (cognitive) workload (Lean & Shan, 2012). Importantly, a same
task may result in different levels of mental effort, on the grounds of individual differences in
ability, cognition, and temporary psychological and physiological state (Lean & Shan, 2012).
16
In recent years, it has become clear that the brain regulates the level of processing
resources via changes in arousal in various systems of the brainstem. The locus coeruleus
(LC) is a key arousal system that appears specifically involved in cognitive ‘arousal’ more
than vigilance or waking state per se. The LC is also the noradrenergic (NE) ‘hub’ of the
brain and it is located in the pons on both sides. Recent research confirms that the LC-NE
system regulates noradrenergic neuromodulation and it concomitantly causes pupillary
changes in relation to these cognitive operations (Joshi, Li, Kalwani, & Gold, 2016).
Experiments have revealed high correlations between pupil response and neuronal activity in
the LC (Rajkowski, Majczynski, Clayton, & Aston-Jones, 2004). Pupil dilation may reach up
to 20% from baseline levels as a result of cognitive processing alone, and some strong
feelings like pain and orgasm may dilate the pupil even more. However, variations from
illumination can account for about 120% dilation change (Laeng et al., 2012). Therefore,
pupillometry must be conducted in luminance-controlled conditions and with specialized
equipment. Most cognitive-related changes of the pupil are difficult to see with the naked
eye, and they may be ‘swamped’ by luminance change in daily contexts.
The pupil response has been found to index external events with high temporal
resolution (Damsma & van Rijn, 2017; Laeng, Eidet, Sulutvedt, & Panksepp, 2016).
Furthermore, as Laeng and colleagues (2012) point out, cognitive processing does not need to
be conscious to be reflected in pupil size. For example, amnesics (Laeng et al., 2007) and
blindsight patients (Tamietto et al., 2009) respond with variations in pupillary diameters even
when lacking conscious awareness of exposure to significant stimuli. It seems also difficult to
control pupil size voluntarily (Laeng et al., 2012), which makes the pupil a particular reliable
(honest) signal of the underlying processing.
Measures of pupillary response have been shown to be an indicator of several
attentive and pre-attentive cognitive mechanisms, including interest and affective processing
(Laeng et al., 2016; Partala & Surakka, 2003), processing of language (Schluroff, 1982),
surprise and violation of expectation (Damsma & van Rijn, 2017; Friedman, Hakerem,
Sutton, & Fleiss, 1973), cognitive conflict/interference (Laeng, Ørbo, Holmlund, & Miozzo,
2011), working memory load (Granholm, Asarnow, Sarkin, & Dykes, 1996), and
perceptual/attentional shifts (Einhäuser, Stout, Koch, & Carter, 2008).
17
2.5.1 Mental effort in the study of rhythm and meter perception A common approach in the study of brain activity related to rhythm and meter perception has
been the mismatch-negativity (MMN) component of event related potentials (ERPs) in
electroencephalography (EEG; Näätänen et al., 2007), where magnitude and latency of early
EEG-signals index a mismatch between expectation and what is heard (e.g., Honing, 2012;
Vuust et al., 2005). The methodology has been used for example to test specifically PC
theory, where typically single time-locked rhythmic ‘oddballs’, that is, rhythmic deviants or
omission of one or more beats, have been linked to an electrophysiological response. The
response has been interpreted as degree of expectation violation between rhythm and
underlying meter. Similar to the MMN component, time-locked pupil dilations have also
been employed to signal metric violation/‘surprise’ effects to rhythmical deviants (Damsma
& van Rijn, 2017). In some studies, stronger responses to single rhythmic deviants in
musicians than non-musicians have been observed, which have been accounted for by the
enhanced sensitivity of musicians to musical incongruity, caused by stronger metric models,
and seemingly supporting PC (Brattico et al., 2008; van Zuijen, Sussman, Winkler, Näätänen,
& Tervaniemi, 2005; Vuust, Ostergaard, Pallesen, Bailey, & Roepstorff, 2009).
As for the present study, an alternative approach to EEG was adopted, namely monitoring
time-averaged pupillary diameters while participants listened to continuously running
rhythms with varying degrees of structural complexity, but without time-locked, single
deviants. We decided to employ this approach to provide a measure of attentional demands
(e.g., mental effort) as participants adjusted their internal metrical model over a period of
time in accordance with the incoming rhythmic texture in order to reduce mismatch error, as
DAT and PC specifically propose. An advantage is that the present paradigm encompasses
both the initial response (e.g., prediction error) and the following adjustment of the metric
framework to ‘fit’ the incoming rhythm, and may therefore capture a different process than
for example the MMN-studies above. Individual and group (musicians vs. non-musicians)
differences in effort, as revealed by the pupils, may be interpreted as differences in perceptual
ability, revealing that differently experienced brains indeed process and analyze musical
information differently.
18
3 The present study Two experiments were designed and carried out to explore professional jazz musicians’ and
non-musicians’ processing of musical polyrhythm and microtiming in groove contexts. Our
dependent variables consisted of three complementary data sources (Jack & Roepstorff,
2002), covering several aspects of pre-attentive and conscious processing: 1) Levels of
mental effort, as measured by group-averaged pupil diameter change; 2) Quality of
sensorimotor synchronisation (tapping accuracy), operationalized as standard deviation of
tapping offset from rhythmic reference points; and 3) Subjective ratings (in The Microtiming
Experiment only) of groove (‘wanting to move the body to the music’) and ‘musical well-
formedness’. All musical stimuli were presented in a passive (‘listening only’) condition and
an active (‘synchronising with the beat’) tapping condition.
For The Polyrhythm Experiment (Experiment 1), we borrowed from the paradigm
used by Vuust and colleagues (2006) two 30-second excerpts from a single ‘real’ musical
track, namely Sting’s tune Lazarus Heart (1987). Specifically, we wanted to investigate how
cross-rhythmic events (4:3) influenced pupillary change and tapping accuracy compared to a
comparable but non-polyrhythmic excerpt. In The Microtiming Experiment (Experiment 2),
we exposed participants to 30-second long original double bass and drum grooves of varying
structural complexity by increasing the number of musical syncopations and note onsets per
bar into three complexity levels. Within two of these three levels we compared the same
grooves with and without hi-hat eighth notes, to directly measure a ‘Timekeeping’ effect,
being a type of ‘event density supporting metric reference’. Finally, for all groove excerpts,
we systematically changed the asynchrony magnitude between the double bass and the drums
into five distinct microtiming conditions. We were mainly interested in microtiming, but also
how other rhythmic features (syncopation/note onsets per bar and timekeeping) were related
to effort, sensorimotor synchronisation and subjective ratings.
3.1 Hypotheses and predictions Our general hypotheses concerning effort and tapping accuracy were partly generated by the
two influential theories Dynamic Attending Theory (DAT) and Predictive Coding Theory
(PC). However, the present study should be seen primarily as exploratory, since previous
experiments on the same topics are lacking. Also, predictions derived from the above theories
seem somewhat unclear with respect to several aspects of the present stimuli material and,
19
most importantly, the two theories generate few obviously competing predictions concerning
effort in relation to rhythm and meter perception. However, it is reasonable to argue that
according to both PC and DAT, the degree of correspondence between metric framework and
continuously incoming rhythm is negatively related to both processing intensity within the
perceptual system (mental effort) and sensorimotor synchronisation accuracy. Hence, when
specific rhythmic instances challenge listeners’ metric frameworks, this will decrease the
meter/rhythm correspondence, thus increase prediction error and concomitantly the need for
adjustment. This would happen in order to fit the metric framework to the incoming rhythm,
as both theories propose, compared to when meter is not challenged by such rhythmic
features. Increased prediction error and internal adjustment demands should together yield
increased mental effort and decreased sensorimotor synchronisation accuracy.
Polyrhythm and microtiming are rhythmic features that are assumed to challenge
metric frameworks, as they both deviate from a ‘normal’ on-the-grid rhythmic texture, built
around a single and unambiguous pulse. In Experiment 1, listeners will face a competing and
distracting counter-rhythmic pulse level on ‘top’ of an acoustically absent main beat level
(i.e., cross-rhythms) that disturbs their fundamental sense of beat and meter. Hence,
maintaining the original beat level is a cognitively demanding task. As for Experiment 2, the
task is more related to temporally placing the beat exactly than internally maintaining a beat
level. In other words, what represented the challenge when exposed to microtiming,
according to DAT and PC accounts, are the (micro)-adjustment of participants’ mental metric
framework/oscillation phases and/or wideness of attentional focus to encompass the onset
asynchronies. To summarise, we predicted that the cognitive processing of polyrhythm and
microtiming in groove contexts should increase pupil dilation and decrease tapping
accuracy, compared to when such rhythmic instances are not present.
In accordance with extensive empirical research documenting effects of musicianship,
it can also be claimed on the grounds of DAT and PC that musicians possess stronger, more
refined metric models than non-musicians. We expected this to be manifested by consistently
higher tapping accuracy in musicians than non-musicians. Our hypotheses are however less
clear in terms of possible pupillary effects of rhythmical expertise. On one side, stronger
metric models could result in higher perceptual sensitivity and hence more prediction error
when faced with rhythmic perturbations and complexities. On these grounds, one would
expect pupil dilation to be larger for musical experts. This is in line with the MMN-studies
that found stronger brain activation in musicians to single metric instances of rhythmic
incongruity. On the other hand, musicians could also be more familiar with and thus more
20
tolerant to metric challenge and metrical ‘counter-evidence’ than non-musicians. Therefore,
they would perhaps adapt and adjust faster and more efficiently their internal metric models
to continuous and repeating musical input, using less effort over a period of time. The
prediction in terms of possible pupillary effects of greater tolerance to metric challenges is
therefore a larger pupil dilation in the non-musicians.
Since tapping involves motor response in addition to listening, we expected the active
condition to generate higher effort measures than the passive condition, which should be
expressed in greater pupil dilations in the former than in the latter. Finally, in line with
earlier research, we expected that the on-the grid or very moderate microtiming versions
would be rated highest on subjective measures of groove and musical well-formedness.
Nevertheless, we expected a group difference also on these ratings, as musicians’ stronger
metric models should be more adjustable to subtle onset asynchronies in timing (Hove et al.,
2007).
3.2 Methods 3.2.1 Participants
We recruited 32 volunteer, unpaid, participants (mean age = 30.06, range = 20-44). Sixteen
of the participants were professional jazz musicians and another 16 were non-
musicians/amateur musicians. In each group, there were 9 males (musicians: mean age =
32.1, range = 20-44; non-musicians: mean age 32.2, range = 20-44) and 7 females
(musicians: mean age = 27.4, range = 24-31; non-musicians: mean age = 27.3, range = 23-
31), giving a total of 18 males and 14 females. Each professional jazz musician was matched
with a non-musician of the same age (±2 years; so that the mean age of the two groups did
not differ), sex and completed level of education (in total, two participants had high-school
degrees, 24 had a Bachelor degree and 10 had a Master’s degree). All professional musicians
had their university degree in music and characterized themselves as jazz musicians, namely:
one trombonist, two sax players, three trumpeters, three pianists, two vocalists, two guitarists,
one bass player and two drummers. Among the non-musicians, five participants played as
amateurs the guitar and one played the flute. No other demographic data were recorded. All
participants had normal or corrected-to-normal (with contact lenses) eyesight and hearing
ability within the normal range, by self-report. Participants from both groups were recruited
by personal invitation from the author or word-of-mouth. All participants were able to
complete the experiment and signed a written informed consent before participation.
21
Musical Ear Test (MET)
To verify objectively that the two groups of musicians and non-musicians differed in musical
expertise, in particular in relation to rhythm, a reliable and valid score of rhythmic
competence was applied. Specifically, all participants went through the Musical Ear Test
(MET; Wallentin, Nielsen, Friis-Olivarius, Vuust, & Vuust, 2010). MET has a melodic and a
rhythmic part (as for the present study, only the rhythmic part was used). The test consists of
52 pairs of rhythmic phrases, each phrase one bar long, consisting of 4-11 wood block beats
in tempo 100 bpm. The two phrases are presented immediately after one another, and the
participants’ task is to decide whether the pairs are identical or not; 26 identical and 26 non-
identical phrase pairs being included (see Figure 3).
Figure 3. Example from Musical Ear Test, rhythm part. From Wallentin et al., (2010).
Despite MET does not address competence of polyrhythm and microtiming
specifically, it measures a general musical/rhythmic competence through the proxy of
musical working memory. Thus, the test seems able to discriminate participants’ sensitivity
to auditory fine-scaled rhythms (Wallentin et al., 2010), which fits the goal of the present
study. MET has shown good psychometric attributes with practically no ceiling- or floor
effects. Moreover, it has been shown to successfully distinguish between groups of
professional musicians from amateur musicians and non-musicians (for further information,
see Wallentin et al., 2010).
A one-way analyses of variance (ANOVA) indicated a significant effect, F(1, 30) =
17.05, p < .001, of Musicianship (musicians vs. non-musicians) for the % correct responses
in the Musical Ear Test, rhythm part. This was due to the expected difference between the
musicians (M = 87.98; SD = 4.63) outperforming the non-musicians (M = 78.37; SD = 8.08)
on this test. For the musicians only, simple linear regressions were calculated to predict
MET-score based on their reported Hours of practice (daily on their instrument), and what
Age they began to play their instrument. Both analyses failed to reveal significant
relationships.
22
3.2.2 Stimuli Experiment 1: The Polyrhythm Experiment
Experiment 1 consisted of two 30-second excerpts from the sax solo of Sting’s tune Lazarus
Heart (1987); one excerpt included polyrhythmic texture, the other did not and served as a
control stimulus. Stimuli were borrowed from Vuust et al. (2006); however, the stimuli’s
duration was shorter in the present study than in the original. Excerpt 1, ‘the polyrhythmic
condition’, contained three 4/4 bars of main meter (M), in tempo 120 bpm, followed by three
(main-meter) bars with a ‘competing’ counter-rhythmic beat level (C), 4:3 (the counter-
rhythm tempo being 160 bpm), without the main meter being explicitly musically referred to.
This sequence was repeated in order to give the pattern of M-C-M-C-M. In Excerpt 2, ‘the
plain condition’, C was excluded, so M was repeated five times (M-M-M-M-M). The main
meter sequence M and cross-rhythmic sequence C were of almost identical loudness, tempo
and length (six seconds), making the excerpts ideal for studying the effect of a polyrhythmic
texture in a musical, ecological setting. Figure A1 in Appendix displays a schematic
overview of stimuli. For even more detailed properties, see Vuust et al. (2006).
Experiment 2: The Microtiming Experiment
As for The Microtiming Experiment we generated 25 new groove excerpts lasting 30 seconds
each (see Figures A2-A6 in Appendix). Excerpts were recorded acoustically by professional
jazz musicians (JJH and the author JFS) on a standard drum kit and double (upright) bass
respectively, with MTL as a studio engineer. Drums and bass constitute the typical ‘rhythm
section’ in many contemporary musical genres, hence, most people should be used to pay
‘rhythmic attention’ to these instruments. It is also a constellation where microtiming
asynchronies may occur in groove contexts (Keil, 1987). Bass and kick drum are both located
low in the typical musical pitch range and time perception has indeed been found to be
generally superior when processing low compared to high musical pitch, as evidenced by
behavioural and brain activity measures (Hove, Marie, Bruce, & Trainor, 2014). Further, a
small pitch difference between instruments increases the chance that timing asynchrony is
discernible for the listener (Rasch, 1988). Finally, by only presenting bass and drum, both
playing a key role in timing perception, we simultaneously removed possible confounders.
The recorded groove excerpts were categorised into three levels (Low, Medium,
High) of what we called structural Complexity. Besides features of polyrhythm and
microtiming, syncopation is another common measure of structural complexity in a groove
23
(Witek et al., 2014). Therefore, Complexity in this context refers to syncopation density as
well as the number of note onsets per bar (these two variables were confounded in the present
stimuli). The ‘low’ complexity excerpt (Figure A2 & A3) featured two notes per bar in the
bass, and no syncopations. ‘Medium’ (Figures A4 & A5) featured three bass notes per bar, in
which two of these were syncopated, and no drum syncopations. To be able to directly
examine the processing effect of a steady timekeeper, ‘low’ and ‘medium’ featured versions
with (Figures A3 & A5) and without (Figures A2 & A4) eighth notes hi-hat (‘HH’).
Generally, there is no obvious connection between an increase in event density caused by
steady timekeeping and structural complexity. Rather, as long the acoustical articulations of
the time-keeping instrument support rather than challenge the listener’s metric reference,
timekeeping will likely simplify the processing of the rhythmic material. ‘High HH’ (Figure
A6) was recorded in a single version only which included both hi-hat and snare drum. It
featured a more intricate drum kit pattern with two sixteenth note syncopations per bar in the
kick drum. The bass line was melodic and non-syncopated, ‘walking bass like’, with eight
eighth notes per bar. The structure was eight bars long, in contrast to the low and medium
complexity-grooves that had a repeated two-bar structure. Another difference between ‘high
HH’ and the other two complexity levels, was the fact that the former was inspired by a real
musical example, namely the R&B/soul-tune Really Love by the artist D’Angelo (2014),
where microtiming are in fact a part of the groove matrix between bass and drums. An
analysis performed by AD (with Amadeus Pro, version 2.3.1; see figure A7 in Appendix)
scrutinising the microtiming asynchrony between bass and drums in the original track of
‘Really Love’, showed that the timing of the electric bass is approximately timed 40-60 ms
behind the kick drum timing, at least in parts of the tune. This also enabled us to compare the
effects from an ‘ecological’ groove, with two ‘artificially made’ grooves (although all
excerpts were acoustically recorded by the author and his colleague on bass and drums,
respectively).
Table 1 Overview of stimuli in Experiment 2: The Microtiming Experiment
Note: *There was no ‘high’ condition, since the high complexity groove was presented with hi-hat only. Italicised cells contain names of conditions as used in text. All five groove-excerpts were presented in five microtiming conditions (relative placement of bass compared to drums): -80 ms, -40 m, 0 ms, +40 ms, +80ms.
Timekeeping:supportingmetricreference(eighthnotehi-hat)No Yes
Complexity: Low low(FigureA2) lowHH(FigureA3)Medium medium(FigureA4) mediumHH(FigureA5)High * highHH(FigureA6)
numberofsyncopations/noteeventsperbar
24
All excerpts were 16 bars (in 4/4 bar) long, using a metronome click track set to 80
bpm as a common rhythmic grid. Eighty bpm is an adequate tempo for groove based music
(especially in the R&B /soul-genre; Danielsen et al., 2015), and also the actual tempo of
Really Love that served as the model for our high complexity groove. The notes played by the
bass player and drummer were not of identical duration and loudness throughout the
recordings, a result of intentionally trying to keep the stimuli as musical and natural as
possible. Bass and drum tracks were recorded separately and put together in Pro-Tools (with
help from technician MTL). Drums were recorded using one microphone on each drum (kick,
hi-hat, snare drum). Three microphones were also used for the double bass recording. Phase
alignment of the three bass tracks was performed, according to the timing from the contact
microphone – the ‘fastest’ of the three incoming signals. This signal happens artificially
early, compared to the normal acoustic properties of the double bass. To compensate for this,
the aligned bass tracks were moved 10 ms later/’to the right’. This was sufficient to give a
sense of bass and drums being together – ‘on the grid’. We used the ‘on the grid’ version as a
reference, and made four additional microtiming levels in terms of relative temporal timing
of the bass compared to the drum/metronome timing: bass 40 ms and 80 ms ahead (-) of, and
bass 40 ms and 80 ms after (+) the drums/metronome reference. In other words, there was a
stepof 40 ms between each level. All bass notes in each trial had the same relative temporal
position compared to the drum track, and the drum track itself had no internal microtiming
asynchronies. Each trial began with three metronome ticks, timed together with the drum set
recording. The last/fourth tick was removed to obscure which of the instruments that deviated
from the initial metronome reference.
In conclusion, Experiment 2 consisted of three grooves of different structural
complexity (varying degree of syncopations and note onsets per bar), in which the low- and
medium complexity groove were presented both with (‘HH’) and without a meter-supporting
time-keeping hi-hat. Each groove (‘low’, ‘low HH’, ‘medium’, ‘medium HH’, ‘high HH’)
were presented in five microtiming levels (including the on-the-grid version), making 25 the
total number of excerpts. See Table 1.
3.2.3 Setup and procedure Testing took place at the Cognitive Laboratory in the Department of Psychology, University
of Oslo, in a windowless and quiet room with constant illumination of about 170 Lux and
constant temperature and humidity. Remote Eye Tracking Device (RED) and I-View
25
software commercially available from SensoMotoric Instruments (SMI), Berlin (Germany)
were used to record oculomotor data (pupil diameters, eye movements, fixations and blinks).
The RED system records quantitatively binocular pupil diameter with high precision
(detecting changes as a small as .0004 mm, according to SMI specs) using infrared light
lamps and an infrared light sensitive video camera. The sampling frequency was set low to 60
Hz. Instructions in English were given on a flat DELL LCD monitor with a screen resolution
of 1680 X 1050, installed above the RED video camera. The distance from the participant’s
eyes to the monitor and eye tracking device was set to 60 cm by use of a chin/head stabilizer.
Participants gave tapping responses during the active condition by pressing a key on the PC’s
keyboard (Dell L3OU) placed in front of the participant. The keyboard was linked to the PC
via a STLAB USB port hub. All participants wore a set of stereo headphones (Philips SBC
HP840) during the music listening.
All participants were individually tested by the author/experimenter who was present
in the room at all times with each participant. Most of the testing was done in the afternoon in
working days, occasionally in evenings. Both experiments were performed in each session.
Near-sighted participants were asked not to use their glasses, and female participants to
refrain from using mascara, since these are often source of artefacts during eye tracking. All
participants were requested to keep their eyes open (in order to obtain continuous pupil
recordings), except for short eye blinks, and to maintain gaze on a black fixation cross (+)
displayed in the middle of the monitor. Furthermore, they were informed that it was possible
to rest their eyes when the fixation cross was not present. The fixation cross was used to
facilitate anchoring gaze and avoid that participants would look away from the screen;
however, it was not necessary for collecting pupil sizes. A neutral grey colour (pixels’ RGB:
163,163,163 for the Experiments, and RGB: 166,166,166 for MET) was used as background.
All experiment sessions began with a standard 5-point eye calibration procedure, a general
instruction slide, and individual adjusting of the sound volume. Each trial was self-paced, i.e.,
initiated by the participant by pressing the spacebar. Three seconds before and during the
music clips, the fixation cross was displayed in the middle of the screen. Each participant was
first exposed to the 25 microtiming excerpts in a ‘listening only’ condition. The order of
stimuli was pseudorandomized, with the constraint that it was made sure that none same
groove types (e.g., ‘low HH’) were played back-to-back. The pseudorandomized order of
stimuli was counterbalanced so that half of the participants in each group (musicians/non-
musicians) began with one of the two blocks. After completing the listening of each excerpt,
the participants were asked two questions (on a 5-step Likert scale: 1 = not at all, 5 = to a
26
great extent): 1) To what extent does listening to this rhythm, make you want to move your
body? and 2) To what extent do you find that this rhythm sounds musically well-formed?
Question 1 tapped onto the concept of groove experience, treating groove exclusively as a
sensorimotor phenomenon, according to Madison’s (2006) definition of groove. We did not
enquire about ‘positive affect’, since microtiming stimuli were brief bass and drums-tracks,
therefore not ‘ecological’ musical clips; hence we did expect them to have relatively low
aesthetic value per se. Question 2 enquired about rating of musical ‘well-formedness’, similar
to Davies’ et al., (2013) ‘musical naturalness’. After listening to all microtiming stimuli, the
participants were exposed to Experiment 1, i.e., the polyrhythmic stimuli and the
corresponding control stimuli (counterbalanced for half of the participants). After a break,
there was an introductory test-trial where participants were asked to tap with their index
finger, synchronising to a 30 second isochronous metronome beat. After this, participants got
the following instruction:
The clips you will hear from now on will begin with the metronome pulse, then the
music will start. Tap with the metronome and, as steady and regularly as you can,
continue tapping the basic pulse of the music, until the music stops.
If participants didn’t perform this task as instructed, the experimenter alerted verbally the
participant, repeating the instructions (e.g., ‘only tap the basic pulse given by the metronome,
not any rhythmic pattern’). Following this, the polyrhythmic and microtiming stimuli were
presented again in an active tapping condition; however, in reverse order for all participants.
After completing the tapping condition, participants were given the Musical Ear Test
(‘rhythm part’ only). The test began with instructions and two practice trials. Every new trial
was initiated by pressing the spacebar. All participants got the MET stimuli in the same order
and no feedback was given during testing. Eye-tracking data was also recorded while
participants did MET, but these data were not analysed. Table A1 in the Appendix shows an
overview of the complete experimental procedure. Immediately following the experiment
session (taking approximately 70 min to complete), the participant filled out an online
questionnaire. The questionnaire was based on the original form from the study of Laeng et
al. (2016), but adapted to the present study.
27
3.2.4 Data processing Pupil, tapping and subjective rating data were exported from BeGaze software (SMI, v.3.4)
and imported to MicrosoftÒ Excel where they were sorted and categorized. As pupil sizes of
left and right eye were identical, participants having maintained fixation on a central cross,
no mean value of the two eyes was necessary. Subtractive baseline correction in different
variations are commonly applied in pupillometry research (e.g., Bianchi, Santurette, Wendt,
& Dau, 2016; Laeng & Sulutvedt, 2014). In the present study, average individual left eye’s
pupil size by each trial (lasting 30,000 ms) were baseline-corrected by the average pupil size
taken 3000 ms before stimulus onset for each participant per trial, giving a measure of ‘pupil
size change’ (corrected for luminance and expressing dilations from baseline as positive
numbers and relative constrictions as negative numbers).
As for the tapping data, the timing of each tap was compared to a corresponding
reference point of the musical pulse, the latter operationalized as X milliseconds after
stimulus onset. In Experiment 1, the inter-pulse-interval for musical reference points was 500
ms (120 bpm), while for Experiment 2 it was 750 ms (80 bpm). The timing of the
drums/metronome (not the double bass) served as the basis for the reference points in the
microtiming stimuli. The first eight taps in each trial in both experiments were excluded from
the analyses. To identify and exclude outliers, pre-processing of tapping data was done by
custom scripting in MATLAB (performed by KN), according to the following rules:
• Exclude (and count) double taps, defined as more than one tap within a single time
period, corresponding to the time period of ±50% between one reference point and the
next/previous reference point (±250 ms around each reference point when inter-pulse-
interval was 500 ms, and ±375ms when inter-pulse-interval was 750 ms).
• Count missing reference points, defined as reference points without any taps within the
time period described above.
• Exclude (and count) complete trials that have more than six reference points containing
double or no taps.
The typical dependent variables in tapping studies are either ‘absolute’ offsets
between reference points and the tap and their standard deviation; additionally, one can
compute the mean and standard deviation of intertap intervals (ITI; Repp & Su, 2013). From
preliminary tapping analyses, we discovered the presence of an artifactual ‘time lag’ or
‘instability’ in the software system (possibly generated from the use of a standard keyboard
and USB port in collecting taps; as confirmed by SMI’s personal communication), that made
28
the absolute offsets non-reliable and possibly non-coherent between participants. For
example, when tapping to the ‘plain condition’ of The Polyrhythm Experiment, professional
musicians average mean offset from reference points were +109 ms, with an inter-participant
variance (SD) of 33 ms. Both measures far exceeded what we could expect from tapping
behaviour in the musician group according to previous studies (e.g., Repp & Su, 2013). This
artefact prevented us from analysing the absolute offsets, which could have been useful in
exploring how microtiming influenced absolute placements of taps. Nevertheless, the intra-
participant tapping offset distributions appeared reliable and within the normal range (the
mean SD offset when musicians tapped to ‘the plain condition’ of The Polyrhythm
Experiment was 18 ms). We therefore selected ‘SD offset’ data as the main dependent
variable for tapping in our analyses, used as a reversed measure of ‘tapping accuracy’. There
was still, however, a small element of uncertainty relating to exclusions of occasional taps: as
discussed above, taps were included when being ±50% around each reference points (±250
ms in Experiment 1 and ±375 ms in Experiment 2). Since taps generally seemed to be placed
±100 ms too late due to technical issues, this meant that ±150ms/±275ms-taps in Experiment
1 and 2, respectively, were considered ‘outliers’ and excluded from the statistical analyses.
We decided to ignore the intertap interval (ITI), since missing and excluded double taps
tended to generate double ITIs (for example 1000 ms instead of 500 ms), as corresponding
reference points did not have any attached taps.
Missing pupil data were generally very few (1.3% or 23 out of totally 1728 trials
across both experiments); thus, there was no need to exclude any participant from the pupil
analyses due to data loss. Tables A2-A5 in Appendix summarise tapping data exclusions,
sorted into three categories for musicians and non-musicians, respectively: 1) number of
excluded trials (having more than six reference points containing double or missing taps); 2)
number of reference points without taps in included trials; 3) number of double taps in
included trials. Across the two experiments, 8.7% or 75 out of totally 864 tapping trials were
excluded, however, all participants’ valid trials were included in our analyses. Group
differences in tapping exclusions were visually scrutinised, but not statistically analysed. As
for the repeated-measures ANOVAs, missing trials in both pupil and tapping data were
estimated using that participant’s group (musician/non-musician) mean pupil change/SD
offset in the respective condition so as to fill out missing cells in the statistical spreadsheet.
Statistical analyses were performed with Statview and/or SPSS (v.24) software.
29
4 Results 4.1 Music Engagement Questionnaire
From the questionnaire (with 5-step Likert scales), we found from independent samples t-
tests that musicians enjoyed more listening to music in general (M = 5; SD = 0) than non-
musicians (M = 4.63; SD = .62); t(30) = 2.42, p < .001. Musicians and non-musicians did not,
however, differ significantly in how interesting they found participating in the present study,
or how interesting they found the study’s musical excerpts.
4.2 A general pupillary drifting effect Figure 4 shows participants’ mean pupil diameter size (in mm) as a function of time (0-30
seconds) for all trials in both experiments combined. By visual inspection we can observe a
response peak 5-7 seconds after stimulus onset, and later a negative ‘drifting’ effect with a
more or less continuous decay – the pupil generally tended to decrease in size as the trials
progressed. Hence, it was reasonable to expect the mean baseline-corrected pupil sizes in
both experiments to be either close to or below the zero level, as the baseline-condition was
only 3 seconds in duration and measured right before each stimulus, whereas the
experimental condition took 30 seconds.
Figure 4. Mean pupil size as function of time (s) for all experimental trials combined. Error bars: ±1 Standard error(s).
30
4.3 Experiment 1: The Polyrhythm Experiment 4.3.1 Pupil responses
A 2*2*2 mixed between-within repeated-measures ANOVA of mean baseline-corrected
pupil change dependent on Musicianship (musicians vs. non-musicians), Rhythm (‘the
polyrhythmic condition’ vs. ‘the plain condition’) and Activity (‘passive’ [listen only] vs.
‘active’ [tapping with the beat]), was performed. ‘The polyrhythmic condition’ yielded larger
pupil dilation change (M = .06 mm; SD = .29) than ‘the plain condition’ (M = -.03 mm; SD =
.26), approaching a significant difference, F(1, 30) = 4.0, p = .054, in the predicted direction.
In addition, we found a significant, F(1, 30) = 35.89, p < .001, difference between the active
(M = .12 mm; SD = .27) and passive (M = -.09 mm; SD = .24) conditions, indicating that
tapping the beat while listening, triggered a larger pupil dilation change than listening only.
However, there was no significant pupil change difference between musicians and non-
musicians. No two- or three-way interactions were found.
4.3.2 Tapping accuracy Musicians had fewer excluded participants,
double taps and missing reference points in
both the ‘polyrhythmic’ and ‘plain’
conditions (Table A2 in Appendix). When
only included taps and trials were taken into
account, a mixed between-within repeated-
measures ANOVA of tapping accuracy (SD
offset) was performed. Independent variables
were the same as in the pupil response
analysis (except Activity, since the active
condition was the only tapping condition). As
expected, tapping accuracy was significantly
lower (i.e., higher SD offset) when tapping
the main meter in ‘the polyrhythmic
condition’ (M = 64.09 ms; SD = 40.22),
compared to tapping the main meter in ‘the
plain condition’ (M = 27.28 ms; SD = 16.12), F(1, 30) = 37.45, p < .001. Also, a significant
effect of Musicianship, F(1, 30) = 28.86, p < .001, appeared. Specifically, the musicians’
Figure 5. Tapping accuracy (SD offset in ms; low values represent higher accuracy) for musicians and non-musicians when tapping the main meter in the ‘plain’ and ‘polyrhythmic’ conditions. Error bars: ±1 Standard error(s).
31
tapping accuracy (M = 28.68 ms; SD = 24.77) exceeded the non-musicians’ (M = 62.69 ms;
SD = 36.96). In addition to the main effects, the expected interaction effect of Rhythm with
Musicianship was significant, F(1, 30) = 5.32, p= .028, as shown in Figure 5, where the
relative difference in tapping performance between musicians and non-musicians were
stronger with cross-rhythms, compared to the plain condition.
4.4 Experiment 2: The Microtiming Experiment 4.4.1 Pupil responses
A 2*5*5*2 mixed between-within
repeated-measures ANOVA of mean
baseline-corrected pupil change
dependent on Musicianship (musicians
vs. non-musicians), Microtiming
asynchrony (-80 ms, -40 ms, 0, +40 ms,
+80 ms), Complexity (‘low’, ‘low HH’,
‘medium’, ‘medium HH’, ‘high HH’)
and Activity (‘passive’ vs. ‘active
conditions’, was performed.
The effect of Musicianship.
As in Experiment 1, there was no
significant pupillary response difference
between musicians and non-musicians,
which was also the case when we
performed a separate analysis where the microtiming asynchronies from zero were collapsed
over direction into three levels (0 ms, ±40 ms and ±80 ms). The analysis did not show any
interaction effects of musicianship by any experimental condition.
The effect of Activity.
Pupil dilation responses were, as in Experiment 1, significantly stronger when
participants tapped to the beat (M = -.02 mm; SD = .26), compared to when they listened only
(M = -.19 mm; SD = .26), F(1, 30) = 82.3, p < .001.
The effects of Microtiming asynchrony.
A significant main effect of Microtiming asynchrony magnitudes on pupils change
emerged, F(4, 120) = 6.08, p < .001, as shown in Figure 6. There was an increased pupil
Figure 6. Pupil change as a function of Microtiming asynchrony magnitudes between bass and drums for both groups. Error bars: ±1 standard errors.
32
response as microtiming asynchrony increased in either direction (i.e. bass timed before
drums [-] or drums timed before bass [+]). Pairwise contrasts (see Table 2) revealed that the 0
ms condition yielded significantly less pupil dilation change than the -80 ms, +80 ms and +40
ms microtiming asynchrony conditions. The 0 ms and -40 ms conditions, did not generate
significantly different pupil responses. The response difference between the -80 ms and -40
ms conditions was statistically significant, while this was not the case between the +40 ms
and +80 ms conditions. The latter indicated that in the present experiment, changing the
microtiming asynchrony from 40 ms to 80 ms demanded more effort in the perceptual system
when bass was timed before drums, than when drums were timed before bass. No other
significant differences between the five Microtiming conditions were found.
Table 2 Pairwise contrasts for main effects of Microtiming and Complexity on pupil change
Note: a negative contrast value indicates that the mean for the condition in the leftmost column is lower than for the condition to the right. Statistically significant (p < .01) contrasts in boldface.
The mixed ANOVA also revealed a statistically significant Activity by Microtiming
asynchrony interaction, F(4, 120) = 9.78, p < .001. This could be explained by no effect of
Activity for the extreme -80 ms condition, which seemed to create approximately the same
mental effort independent of activity.
The effects of Complexity.
No main effect of Complexity (i.e., different levels of syncopations/note onsets per
bar) on pupil change was found. Unsurprisingly, a separate 2*3*5*2 repeated-measures
ANOVA that compared pupil change from the three complexity levels only, that included hi-
hat (‘Low HH’, ‘Medium HH’, ‘High HH’), also failed to reveal an effect. We did, however,
find a three-way interaction between Microtiming, Complexity, and Passive/Active, F(16,
480) = 1.71, p = .042, that was difficult to interpret and most likely reflecting spurious effects
resulting from the small number of trials constituting each condition. All other main and
interaction effects were weak and statistically non-significant, including the effect of
PairwisecontrastsformaineffectsofMicrotimingandComplexityMicrotiming:
Contrast SE p Contrast SE p-80 -40 0.045 0.016 0.010 low lowHH -0.005 0.018 0.768-80 0 0.070 0.019 0.001 low medium -0.031 0.016 0.062-80 +40 0.025 0.016 0.127 low mediumHH -0.005 0.017 0.754-80 +80 0.014 0.015 0.371 low highHH -0.020 0.020 0.336-40 0 0.026 0.016 0.132 lowHH medium -0.025 0.015 0.100-40 +40 -0.019 0.013 0.131 lowHH mediumHH 0.000 0.018 0.999-40 +80 -0.031 0.015 0.057 lowHH highHH -0.014 0.015 0.3400 +40 -0.045 0.015 0.006 medium mediumHH 0.025 0.011 0.0330 +80 -0.056 0.016 0.002 medium highHH 0.011 0.015 0.470+40 +80 -0.011 0.013 0.407 mediumHH highHH -0.014 0.016 0.382
Complexity:
33
‘Timekeeping’ (comparing the ‘low’ and ‘medium’ complexity grooves with and without hi-
hat, respectively) on pupil response, investigated in a separate ANOVA.
4.4.2 Tapping accuracy Across all conditions of Experiment 2, musicians had fewer double taps but more missing
reference points in included trials than the non-musicians (Tables A3-A5 in Appendix). Yet,
it is probable that non-musicians had more missing reference points, on the basis of having
far more excluded trials (63) than the musicians (5). Note that excluded trials cover a
substantial amount of missing reference points. Taking into account only included trials and
taps, we performed a 2*5*5 mixed between-within ANOVA of SD offset, with the same
independent variables as in the pupil data analysis above (except Activity, since the active
condition was the only tapping condition).
The effect of Musicianship.
The analysis revealed a main effect of Musicianship, F(4, 120) = 45.97, p < .001.
Musicians (M = 23.11 ms; SD = 11.50) outperformed non-musicians (M = 80.08 ms; SD =
54.64) in tapping accuracy across all levels of microtiming asynchrony and complexity, as
can be seen in both graphs of Figure 7.
Figure 7. Tapping accuracy (SD offset in ms), for musicians and non-musicians as a function of Microtiming asynchrony (right) and structural Complexity (left). Error bars: ±1 standard errors. The effects of Microtiming asynchrony.
The main effect of Microtiming asynchrony was statistically significant, F(4, 120) =
10.49, p < .001. However, the analysis also revealed a statistically significant Microtiming
asynchrony by Musicianship interaction, F(4, 120) = 5.77, p < .001. The left side graph of
Figure 7 shows that birectional microtiming asynchrony decreased tapping accuracy;
especially the -80 ms condition seemed to have a negative effect. This was confirmed by
0
20
40
60
80
100
Cel
l Mea
n fo
r SD
Offs
et A
ll tap
s
Low Low HH Medium Medium HH High HH
Non-musiciansMusicians
0
20
40
60
80
100
120
Cel
l Mea
n fo
r SD
Offs
et A
ll tap
s
pre80 pre40 post00 post40 post80
Non-musiciansMusicians
-80ms-40ms0ms+40ms+80ms ‘low’‘lowHH’’medium’‘mediumHH’‘highHH’
0
20
40
60
80
100
120
Cel
l Mea
n fo
r SD
Offs
et A
ll tap
s
pre80 pre40 post00 post40 post80
Non-musiciansMusicians
0
20
40
60
80
100
120
Cel
l Mea
n fo
r SD
Offs
et A
ll tap
s
pre80 pre40 post00 post40 post80
Non-musiciansMusicians
34
Table 3 showing the results for pairwise contrasts for the main effect of Microtiming
asynchrony, for non-musicians and musicians separately. For the musicians, in fact, it was
only the -80 ms condition that significantly increased SD offset compared to the other
conditions, but for the non-musicians, also the +80 ms conditon had a significant negative
sensorimotor synchronisation effect (compared to the -40 ms condition). The -40 ms and +40
ms conditions did generally not decrease tapping accuracy compared to the on-the-grid-
excerpts.
Table 3 Pairwise contrast for the effect of Microtiming asynchrony on tapping accuracy (SD offset) – overall and separately for non-musicians and musicians.
Note: a negative contrast value indicates that the mean for the condition in the leftmost column is lower than for the condition to the right. Statistically significant (p < .01) contrasts in boldface.
The effects of Complexity.
The mixed ANOVA revealed a main effect of Complexity, F(4, 120) = 5.76, p < .001.
From the right side graph of Figure 7 it is evident that this main effect was primarily due to a
positive influence of Timekeeping (hi-hat eighth notes vs. no hi-hat eighth notes). That is,
participants’ tapping accuracy was significantly higher (lower SD Offset) in both the low and
medium-grooves with hi-hat, compared to the low and medium-grooves without hi-hat. The
latter is displayed in Table 4, showing the pairwise contrasts for the main effect of
Complexity, and for non-musicians and musicians separately.
To control for (exclude) the effect of Timekeeping, we comparing tapping accuracy in
the two groups on the grooves with hi-hat only (‘low HH’, ‘medium HH’ and ‘high HH’). A
2*3 mixed between-within ANOVA with Musicianship and this simplified Complexity
variable as independent variables, gave a significant interaction effect of Complexity with
Musicianship, F(2, 60) = 3.31, p = .043, in addition to the expected main effects. Although
Figure 7 displays all five complexity conditions, we can see from the graph that both groups’
tapping accuracy decreased as Complexity (number of syncopations/sound events per bar)
increased, but to a greater degree in the non-musicians; hence the interaction effect is mainly
Microtiming: Non-musicians. Musicians.Contrast SE p Contrast SE p Contrast SE p
-80 -40 19.537 3.763 0.000 34.266 7.450 0.000 4.809 1.074 0.000-80 0 15.472 4.503 0.002 24.532 8.869 0.014 6.413 1.569 0.001-80 +40 15.696 4.366 0.001 25.521 8.670 0.010 5.871 1.044 0.000-80 +80 7.831 3.309 0.025 11.137 6.535 0.109 4.524 1.050 0.001-40 0 -4.065 2.601 0.129 -9.734 5.047 0.073 1.604 1.264 0.224-40 +40 -3.841 3.199 0.239 -8.745 6.314 0.186 1.063 1.031 0.319-40 +80 -11.706 2.797 0.000 -23.128 5.442 0.001 -0.285 1.295 0.8290 +40 0.224 2.895 0.939 0.989 5.683 0.864 -0.541 1.106 0.6320 +80 -7.642 2.922 0.014 -13.395 5.764 0.035 -1.889 0.963 0.069+40 +80 -7.865 3.140 0.018 -14.383 6.220 0.035 -1.347 0.871 0.143
35
driven by them. However, the variance of the non-musicians’ tapping data was much larger
than the variance of the musicians’ tapping data, which might be a reason for why no
significant tapping accuracy differences occurred between any of the three complexity levels
(‘low HH’, ‘medium HH’ and ‘high HH’) when alpha was set low to .01 to control for
multiple comparisons.
Table 4 Pairwise contrast for the effect of Complexity on tapping accuracy (SD offset) – overall and separately for non-musicians and musicians.
Note: a negative contrast value indicates that the mean for the condition in the leftmost column is lower than for the condition to the right. Statistically significant (p < .01) contrasts in boldface.
To summarise the main findings from tapping data in Experiment 2: musicians kept
time more accurately than non-musicians; microtiming asynchrony (though only the two
extreme conditions, ±80 ms) negatively influenced tapping accuracy in both groups, but non-
musicians to a substantially greater degree than musicians. Tapping accuracy generally
tended to be higher in the low complexity than the high complexity grooves. Finally,
timekeeping (i.e., presence of meter-supporting hi-hat eighth notes) increased tapping
accuracy in both groups.
4.4.3 Rating scales The two rating scales were based on the following questions: 1) To what extent does listening
to this rhythm, make you want to move your body? (‘move body’) and 2) To what extent do
you find that this rhythm sounds musically well-formed? (‘well-formed’) Participants’
responses on the two scales were moderately to highly correlated (non-musicians: r = .56, p =
.000; musicians: r = .80, p < .001). We performed a 2*5*5*2 mixed between-within ANOVA
of subjective ratings, dependent on Musicianship (musicians vs. non-musicians), Microtiming
asynchrony (-80 ms, -40 ms, 0, +40 ms, +80 ms), Complexity (‘low’, ‘low HH’, ‘medium’,
‘medium HH’, ‘high HH’) and Rating Scales (‘move body’ vs ‘well-formed’).
Complexity: Non-musicians. Musicians.Contrast SE p Contrast SE p Contrast SE p
low lowHH 13.704 3.474 0.000 15.307 6.729 0.038 12.101 1.730 0.000low medium -0.087 3.679 0.981 -1.054 7.092 0.884 0.880 1.962 0.660low mediumHH 13.232 3.437 0.001 17.063 6.728 0.023 9.402 1.414 0.000low highHH 1.645 5.072 0.748 -5.888 10.085 0.568 9.178 1.103 0.000lowHH medium -13.791 3.295 0.000 -16.361 6.339 0.021 -11.221 1.800 0.000lowHH mediumHH -0.471 2.539 0.854 1.756 4.894 0.725 -2.699 1.352 0.064lowHH highHH -12.059 5.304 0.030 -21.195 10.560 0.063 -2.923 1.020 0.012medium mediumHH 13.319 2.768 0.000 18.117 5.410 0.004 8.522 1.174 0.000medium highHH 1.732 5.396 0.750 -4.834 10.638 0.656 8.298 1.815 0.000mediumHH highHH -11.587 5.580 0.047 -22.951 11.098 0.056 -0.224 1.178 0.852
36
The effect of Musicianship.
We found a significant interaction effect of Microtiming by Musicianship, F(4, 120) =
10.25, p < .001, indicating that musicians were more responsive to microtiming than non-
musicians, using a wider range of the rating scale. As shown in Figure 9, musicians rated the
conditions with largest microtiming (±80 ms) lower, and the on-the-grid condition higher
than the non-musicians.
The effects of Microtiming asynchrony.
The main effect of Microtiming, F(4, 120) = 30.06, p < .001, provides evidence that
bidirectional magnitudes of microtiming asynchrony were negatively related to subjective
rating on both rating scales. In other words, when ratings were higher, the closer aligned the
bass and drums were timed (see Table 5). Furthermore, conditions where bass was timed
after the drums, were preferred in favour of (i.e., rated higher than) the conditions where bass
was timed ahead of the drums. As already mentioned, Figure 9 clearly shows that the
musicians are to a greater degree responsible for this trend. Also, a significant interaction
effect of Rating scales by Microtiming, F(4, 120) = 5.61, p < .001, emerged. From Figure 10,
we observe that participants rated excerpts higher on ‘well-formed’ than ‘move body’, and
that this difference tended to be larger with less bidirectional microtiming asynchrony.
Figure 9. Interaction effect of Musicianship by Microtiming asynchrony on subjective ratings (mean of both scales). Error Bars: ±1 Standard Error(s).
Figure 10. Interaction effect of Rating scales by Microtiming asynchrony, on subjective ratings. Error Bars: ±1 Standard Error(s).
37
Table 5 Pairwise contrasts for main effects of Microtiming asynchrony and Complexity on subjective ratings.
Note: a negative contrast value indicates that the mean for the condition in the leftmost column is lower than for the condition to the right. Statistically significant (p < .01) contrasts in boldface.
The effects of Complexity
As expected, subjective ratings increased as Complexity also increased, F(1, 30) =
27.04, p < .001. Pairwise contrasts for all conditions are presented in Table 5.
In addition, we found a positive ‘Timekeeper effect’ on subjective ratings. Low and
medium complexity grooves with hi-hat were rated higher on both scales than the
corresponding grooves without hi-hat. On ‘Wanting to move’ (with HH: M = 2.53, without
HH: M = 2.1), t(31) = 5.32, p < .001. On ‘Musically well-formed’: (with HH: M = 3.04,
without HH: M = 2.71), t(31) = 4.70, p < .001. This effect is also evident from the pairwise
contrasts in Table 5, where the two
rating scales are combined.
Finally, from the initial
ANOVA-analysis we found a
Microtiming asynchrony by
Complexity interaction effect on
subjective ratings, F(16, 480) =
3.78, p < .001. We can appreciate
from Figure 11 that microtiming
asynchronies seemed to give larger
fluctuations in ratings (steeper
profiles on the graph) as groove
complexity increased.
Microtiming: Complexity:Contrast SE p Contrast SE p
-80 -40 -0.616 0.105 0.000 Low LowHH -0.272 0.075 0.001-80 0 -0.931 0.130 0.000 Low Medium -0.566 0.070 0.000-80 40 -0.828 0.108 0.000 Low MediumHH -0.616 0.095 0.000-80 80 -0.341 0.101 0.002 Low HighHH -1.122 0.151 0.000-40 0 -0.316 0.077 0.000 LowHH Medium -0.294 0.080 0.001-40 40 -0.212 0.080 0.013 LowHH MediumHH -0.344 0.088 0.001-40 80 0.275 0.094 0.006 LowHH HighHH -0.850 0.153 0.0000 40 0.103 0.059 0.093 Medium MediumHH -0.050 0.076 0.5160 80 0.591 0.115 0.000 Medium HighHH -0.556 0.145 0.00140 80 0.487 0.088 0.000 MediumHH HighHH -0.506 0.154 0.003
Figure 11. Interaction effect of groove Complexity and Microtiming asynchrony on subjective ratings (based on two rating scales, ranging 1-5). Error Bars: ±1 Standard Error(s).
38
5 General Discussion 5.1 General effects on pupil responses and tapping
accuracy support DAT and PC The results from both experiments on pupil and tapping response strongly support the notion
that the brain allocates increased effort to process musical stimuli whenever rhythmic input
challenges fundamental metrical models. We have little foundation, however, to conclude
whether either PC or DAT better explain the data, also because it remains unclear as to
whether the two accounts can really generate diverging predictions (at least in the present
context). Looking at pupil responses first, Experiment 1 shows that both listening to, and
tapping the main meter (M) in a rhythm containing a competing 4:3 pulse train (C), yielded
higher measures of effort for both groups, as indexed by pupillary change, compared to
listening to and tapping the main meter in a comparable non-polyrhythmic context. The
above findings fit the hypotheses given by DAT and PC, since according to these accounts,
an acoustically-salient counter-rhythmic (C) pulse level will challenge the participants’
maintenance of the neural oscillations (DAT)/predictive models (PC), which represent the
original but acoustically-absent main meter level (M). Like our results suggest, this demand
involves mental processing power. Similarly, in Experiment 2, when microtiming
asynchronies between bass and drums in the groove excerpts emerged and increased, a
corresponding increase in cognitive workload reflected in the pupil was accordingly
observed. Based on the theories, increased attentional processing demands would be a result
of participants experiencing prediction error, and adjusting the neural oscillations phase
and/or widening the temporal attentional focus (DAT)/updating the anticipated mental model
(PC), so as to account for the incoming rhythmic events across the 30-second music duration.
The larger the asynchronies between the timing of the bass and drums, the more the need for
interval adjustments; interestingly, we did measure a higher degree of effort concomitantly to
level of asynchrony. One should note that the pupil changes may also be indicative of
response conflicts (Kamp & Donchin, 2015). The inherent temporal ambiguity of exact beat
placement that emerged, especially in the most ‘extreme’ microtiming asynchrony
conditions, may have generated response conflicts as to mentally or physically lay down the
tap. Indeed, the ±80 ms conditions may also have caused metric uncertainty, since it
approached the perceptual threshold for temporal integration; that is, when microtiming
39
asynchrony exceeds 100 ms, sound onsets – even when in a similar frequency range – tend to
be experienced as separate, rather than integrated events (Repp, 2005; Warren, 1993).
General tapping results did partly mirror pupil data and appeared to be consistent with
expectations. We found that both polyrhythm and bidirectional (bass ahead of drums vs.
drums ahead of bass) microtiming asynchrony influenced tapping accuracy negatively for
both groups. However, only when exposed to the ±80 ms microtiming asynchrony conditions,
did the accuracy significantly decrease from the on-the-grid level; the ±40 ms conditions did
not disturb participants’ fine grained experience of beat placement to the degree that it was
reflected in tapping accuracy. This divergence might be explained by the 80 ms, but not 40
ms asynchrony, approaching the threshold for temporal integration, as mentioned above.
Furthermore, we generally found a higher effort in the active ‘tap with the beat’
condition, compared to the passive ‘listen only’ condition. This is not surprising, as music
production is clearly a more complex activity than music perception (Zatorre et al., 2007).
Tapping involves an active participation in the groove that demands a continuous and
coordinated motoric ‘realization’ of the internal metric model. Additionally, making an
explicit response may enhance the intensity of attentional focusing (Moresi et al., 2008; Van
der Molen, Boomsma, Jennings, & Nieuwboer, 1989) over passively listening, which would
also be expected to yield larger pupil sizes in the former than the latter. Finally, we found an
overall negative ‘drifting’ of the pupil sizes from around 5-7 seconds, and throughout the 30
seconds of the trial length. This is not unusual with pupillometric measurements and may be
interpreted as a decrease in general arousal levels with protracted focus on a task. However,
given the specific predictions generated by our two cognitive meter perception theories, the
response peak followed by gradual and continuous decay may also be interpreted as a
correspondingly gradually decreasing anticipation error, resulting from the participants’
mental models becoming initially challenged, but then increasingly adjusted, as participants
were accustomed to the rhythms.
5.2 Effects of musical expertise Numerable studies have documented the enhanced perceptual skills of musicians for rhythm
and meter (e.g., Chen et al., 2008; Drake et al., 2000; Geiser et al., 2010; Matthews et al.,
2016; Stupacher et al., 2017). The rhythm part of Musical Ear Test did also distinguish the
groups, as musicians outperformed the non-musicians. Both groups however, performed
slightly better than earlier samples of musicians and non-musicians (Wallentin et al., 2010).
40
This could be due to indeed more rhythmic competent participants selected for the present
study or due to the test situation; that is, our participants were permitted short breaks between
each trial and initiated trials themselves by pressing the spacebar. This was in contrast to the
Wallentin et al. (2010) study, where the test ran as a whole without any stops, with only one
‘resting’ bar between each trial.
It was of great interest to us to investigate the effect of musical expertise on the
cognitive processing of polyrhythm and microtiming especially. In the following, we will
look at several explanations that can account for the lack of pupillary group differences found
across both experiments, as well as discussing the clear-cut and expected effects of
musicianship on tapping data.
5.2.1 Pupillary response was unaffected by musical expertise As previously mentioned, MMN-studies have often found stronger brain responses in
musicians than in non-musicians, for example to instances of harmonic (Brattico et al., 2008)
and metrical incongruence (Vuust et al., 2005) and even syncopations (which musicians
clearly should be more accustomed to; Vuust et al., 2009). Indeed, enhanced perceptual
sensitivity is supported in part by increased involvement of the working memory system
(musical working memory is also the target variable of Musical Ear Test). Chen et al. (2008)
found an increased activation of the dorsolateral prefrontal cortex (being associated with
working memory function) in musicians compared to non-musicians when processing
meter/rhythmic structures. Therefore, working memory recruitments imply heightened
processing demands which should also be reflected in measures of pupillary changes (Laeng
et al., 2011). Based on such reasoning and the MMN-results, it was expected that musicians
displayed increased pupillary response to rhythmic complexity. However, in other lines of
research, expertise has been negatively related to mental effort both in musical tasks (Bianchi
et al., 2016) and other domains, like face recognition memory (e.g., Wu, Laeng, &
Magnussen, 2012), indicating a more efficient processing of relevant stimuli in experts than
in novices. Thus, although we generally expected some pupillary response effects of
musicianship, the direction (decreased or increased response) was less certain. Given that, in
fact, our analyses failed to reveal any significant differences, it remains unclear what effects
expertise has on effort in our specific domain.
Two recent studies, Bouwer et al., (2014) and Damsma & van Rijn (2017), may partly
inform this discussion. Both investigations came to the conclusion that basic meter perception
41
is independent of musical expertise, since they found no effect of musical expertise on MMN
and on pupil response respectively, when omitting beats placed at metrically different spots
in ‘simple’/basic rhythm excerpts. This may have implications for the lack of group pupil
differences in our study during processing of microtiming. Given the notion that the internal
metric model strength for musicians and non-musicians is indeed similar for ‘basic’ rhythms
implying a ‘simple meter’, and the grooves of the present study are in the domain of ‘basic’
(despite including micro-metrical ambiguities), it follows that general enculturation, shared
by both groups, would render possible an ‘adequate’ processing of these stimuli. However,
this account apparently contrasts with the clear-cut difference observed between our groups
in tapping performance. Moreover, remaining unclear is the absence of pupillary effects
between groups when participants processed cross-rhythms, since such rhythms are more
‘complex’ than ‘basic’. Indeed, group differences related to polyrhythmic processing should
be expected based on earlier studies’ inverse correlations between rhythmic expertise in
professional musicians and brain activity in the area of BA47 (related to the processing of
metric structures) when listening to 4:3 polyrhythm (Vuust et al., 2006; Vuust, Wallentin,
Mouridsen, Østergaard, & Roepstorff, 2011). However, a very recent study by Stupacher et
al., (2017) suggests an alternative interpretation since they failed to find any effect of musical
expertise on EEG response when participants listened to a 3:4 cross-rhythmic sequence.
However, the predicted group difference in persistent endogenous oscillations did emerge
during the silent periods of 2-3 seconds that immediately followed each polyrhythmic
sequence. The authors speculated that under music listening, measurable brain activity
constitute a resultant of the close interaction between bottom-up (rhythm) and top-down
(meter) perceptual processes. Bottom-up responses are more or less hardwired, probably
independent of musical expertise, and activated independently of which metric framework
that is endorsed while listening. Top-down mechanisms in contrast, represents the metric
mental model. Importantly, neural oscillations generated from bottom-up processing were
found to have stronger amplitudes than top-down processes (Stupacher et al., 2017). Hence,
under music listening, expertise-sensitive top-down mechanisms could have been masked by
the strong effect of bottom-up rhythm perception, at least when measuring domain general
brain activity (pupil dilation, EEG), not activity in specific areas (i.e., BA47). In the silent
period, however, bottom-up processing is removed. Unfortunately, we did not record pupil
sizes within the silent periods following the auditory stimuli.
An additional account is the possibility that musicians have a higher initial response (i.e.,
prediction error) to metric challengers, as MMN-studies typically have suggested, but they
42
can also adjust their internal models more efficiently (using less effort) over a period of time,
so that the averaged pupil dilation over the 30 seconds would appear similar to the non-
musicians’. Furthermore, pupil response can index aesthetic experiences in music listening
(Laeng et al., 2016), which may provide yet an alternative account for the lack of pupillary
group differences in The Polyrhythm Experiment especially. Because musicians and non-
musicians did rate the experiments’ musical stimuli to be equally interesting, it is not
unreasonable to also expect similar ‘aesthetic’ pupillary response from the two groups, when
being exposed to more and perhaps also more interesting musical information (i.e., in ‘the
polyrhythmic condition’).
One obvious explanation for the negative finding across both experiments is statistical
power. In other words, there might be a difference between groups in the present sample, but
that N is too low to be able to confirm it. After all, 16 participants constitute a small group in
general for psychology experiments as well as pupillometry experiments. More participants
might have brought to surface ‘hidden’ group differences. We might also argue that possible
expertise effects on effort were obscured because both our groups were to some extent
musically skilled, as the non-musicians’ scores on Musical Ear Test (MET) suggested, being
higher than the score of earlier samples of non-musicians (Hansen et al., 2012; Wallentin et
al., 2010). Two non-musician participants even scored higher on MET than the professional
jazz musicians with the lowest scores. In fact, the non-musician group included both non-
musicians and amateur musicians. Had the non-musicians group been singled out in two
separate groups (non-musicians vs. ‘amateur’ musicians), this may also have influenced the
result (e.g. Oelmann & Laeng, 2009). Importantly though, we did observe clear group
differences both in MET-scores and in tapping accuracy on both experiments, reducing the
force of the above account.
5.2.2 Musicians outperformed non-musicians in tapping accuracy The tapping results did document musicians’ more efficient processing of polyrhythmic and
microtiming features. In principle, sensorimotor synchronisation is enabled by neural events
that are entrained with the music (e.g., Stupacher et al., 2017). Differences in tapping
accuracy may therefore be seen to reflect differences in neural functionality, engendered by
levels of rhythmic expertise. Across experiments and conditions, the musicians consistently
tapped with substantially higher rhythmic accuracy, despite apparently not requiring more
mental effort than the non-musicians. This clearly illustrate the enhanced rhythm perception
43
skills of professional musicians. Interestingly, the relative difference in performance between
groups increased when meter perception ‘challengers’ like polyrhythm and microtiming were
introduced, as we specifically expected: In Experiment 1, despite both groups’ tapping
accuracy decreased from plain to polyrhythm, the negative effect of polyrhythm on tapping
accuracy was more pronounced in the non-musicians than in the musicians. Similarly, in the
microtiming experiment, tapping accuracy was more negatively affected by microtiming in
the non-musicians, although negatively influencing both groups. We may compare our
microtiming tapping results especially to a study by Hove et al. (2007) who found that
occurrence of onset (microtiming) asynchronies increased non-musicians’ tapping variability
(SD of ITI, a negative measure of tapping accuracy), but decreased tapping variability (i.e.
increased accuracy) for the musicians. However, the magnitude of microtiming was larger
(up to 80 ms) in our experiment, than in the Hove et al.-study (up to 50 ms) and only the ±80
ms, and not ±40 ms condition did in fact affect tapping accuracy. In addition, Hove and
colleagues presented two simple sine tones in asynchrony, with an absence of additional
rhythmic cues. Therefore, their musicians might have been able to place the two onsets into a
single internal metric model, aiding their sense of time and increasing their tapping accuracy.
In contrast, there were several rhythmic cues throughout the present study and the bass/drums
asynchrony apparently confused rather than aided beat placement.
5.3 Microtiming and the experience of groove The participants’ response profiles on our two rating scales (‘wanting to move the body to the
music’ and ‘musical well-formedness’) were considerably correlated (r = .56 - .80),
suggesting that an experience of groove (addressed by question 1) is partly an experience of
the groove being musically well-formed (addressed by question 2). Thus, treating the two
scales as one combined score did seem to make both theoretical and empirical sense,
although the two scales may capture unique and subtle aspects of the musical experience.
Also, general ratings of ‘well-formedness’ suggested that participants did not experience the
excerpts as particularly unnatural in musical terms, indicating an adequate ecological validity
of The Microtiming Experiment.
As mentioned earlier, although microtiming is assumed to play a vital role in much
groove-based music, systematic experimental investigations have given inconsistent evidence
as to whether microtiming asynchronies in grooves actually promotes groove experience
(Davies et al., 2013; Kilchenmann & Senn, 2015). Nevertheless, the present findings seem
44
rather consistent with earlier studies. Indeed, non-deviated grooves were generally rated
highest on both scales. The larger the microtiming asynchrony magnitudes, the lower the
clips were rated. There could be at least four reasons why the most highly rated grooves were
the on-the-grid versions. First, it might be that systematic microtiming asynchrony between
bass and drums is not a correlate to a groove experience, at least not for the type of grooves
chosen for this experiment. Second, it might be that microtiming in a rhythm section dyad
only fully reaches its potential when the musicians (or producer) intuitively try to groove.
That is, ‘moving around’ mechanically on bass tracks in ProTools is a rather non-musical
way of achieving ‘tasteful’ rhythmic nuances. Roholt (2014) suggests that the analytical
approach of measuring (and in this case manipulating) timing nuances may give little sense
as a way to understand microtiming’s role in groove-based music, as long as measurements
are not connected to a phenomenological account of the groove (i.e., the effect it has on the
listener). An interesting finding from the present study may illustrate this point: the second
highest rated clip was the +40 ms version of the high complexity groove (as shown in Figure
11; the only clip that was rated higher, was the on-the-grid version of the same groove type).
The high complexity groove was the only excerpt based on a real tune, and the +40 ms
condition was the excerpt that resembles most precisely the timing of the bass and drum track
from the original version of Really Love. One might therefore speculate that this particular
excerpt reflects a pattern between bass and drums where microtiming is indeed suitable, and
that the ‘groovy’ feel from the original album was partly re-created in the corresponding
condition of the present experiment. Third, since Roholt (2014) argues that the potential
groove promoting effects of microtiming depend on its role in the musical context and that
music is perceived as a figure/ground kind of relationship, microtiming should not be given
the figure role; instead, to contribute to a groovy experience, it must fade into the
background. The rather repetitive and non-ecological experimental setting could have
contributed to both an analytical rather than a more ‘holistic’ listening mode, with an
enhanced focus on the asynchronies. Fourth, when only two instruments were presented in
asynchrony, this created a confusing sense of two separate, phase-shifted, rhythmic streams.
If the timing of either drums or bass had been supported by additional instruments, like a
synth or a guitar, this would have given more acoustic salience to one of the timings, and
therefore a stronger cue in the listener to experience that timing as the main one, with
asynchrony timing either pushing or pulling against. Said differently, reducing the ambiguity
concerning which time is experienced as the reference time may be a key to experience
microtiming asynchrony as groove promoting.
45
Group differences emerged also on subjective ratings. Consistent with earlier studies
(Davies et al., 2013; Senn et al., 2016), musicians showed an increased responsivity to
different microtiming conditions, compared to the non-musicians, with the use of wider
ranges in the rating scales. It is not unknown that musicians outperform non-musicians in
discriminating between fine-scaled rhythmic nuances. For example, while trained listeners
need around 20 ms asynchrony between two tones (in isolation) to decide which of them is
presented first (Hirsh, 1959), untrained listeners usually need longer time intervals
(Broadbent & Ladefoged, 1959). Even if it was plausible to speculate that musical
competence would increase not only responsivity, but also tolerance or even preference for
microtiming (as for example suggested by Hove et al., 2007), such tolerance was only
reflected in tapping data, not in subjective rating or pupil data.
Finally, although the on-the-grid versions were generally the most preferred, we made
the interesting observation that participants seemed to prefer the microtiming conditions
where the double bass was timed after the drums (similar to the original of D’Angelo’s
Really Love), in favour of ahead of the drums. Moreover, -80 ms condition (bass 80 ms ahead
of drums) yielded both the highest effort, lowest tapping accuracy and lowest rating of all
excerpts. It is not clear however, if this finding can be generalized to other musical genres,
such as a bass/drums swing jazz accompaniment.
5.4 Groove experience, effort and complexity A question of interest concerned the processing of microtiming, and involved exploring
possible systematic relations between effort and groove experience, moderated by rhythmic
structural features/complexities. Unravelling such associations is intriguing, but a
considerable endeavour, given the somewhat paradoxical ‘effortless-but-complex’-dialectic
of groove and groove experience. On one hand, music theorists, musicians and listeners may
characterize groove as a seemingly smooth and effortless musical attendance (Danielsen,
2006; Janata et al., 2012; Roholt, 2014), being paralleled with the well-researched concept
(both in music and elsewhere) of flow (Csikszentmihalyi, 1990; de Manzano, Theorell,
Harmat, & Ullén, 2010). On the other hand, groove-based music also admittedly contains
structural complexities that challenge or even violate listeners’ sense of meter (Vuust et al.,
2014; Witek, 2017). Thus, one possibility is that different structural rhythmic features pave
the way for different connections between effort and ratings of groove; allowing results both
supporting the effortless- and the complexity-side of groove.
46
Janata et al., (2012) suggest that the affectively positive psychological state of feeling
in the groove is closely linked to perceptual fluency, as individuals are able to anticipate
meter and distinct musical onsets, and actualize or imagine them in relation to body
movements. The authors found participants’ tapping accuracy to be higher when engaging
with music rated as high-groove, compared to low-groove; participants also rated tapping and
movement synchronisation tasks as easier when they at the same time rated the music as
groovier. Hence, high predictability and ‘effortless’ attendance seems to be two related key
aspects to groove experience, since predictability comprises perceptual fluency that is again
related to ‘effortlessness’. Indeed, we found that complexity in the form of microtiming
seemed to increase prediction error in a way that concomitantly increased effort and
decreased groove. The on-the-grid excerpts, being rhythmically most predictable, were the
highest rated, having the highest tapping accuracy and at the same time demanding least
cognitive workload from the participants. When bidirectional microtiming asynchrony
increased, this decreased subjective ratings, tapping accuracy, and strengthened the pupil
response; this pattern emerged both when participants listened, and when they tapped their
perceived beat. The microtiming asynchronies’ effect on pupil size yielded a U-shaped curve
(Figure 7), while the microtiming effect on groove rating gave an inverted U-shaped curve
(Figure 10). It is tempting to propose that microtiming asynchronies in the present
experiment, perhaps with the exception of the most ecologically valid +40ms D’Angelo
excerpt, were experienced by the listeners, not as groove promoters, but more as pure
unmusical ‘metric violations’. Manifested as brain response, the prediction error between
rhythm and meter caused by microtiming asynchrony was signalled by the observed
corresponding increase in effort.
Additional evidence from tapping and subjective rating data supporting the effortless-
notion of a groove experience, was the finding that timekeeping increased both tapping
accuracy and groove ratings; this being consistent with Madison et al. (2011). Timekeeping
probably reduced metric ambiguity and increased predictability, independent of whether
microtiming features was present or absent. Probably, the hi-hat eight-notes provided
listeners with more temporal information and simultaneously added extra salience to the
drum kit timing, making it appear more like the ‘main’ timing, in favour of the timing of the
bass or ‘somewhere between the timing of the bass and drum’ (like discussed in the previous
section). However, we did not observe any significant effect of pupil responses by adding hi-
hat eighth notes, as it could possibly be expected.
47
Some of our results also indicate that groove experience does not only emerge from
predictability, but also from complexity. To illustrate: even if a series of metronome click
have extremely high rhythmic predictability and is easy to bodily synchronise with, that does
surely not alone promote groove (Stupacher et al., 2013). It has recently been both
theoretically proposed (Witek, 2017) and empirically shown (Witek et al., 2014) that
structural complexities producing tension between the rhythm and the underlying meter
(without breaking down the listeners’ metrical model), may be groove experience
components by virtue of inviting the participant to participate. This would allow the
‘fulfilling’ with their bodies the open spaces or metrical ambiguities in the groove. Witek et
al. (2014) found an inverted U-shaped curve between syncopation density in drum kit
grooves, and groove experience. Thus, too little and too much syncopation (complexity)
yielded lower groove experience, while medium syncopation was related to higher groove
experience. The findings were thought to reflect a general inverted U-shaped relation
between structural complexity and aesthetic pleasure of art (Berlyne, 1971). Interestingly, in
the present study, higher levels of complexity (numbers of syncopations and sound event per
bar) increased groove ratings, and in addition respondents were more responsive to
microtiming when complexity increased (Figure 11), giving a ‘possibility’ for microtiming to
play a more adaptive role in more complex groove matrices. However, we did not observe a
U-shaped pattern between complexity and groove experience, possibly because grooves with
an ‘exaggerated’ density of syncopations/notes in our stimuli material were not included, as
in Witek and colleagues’ study. This was perhaps an additional reason for finding this
variable to be unrelated to mental effort. A complexity effect emerging from
syncopations/sound events per bar could nevertheless be seen in participants’ tendency to tap
with lower tapping accuracy, even if groove ratings were positively related to complexity.
This last finding contrasts the earlier results by Janata et al., (2012) and underlines the
impression that predictability alone does not necessarily increase groove.
To conclude, different types of rhythmic features (microtiming, ‘meter-supporting’
timekeeping and syncopations) might have yielded different kinds of effects on mental effort,
tapping and subjective ratings – together supporting the ‘effortless-but-complex’-dialectic.
When moderated by microtiming, groove ratings were negatively related to effort, probably
since microtiming was experienced more like a metric violation than a groove promoter. Hi-
hat eighth notes increased predictability and concomitantly yielded higher groove ratings and
tapping accuracy. Yet, under influence of timekeeping, groove ratings were empirically
unrelated to mental effort, since adding a timekeeper did not influence pupil response.
48
Syncopations (and note onsets per bar) represented a structural complexity that promoted
groove ratings despite lowering tapping accuracy, but interestingly it did not affect mental
effort. Therefore, in the present experiment, mental effort in musical grooves signal metric
violations that have concomitantly a decreasing effect on groove. It remains an open question
whether the fact that structural features which increased groove ratings were not reflected in
pupil size, can be generalized to other groove-contexts.
5.5 Limitations and methodological considerations As in all scientific investigations, the present study has several limitations, of which some
have been discussed throughout the text. Not yet discussed are possible problems related to
recruitment of participants. Participants were recruited from a personal network of musicians
and non-musicians. Musicians were told that they were invited to participate because they
were musicians, giving them an incentive to ‘perform well’. In contrast non-musicians, who
were selected on the basis of being ‘not musicians’, by being deprived of a certain quality.
Moreover, the experiment was not blinded. The author was the experimenter whom is also a
professional musician. His presence during the experiment could have yielded more
performance anxiety to the musicians.
An obvious limitation of the present study lies in the ecological aspect of the
microtiming stimuli, being all ‘artificially’ designed. This made experimental control
possible, but had several drawbacks related to our research questions (e.g. of microtiming’s
relation to groove). In addition, the ‘Complexity’ variable was confounding syncopation
density with note onsets per bar. We chose this solution, since the processing effects from
syncopations were not the main aim of the present study. There was also a potential source of
bias in that the high complexity groove constituted a copy of an original tune (Really Love),
which several participants perhaps recognized; this was not asked for. Another issue was that
we did not control statistically for musical genre preference. We mapped this in the
questionnaire, but did not see the direct advantage of including it in the study, since the
grooves (at least the ‘low’ and ‘medium’) were quite ‘generic’. Moreover, we did not in
particular address the role of affect and aesthetic experiences in this study, although
discussing it briefly in part 5.2.1. This is nevertheless a limitation, since affect is an important
part of music perception in general and of the experience of groove in particular.
There were technical problems related to tapping data, already discussed in the
‘Methods’-section. The absolute offset tapping data seemed unreliable, so we did not get the
49
chance to investigate how microtiming influenced absolute offsets. Clearly, it was non-
optimal to tap on a standard PC-keyboard; a device specially made for tapping would perhaps
have made a significant difference. As for the groove ratings, we might have measured
participants’ actual movements (cf. Kilchenmann & Senn 2015), instead of just asking about
‘wanting to move’. Though, an obvious challenge was that each participant’s head was kept
in a fixed position while listening to music, in order to get optimal pupil data. As was
discussed earlier, although presenting two theories of meter perception (PC and DAT), we do
not have any foundation to say which of the two theories that explain our data better.
5.6 Future studies The present study may spark the interest in several future investigations on the following:
• Syncopations and effort, for example with syncopation stimuli from the study by Witek et
al. (2014), to further investigate the relation between rhythmic complexities, effort and
groove experience, also when syncopation density is ‘exaggerated’.
• The effect of microtiming on effort and ratings in instances where ecological validity and
‘aesthetics’ are maximized, for example by using original tracks.
• The relevant musical contexts for microtiming, that is, the characteristics of musical and
rhythmic patterns that are suitable for microtiming to play a groove-promoting role.
• Replications of the present microtiming study with swung eight-note jazz
accompaniments (e.g. cymbal and bass; see Collier & Collier 1996), instead of the even
eighth grooves of the present study, also to assess generalizations from the present study
to other genres.
• The effect of effort related to free form rather than isochronous tapping. In a free-form-
paradigm, participants could be asked to tap whatever rhythmic pattern to the music that
felt comfortable. The hypothesis is that this should be more effort demanding (Janata et
al., 2012).
• General and expertise effects in effort during silent periods following rhythmic material,
to measure effects of top-down processes only, as done by Stupacher et al. (2017), and
discussed in section 5.2.1.
• Effects from maximizing group differences, for example by comparing professional
drummers/percussionists with participants with dysrhythmia (Launay, Grube, & Stewart,
2014), having specific impairments in meter and beat perception.
50
6 Conclusion By addressing relations between local auditory events (rhythm) and the underlying mental
structure (meter) in groove-based music, the present study taps into cognitive and brain
functioning in terms of time perception, anticipation, bottom up/top-down processing,
musical effort and the experience of groove. Further, it adds to the scientific literature of
documenting domain-specific expertise effects of music perception. To our knowledge, it is
the first time an index of mental effort – measured by pupillary response – is employed
during exposure to instances of polyrhythm and microtiming in a groove context. Both
presence of polyrhythm/cross-rhythms and magnitudes of microtiming asynchronies between
bass and drums were found to be positively related to mental effort and negatively related to
tapping accuracy. The main results apparently support Dynamic Attending Theory (DAT)
and Predictive Coding Theory (PC), which both predict that rhythmic instances (complexity)
lowering prediction accuracy, should increase effort and decrease tapping accuracy. Group
differences in pupil response were not observed and possible reasons for this were discussed.
Nevertheless, the perceptual skills related to rhythm perception in professional jazz musicians
were strongly reflected in their superior tapping accuracy, despite not allocating more effort
to the task than the non-musicians. In both experiments, tapping the beat while listening
yielded higher psychophysiological effects (more mental effort) than when listening only.
When giving their subjective ratings of groove and well-formedness, participants
generally preferred the on-the-grid grooves more than the microtiming grooves, consistent
with existing research. However, grooves that reflected a rhythm matrix in which
microtiming had been suitable in a real musical setting did yield high ratings also in the
corresponding microtiming condition. Timing conditions where bass was timed behind drums
were more preferred than conditions with bass timed ahead of drums. Musicians were more
responsive (used a bigger ranger of the subjective rating scale) but less tolerant to
microtiming, than the non-musicians. Finally, we proposed that the link between mental
effort and groove experience, moderated by rhythmic complexity, is dependent on ‘type’ of
complexity. In our stimuli material, rhythmic features that increased groove were not
reflected in effort, even if structural complexity (syncopations) was added. In contrast, the
rhythmic complexity of microtiming asynchronies decreased groove and was related to
higher effort, possibly functioning more like a pure metrical violation than a groove
promoter.
51
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7.2 Discography D’Angelo (2000). Voodoo. RCA.
Destiny’s Child (2004). Survivor. Columbia.
Jack Ü, Skrillex, Diplo (2015). Skrillex and Diplo presents Jack Ü. WEA International.
Keita, Salif (1987). Soro. Island Records.
Spears, Britney (2011). Femme Fatale. JIVE records.
Sting (1987). Nothing Like the Sun. A&M Records.
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7.3 Filmography
Collins, A. (2014). How playing an instrument benefits your brain. TED Education.
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brain-anita-collins
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8 Appendix 8.1 Figures A1-A7
Figure A1. Experiment 1: Overview of ‘the polyrhythmic condition’; an excerpt from Sting’s Lazarus Heart (1987). Adapted from Vuust et al., (2006).
Figure A2. Experiment 2: Low structural complexity groove without hi-hat (‘low’)
Figure A3. Experiment 2: Low structural complexity groove with hi-hat (‘low HH’). See Figure A2 for further details on tempo, timing and instrumentation.
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Figure A4. Experiment 2: Medium structural complexity groove without hi-hat (‘medium’). See Figure A2 for further details on tempo, timing and instrumentation.
Figure A5. Experiment 2: ‘Medium structural complexity groove with hi-hat (‘medium HH’). See Figure A2 for details on tempo, timing and instrumentation.
Figure A6. Experiment 2: High structural complexity groove (‘high HH’). See Figure A2 for details on tempo, timing and instrumentation.
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Figure A7. Stereo waveform (amplitude/time) illustrating the microtiming asynchrony between bass and drums in D’Angelo’s Really Love (2014). The onsets of the kick drum and the bass are marked. Made by AD.
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8.2 Tables A1-A5 Table A1 Overview of experimental procedure
Note: Order of stimuli was counterbalancd for half of the participants in each group (musicians/non-musicians).
Table A2 Tapping exclusions in Experiment 1
Note: Tapping exclusions were calculated according to rules accounted for in the ‘Data processing’-section. Table A3 Tapping exclusions in Experiment 2 (overview from Table A4 & A5)
Note: Tapping exclusions were calculated according to rules accounted for in the ‘Data processing’-section.
Overview of experimental procedureExperiment Condition Trial
Experiment 2: Microtiming 'Listen only', rate excerpts after listening 1-25
Experiment 1: Polyrhythm vs. non-polyrhythm 'Listen only', rate excerpts after listening 1-2
BREAK
Experiment 1: Polyrhythm vs. non-polyrhythm Tap with the beat 25-1
Experiment 2: Microtiming Tap with the beat 2-1
BREAK
Musical Ear Test 1-52
Tapping exclusions in Experiment 1’the polyrhythmic condition’
’the plain condition’
Total
Musicians
Excluded trials 1 0 1
Missing reference points in included trials 3 0 3
Double taps in included trials 5 3 8
Non-musicians
Excluded trials 4 2 6
Missing reference points in included trials 10 4 14
Double taps in included trials 14 10 24
Tapping exclusions in Experiment 2 (overview from Table A4 & A5)Musicians Non-musicians
Excluded trials 5 63
Missing reference points in included trials 149 109
Double taps in included trials 32 296
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Table A4 Tapping exclusions in Experiment 2, across ‘Microtiming’ and ‘Complexity’-conditions
Note: Tapping exclusions were calculated according to rules accounted for in the ‘Data processing’-section.
Tapping exclusions in Experiment 2, across ‘Microtiming’ and ‘Complexity’-conditionsMusicians ‘low’ ‘low HH’ ‘medium’ ‘medium HH’ ‘high HH’ Total-80 ms
Excluded trials 2 0 0 0 0 2
Missing reference points in included trials 2 4 13 1 16 36
Double taps in included trials 2 0 1 3 0 6
-40 ms
Excluded trials 0 0 1 0 0 1
Missing reference points in included trials 4 3 6 5 13 31
Double taps in included trials 1 2 0 2 2 7
0 ms
Excluded trials 0 0 0 0 0 0
Missing reference points in included trials 3 2 4 4 14 27
Double taps in included trials 2 0 0 1 0 3
+40 ms
Excluded trials 0 0 0 0 0 0
Missing reference points in included trials 7 1 4 2 15 29
Double taps in included trials 5 5 2 0 0 12
+80 ms
Excluded trials 1 0 0 0 1 2
Missing reference points in included trials 4 2 2 1 17 26
Double taps in included trials 2 1 0 1 0 4
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Table A5 Tapping exclusions in Experiment 2, across ‘Microtiming’ and ‘Complexity’-conditions
Note: Tapping exclusions were calculated according to rules accounted for in the ‘Data processing’-section.
Tapping exclusions in Experiment 2, across ‘Microtiming’ and ‘Complexity’-conditionsTotal Non-musicians ‘low’ ‘low HH’ ‘medium’ ‘medium HH’ ‘high HH’ Total
-80 ms
2 Excluded trials 5 4 5 2 1 17
36 Missing reference points in included trials 2 2 11 4 10 29
6 Double taps in included trials 9 15 15 18 21 78
-40 ms
1 Excluded trials 2 6 3 1 1 13
31 Missing reference points in included trials 4 0 3 2 5 14
7 Double taps in included trials 14 5 14 1 2 36
0 ms
0 Excluded trials 2 2 2 3 3 12
27 Missing reference points in included trials 3 1 3 5 11 23
3 Double taps in included trials 19 7 4 8 21 59
+40ms
0 Excluded trials 3 1 0 2 3 9
29 Missing reference points in included trials 1 2 7 0 7 17
12 Double taps in included trials 7 9 15 6 20 57
+80ms
2 Excluded trials 3 0 2 3 4 12
26 Missing reference points in included trials 4 4 4 2 12 26
4 Double taps in included trials 17 9 13 12 15 66