Groove on the brainrhythmic complexity and predictive coding
Peter VuustDirector of Center for Music in the Brain (MIB)
Profes s or a t the Roy al Academy of Mus ic , Aarhus , DenmarkProfes s or a t Aarhus Univ ersi ty
PhD. in neuros c ienc e, MSc . In math, French and mus ic Bas s is t/c ompos er
DNC - Danish Neuroscience Center
MR / fMRI PET MEG EEG
RAMA
Center for Music in the Brain
Groove and the brain� Humans move in time to music
� Humans mov e s pontaneous ly to s ome k inds of mus ic more than other (Madis on et a l 2006, J anata et a l . 2012)
� Different body -parts embody d i fferent metric a l leve ls in d i fferent s patia l orientations (Toiv ia inen et a l . 2010, Burger e t a l . 2012)
� Rhy thmic c omplex i ty in fluenc es s ens orimotor s y nc hronisation(Repp 2005, 2013, Pate l 2005, Kel ler e t a l . 2005, Konv al ink a et a l 2011, 2013, Heggli et a l in prep)
� Rhythm perception involves auditory and motor areas of the brain� Sec ondary motor areas , bas al gangl ia , c erebellum, s uperior tempora l
gy rus (Penhune et a l . 1998, Vuus t e t a l . 2005, Grahn et a l 2007, 2011, Chen et a l . 2008, Stupac her et a l . 2012), in ferior fronta l gyrus , anterior c ingulate, temporo-parie ta l junc tion (Vuus t e t al . 2006, 2011)
� Potential for rehabilitation� Park ins on’s d is eas e (Thautet a l . 1996, Benoi t e t a l . 2014, Kotz et a l . 2015) � After s trok e (Sc hneider e t a l . 2007, Al tenmül ler et a l 2009)
� Relatively unique to human beings� But s ee e.g. Pate l e t a l . 2009 or Cook et a l . 2013 � Beat trac k ing is not s imple
Gra h n & Brett (2 0 0 7 )
Vu u st et a l (2 0 1 1 )
In ferior fronta l gy rus (BA47)
Groove – The pleasurable desire to move
What are the brain mechanisms related to groove?Which grooves engage us the most?
Predictive coding of music
)()()|()|( inputp
modelpmodelinputpinputmodelp =Vu u st & Witek 2 0 1 4
Geb a u er, Krin g elb a ch & Vu u st , 2 0 13Vu u st et a l. , 2 0 0 9
Vu u st & Frith , 2 0 0 8
1 32 4
Prediction and prediction error
Prediction error
0 100 200
-50
0
50
100 fT/c m
ms
s Ιs ΙΙs ΙΙΙ
EEG
Vuus t e t al , Cortex , 2009
The optimal amount of syncopation
• “How much does this rhythm makeyou want to move?”(1-5)
• “How much pleasure do you experience with this rhythm?” (1-5)
� Stimuli� 50 funk drum-breaks, � 80% taken from real grooves (funk/rock repertoire) 20% composed
by authors� Synthezised drum kit (BD, SD, HH)� 2-bar phrase, 4 repetitions, 120 bpm, 16 sec.
low medium high
Witek , Clark e, Wal lentin , Kringelbach, & Vuus t, PLoS One, 2014
Syncopation and groove
Wan
ting
to M
ove
Exp
erie
nce
of P
leas
ure
β=-.568 β=-.667
Simple Complex Simple Complex
MOVEMENT PLEASURE
There is a rhythmic sweet spot for pleasure and desire to move.
Syncopation Syncopation
low med high
Syncopation (𝑆 - g(µ))(amount of stimulusdeviation from the meter)
Meter experience (π )(precision of the prediction)
x
Explainable prederror(Musical appreciation?)
=
Relationship between syncopation and meter
26 Partic ipants
15 drum-break s:5 low5 medium5 h igh
Evidence for a broken predictive model(Motion Capture)
Meas ures :� Mov ement forc e (ac c eleration)� Sy nc hronis ation acc urac y (cros s-c orre la tion at main puls e)� Period ic i ty prominenc e (auto-corre la tion)Witek et a l, Exp Bra in Res. 2 0 1 7 , in Press,
Sync
hron
izat
ion
inde
x
SPM analysis (mean bold)
Low Medium High
Right*
Low Medium High
*
Low Medium High
**
Auditory Cortex Pallidum/Putamen Anterior Inferior Insula
0
2
4
6
Audition MotionEmotion
Challenging rhythms employ auditorymotor and sensory integration networks
Connectivity analysesHigher meta-stabilit y when the music grooves
EstimatedEffective connectiv ity
low medium high
Groove ratings
Deco & K ri ngel bach (2014) Neuron
???rhythmic complexity
rhythmic complexity
subj
ectiv
e g
roov
e
Low Medium High
ordered metastable random
L H
M
neural dynamics
Wi tek, Gi l son, Cl arke, Wal l ent i n, ,Deco, K ri ngel bach & V uust , 2017, submi t ted
The networks of groove
The connections lis ted in order of s ignificance (p<0.001, uncorrected)
L Hippocampus L PrecentralL Thalamus L PrecentralR Thalamus L Sup Motor AreaL Inf Occipital L Mid OrbitofrontalL Inf Occipital L ParahippocampalL Pallidum L Sup OccipitalL Supramarginal L PallidumL Thalamus L Mid TemporalR Amygdala R Mid TemporalL Post Cingulum R AmygdalaR Thalamus R Sup Motor AreaR Thalamus R Sup Frontal
123456789101112
A B
C D
Groove: The interaction between rhythmand harmony
HARMONY
RHYTHM
LOW
Major triads
MED
7th chords with tensions
HIGH
including b9-intervals
LOW(Clav e minus s y nc opation)
MEDClave
HIGHWrong Clave
Witek, Ma th ews, Heg g li, Lu n d , Pen h u ne & Vu u st, in p rep a ratio n
6 versions of each of the 9 categories (54 stimuli in total)
The influence of complexity on ”move” and ”pleasure”
Wanting to move
low med high
Pleasure
low med high
Online Survey201 respondents from five continents Task: to rate (1-5)
� wanting to move
� pleasure
Additionally, information on musical training, enjoyment of groove music and dancing was collected Ma th ews, Witek, Heg g li, Pen h u n e & Vu u st, in prep a ra tio n
Rhythmic complexity Rhythmic complexity
Data analysesLinear mixed effects (maximal random structure):
Interaction between group and complexity
Wanting to move
low med highRhythmic complexity P < .05
� Stimuli� 2 x 2 design (rhythmic and harmonic complexity (MM, MH, HM, HH)� Repeated piano chord patterns + hihat
� Participants� Musicians: N = 26� Non-music ians: N = 29
� Task: to rate (1-5)� wanting to move � pleasure� perceived beat strength
� fMRI � Multi-echo (2 echoes) BOLD fMRI� TR=2
� (DTI and facial EMG)
Brain processing of rhythmic and harmonic complexity
Ma th ews, Witek, Lu n d , Pen h u n e & Vu ust, in prep a ra tion
HARM O NY
RHYTHM
LO W
M ajor t r iads
M ED
7t h chor ds wit h t ensions
HI G H
including b9-int er vals
LO W( Clave m inus syncopat ion)
M EDClave
HI G HWr ong Clave
Ratings (1-5)
β
Wanting to Move
Rhy thm 0.6***
Harmony 0.1***
Group*Rhy thm*Harmony 0.03*
Pleasure
Rhy thm 0.6***
Harmony 0.2***
Group 0.01*
Group*Rhy thm*Harmony 0.02^
med highRhythmic complexity
med high med highRhythmic complexity
med high
*p < .05, **p < .01, ***p < .001, p̂ < .06
Data analysesLinear mixed effects (maximal random structure):
Extracted Betas
L Caudate
p < .05 (FDR)
R Putamen
Musician Non−Musician
Med High Med High−1.0−0.5
0.00.51.0
Rhythmic Complexity
L ve
ntra
l BG
Bet
as
Harmonic ComplexityMedHigh
Extracted Betas
med highRhythmic complexity
med high
Rhythmic complexitymed high
Basal ganglia activation
L SMAL PMC R PMC
L mOFC
L
Extracted Betas
med highRhythmic complexity
med high
−1
0
1
2
Med HighRhythmic Complexity
SMA Group
MusNon−Mus
Extracted Betas
med highRhythmic complexity
med high
OFC, PMC & SMA
Statistics
βR PutamenRhythm 0.13**L CaudateRhythmGroupGroup*Rhythm*Harm
0.13***0.05*0.03*
R CaudateRhythm 0.13***L SMARhythmGroup
0.09**0.06*
R PMCGroup 0.04*L mOFCRhythmGroup*Rhythm*Harm
0.08**0.03*
*p < .05, **p < .01, ***p < .001
Conclusions� The predictive coding model provides an elegant framework for interpreting brain
processing of music and informs our understanding of � The relationship between rhythm and meter� Motor behavior in relation to rhythm
� There is an inverted U-shaped relation between degree of syncopation and ”wanting to move”/”pleasure”. This U-shape is reflected in activ ity in measures of connectiv ity and meta-stability in the brain.
� The motor system seems to be driven by emotional and sensory systems a process that implicates reward areas of the brain.
� The clavé stimulus combining harmony and rhythm produces a strong sensation of groove in relation to medium syncopated rhythms compared to high and low complexity and involves motor and reward related brain activ ity.
� Musicians have more groove-related (pre)-motor activ ity than non-musicians
� Syncopation is not everything!
Thanks for listening . . .
Top Related