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Behavioral and DTI
Studies on Normal
and Impaired
Learning of Musical
Structure
Psyche Loui
Wesleyan University
CogSci 2013
August 1, 2013
The world knows and loves music
Tone-deafness: a disorder of pitch
perception and action
 Congenital amusia
 Inability to sing in tune
 Incidence: 4 – 17%
 Montreal Battery of
Evaluation for Amusia
 Inability to discriminate pitch
>1 semitone threshold
(musicianbrain.com/pitchtes
t)
What is the source of musical
knowledge?
Frequency
Probability
Pitch
Harmony
Melody
Perception
Existing musical systems confound learning
with memory
Test learning with new frequencies &
probabilities
New musical system
Tone-deafness
We need a system to assess
implicit music learning
Bohlen-Pierce
A new tuning system – the BP scale
F = 220 * 2 n/12
F = 220 * 3 n/13
200
300
400
500
600
700
0 1 2 3 4 5 6 7 8 9 10 11 12 13
increments (n)
frequency(Hz)
Western
Loui, Wessel, & Hudson Kam, 2010, Music Perception
The BP scale can form chords
200
300
400
500
600
700
0 1 2 3 4 5 6 7 8 9 10 11 12 13
increments (n)
frequency(Hz)
F = 220 * 3 n/13
Bohlen-Pierce
3 : 5 : 7
Loui, Wessel, & Hudson Kam, 2010, Music Perception
Composing in the Bohlen-Pierce
scale
10 7 10 10
6 4 7 6
0 0 3 0
F = 220 * 3 n/13
Krumhansl, 1987;
Loui, Wessel, & Hudson Kam, 2010, Music Perception
Composing melody from harmony –
applying a finite-state grammar
10 7 10 10
6 4 7 6
0 0 3 0
Loui, Wessel, & Hudson Kam, 2010, Music Perception
Melody: 6  4  7  7  7  6  10  10
10 7 10 10
6 4 7 6
0 0 3 0
Composing melody from harmony –
applying a finite-state grammar
Loui, Wessel, & Hudson Kam, 2010, Music Perception.
10
Can we learn the B-P scale?
General design of behavioral studies:
1. PRE-TEST
 assess baseline
2. EXPOSURE to melodies in one grammar
 ~30 minutes
3. POST-TESTS
 assess learning
Learning a musical system:
Probability sensitivity
 Can we remember old melodies?
2-AFC test of recognition
 Can we learn new melodies?
2-AFC test of generalization
Double dissociation between learning
and memory
No. of melodies
12740100No. of repetitions
5 10 15 400
40%
50%
60%
70%
80%
90%
100%
PercentCorrect
0
0.2
0.4
0.6
0.8
1
1.2
Differenceinrating
(familiar-unfamiliar)
recognition
generalization
Loui & Wessel, 2008
Loui, Wessel & Hudson Kam, 2010
Disrupting harmony – the forced
octave scale
0 1 2 3 4 5 6 7 8 9 10 11 12 13
Increments (n)
0 1 2 3 4 5 6 7 8 9 10 11 12 13
200
300
400
500
600
700
Frequency(Hz)
200
300
400
500
600
700
Western scale: F = 220 * 2 n/12
B-P scale: F = 220 * 3 n/13
Forced-octave scale: F = 220 * 2 n/13
3 : 5 : 7
3:4.13:5.11
Loui, 2012, TopiCS
Generalization
Disrupting melody – eliminating
select transitional probabilities
10 10 7 10
6 7 4 6
0 3 0 0
Loui, 2012, TopiCS
Generalization
Mechanisms enabling generalization in musical
AGL depend on transitional probabilities.
Learning a new musical system:
Frequency sensitivity
 Can we learn to expect frequent tones?
 Probe tone ratings test
 Probe tone profiles reflect frequencies of compositions
Krumhansl, 1990
Pre-exposure probe tone ratings
1
2
3
4
5
6
7
0 1 2 3 4 5 6 7 8 9 10 11 12
Probe tone
Rating
0
200
400
600
800
1000
1200
Rating
Exposure
Frequencyofexposure
F = 220* 3n/13
Loui, Wessel & Hudson Kam, 2010, Music Perception
Post-exposure probe tone ratings
1
2
3
4
5
6
7
0 1 2 3 4 5 6 7 8 9 10 11 12
Probe tone
Rating
0
200
400
600
800
1000
1200
Rating
Exposure
Frequencyofexposure
Loui, Wessel & Hudson Kam, 2010, Music Perception
Correlating ratings with exposure
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Pre
Correlation(r)
Post
Exposure
Loui, Wessel & Hudson Kam, 2010, Music Perception
**
** p <
0.01
Participants:
 15 tone-deaf, 20 control
 Matched for age, sex, number of years of musical training
Pre-test  30-min. exposure  Post-test
 Pre- vs. Post-Exposure
 Tone-deaf vs. Controls
Probe tone test: Melody  Tone
 Probe tone profiles reflect frequencies in musical
compositions (Krumhansl 1990)
Statistical learning in tone-deaf individuals
(In progress)
Jan Iyer
Ratings: Controls
0
200
400
600
800
1000
1200
1
2
3
4
5
6
7
0 1 2 3 4 5 6 7 8 9 10 11 12
ExposureFrequency
Ratings
Probe Tone
Pre-
Exposure
Ratings
* Error bars represent
between-subject
standard errors for all
graphs
Post-
Exposure…
Ratings: Tone-deaf
0
200
400
600
800
1000
1200
1
2
3
4
5
6
7
0 1 2 3 4 5 6 7 8 9 10 11 12
ExposureFrequency
Rating
Probe Tone
Pre-Exposure
Ratings
Exposure
Post-Exposure
Ratings
-0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Control Tonedeaf
r-value(ratingsvs.exposure)
Pre-Exposure
Post-Exposure
*
Disrupted frequency learning in the
tone-deaf
*
* *
* p < 0.05
** p = 0.001
MBEA (scale score) correlates with
probe tone learning
-0.2
0
0.2
0.4
0.6
0.8
1
10 15 20 25 30
Post-ExposureProbe
ToneCorrelation(r)
MBEA 2 (Contour) Score
r=0.18
-0.2
0
0.2
0.4
0.6
0.8
1
10 15 20 25 30
Post-ExposureProbe
ToneCorrelation(r)
MBEA 3 (Interval) Score
r=0.15
-0.2
0
0.2
0.4
0.6
0.8
1
10 15 20 25 30
Post-Exposure
ProbeTone
Correlation(r)
r=0.36
(p<0.05)
Average of first three MBEA Scores
-0.2
0
0.2
0.4
0.6
0.8
1
10 20 30 40
Post-Exposure
ProbeTone
correlation(r)
r=0.45
(p<0.01)
MBEA 1 (Scale) Score
Loui & Schlaug, 2012, ANYAS
What are the neural substrates
of music learning?
STG IFG
Superior Temporal Gyrus (STG)
Inferior Frontal Gyrus (IFG)
Mandell et al, 2007; Hyde et al, 2007
Diffusion tensor imaging
Tone-deafness – regions of interest
STG IFG MTG
Loui, Alsop, & Schlaug, 2009, Journal of Neuroscience
Superior AF Inferior AF
Control Tone-deaf
Normal vs. tone-deaf AFs
Loui, Alsop, & Schlaug, 2009, Journal of Neuroscience
Tract volume reflects individual
differences in learning
Volume of right ventral arcuate
fasciculus is correlated with
generalization score, but not with
recognition score.
r = 0.53, p = 0.03
0
5
10
15
20
25
0 0.5 1 1.5
RIFG–RMTG
Tractvolume(103mm3)
Generalization
(proportion correct)
Loui, Li, & Schlaug (2011) NeuroImage
r = 0.054, n.s.
0
5
10
15
20
25
0 0.5 1
Recognition
(proportion correct)
Crucial junction of arcuate fasciculus
predicts learning behavior
Search for Fractional Anisotropy correlates of
generalization performance
FA (white matter integrity) of temporal-parietal junction
predicts individual differences in pitch-related learning.
p < 0.05 FWE
Loui, Li, & Schlaug (2011) NeuroImage
Behavioral implications of individual
differences in structural connectivity in
statistical learning
Normal Tone-deaf
Tracts from right STG
Loui, Alsop, & Schlaug, 2009, Journal of Neuroscience
Summary
Frequency ProbabilityPitch
Experiments now available for
download
http://figshare.com/articles/Bohlen_Pierc
e_scale_artificial_grammar_learning_expe
riment/75772
Also at
http://psycheloui.com/publications/down
loads
Max/MSP format
Several versions with melodies included
Acknowledgements
Gottfried Schlaug
BIDMC, HMS
Music and Neuroimaging Lab
(http://musicianbrain.com)
Katy Abel
Rob Ellis
Anja Hohmann
Jan Iyer
Charles Li
Berit Lindau
Christoph Mathys
Sang-Hee Min
Matthew Sachs
Catherine Wan
Jasmine Wang
Anna Zamm
Xin Zheng
David Alsop
BIDMC, HMS
Carol Krumhansl
Cornell University
University of California at Berkeley
David Wessel
Center for New Music & Audio
Technologies
Erv Hafter
Auditory Perception Lab
Carla Hudson Kam
Language & Learning Lab
Bob Knight
Helen Wills Neuroscience Institute
Take-home
 Much of what we know and love about music is acquired
via statistical sensitivity to the frequency and probability
of occurrence of events in the auditory environment.
 This statistical learning mechanism relies on intact white
matter connectivity between temporal and frontal lobe
regions, and may subserve multiple auditory-motor
functions including language as well as music.

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Behavioral and DTI Studies on Normal and Impaired Learning of Musical Structure

  • 1. + Behavioral and DTI Studies on Normal and Impaired Learning of Musical Structure Psyche Loui Wesleyan University CogSci 2013 August 1, 2013
  • 2. The world knows and loves music
  • 3. Tone-deafness: a disorder of pitch perception and action  Congenital amusia  Inability to sing in tune  Incidence: 4 – 17%  Montreal Battery of Evaluation for Amusia  Inability to discriminate pitch >1 semitone threshold (musicianbrain.com/pitchtes t)
  • 4. What is the source of musical knowledge? Frequency Probability Pitch Harmony Melody Perception
  • 5. Existing musical systems confound learning with memory Test learning with new frequencies & probabilities New musical system Tone-deafness We need a system to assess implicit music learning
  • 6. Bohlen-Pierce A new tuning system – the BP scale F = 220 * 2 n/12 F = 220 * 3 n/13 200 300 400 500 600 700 0 1 2 3 4 5 6 7 8 9 10 11 12 13 increments (n) frequency(Hz) Western Loui, Wessel, & Hudson Kam, 2010, Music Perception
  • 7. The BP scale can form chords 200 300 400 500 600 700 0 1 2 3 4 5 6 7 8 9 10 11 12 13 increments (n) frequency(Hz) F = 220 * 3 n/13 Bohlen-Pierce 3 : 5 : 7 Loui, Wessel, & Hudson Kam, 2010, Music Perception
  • 8. Composing in the Bohlen-Pierce scale 10 7 10 10 6 4 7 6 0 0 3 0 F = 220 * 3 n/13 Krumhansl, 1987; Loui, Wessel, & Hudson Kam, 2010, Music Perception
  • 9. Composing melody from harmony – applying a finite-state grammar 10 7 10 10 6 4 7 6 0 0 3 0 Loui, Wessel, & Hudson Kam, 2010, Music Perception
  • 10. Melody: 6  4  7  7  7  6  10  10 10 7 10 10 6 4 7 6 0 0 3 0 Composing melody from harmony – applying a finite-state grammar Loui, Wessel, & Hudson Kam, 2010, Music Perception. 10
  • 11. Can we learn the B-P scale? General design of behavioral studies: 1. PRE-TEST  assess baseline 2. EXPOSURE to melodies in one grammar  ~30 minutes 3. POST-TESTS  assess learning
  • 12. Learning a musical system: Probability sensitivity  Can we remember old melodies? 2-AFC test of recognition  Can we learn new melodies? 2-AFC test of generalization
  • 13. Double dissociation between learning and memory No. of melodies 12740100No. of repetitions 5 10 15 400 40% 50% 60% 70% 80% 90% 100% PercentCorrect 0 0.2 0.4 0.6 0.8 1 1.2 Differenceinrating (familiar-unfamiliar) recognition generalization Loui & Wessel, 2008 Loui, Wessel & Hudson Kam, 2010
  • 14. Disrupting harmony – the forced octave scale 0 1 2 3 4 5 6 7 8 9 10 11 12 13 Increments (n) 0 1 2 3 4 5 6 7 8 9 10 11 12 13 200 300 400 500 600 700 Frequency(Hz) 200 300 400 500 600 700 Western scale: F = 220 * 2 n/12 B-P scale: F = 220 * 3 n/13 Forced-octave scale: F = 220 * 2 n/13 3 : 5 : 7 3:4.13:5.11 Loui, 2012, TopiCS Generalization
  • 15. Disrupting melody – eliminating select transitional probabilities 10 10 7 10 6 7 4 6 0 3 0 0 Loui, 2012, TopiCS Generalization Mechanisms enabling generalization in musical AGL depend on transitional probabilities.
  • 16. Learning a new musical system: Frequency sensitivity  Can we learn to expect frequent tones?  Probe tone ratings test  Probe tone profiles reflect frequencies of compositions Krumhansl, 1990
  • 17. Pre-exposure probe tone ratings 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 8 9 10 11 12 Probe tone Rating 0 200 400 600 800 1000 1200 Rating Exposure Frequencyofexposure F = 220* 3n/13 Loui, Wessel & Hudson Kam, 2010, Music Perception
  • 18. Post-exposure probe tone ratings 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 8 9 10 11 12 Probe tone Rating 0 200 400 600 800 1000 1200 Rating Exposure Frequencyofexposure Loui, Wessel & Hudson Kam, 2010, Music Perception
  • 19. Correlating ratings with exposure 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Pre Correlation(r) Post Exposure Loui, Wessel & Hudson Kam, 2010, Music Perception ** ** p < 0.01
  • 20. Participants:  15 tone-deaf, 20 control  Matched for age, sex, number of years of musical training Pre-test  30-min. exposure  Post-test  Pre- vs. Post-Exposure  Tone-deaf vs. Controls Probe tone test: Melody  Tone  Probe tone profiles reflect frequencies in musical compositions (Krumhansl 1990) Statistical learning in tone-deaf individuals (In progress) Jan Iyer
  • 21. Ratings: Controls 0 200 400 600 800 1000 1200 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 8 9 10 11 12 ExposureFrequency Ratings Probe Tone Pre- Exposure Ratings * Error bars represent between-subject standard errors for all graphs Post- Exposure…
  • 22. Ratings: Tone-deaf 0 200 400 600 800 1000 1200 1 2 3 4 5 6 7 0 1 2 3 4 5 6 7 8 9 10 11 12 ExposureFrequency Rating Probe Tone Pre-Exposure Ratings Exposure Post-Exposure Ratings
  • 24. MBEA (scale score) correlates with probe tone learning -0.2 0 0.2 0.4 0.6 0.8 1 10 15 20 25 30 Post-ExposureProbe ToneCorrelation(r) MBEA 2 (Contour) Score r=0.18 -0.2 0 0.2 0.4 0.6 0.8 1 10 15 20 25 30 Post-ExposureProbe ToneCorrelation(r) MBEA 3 (Interval) Score r=0.15 -0.2 0 0.2 0.4 0.6 0.8 1 10 15 20 25 30 Post-Exposure ProbeTone Correlation(r) r=0.36 (p<0.05) Average of first three MBEA Scores -0.2 0 0.2 0.4 0.6 0.8 1 10 20 30 40 Post-Exposure ProbeTone correlation(r) r=0.45 (p<0.01) MBEA 1 (Scale) Score Loui & Schlaug, 2012, ANYAS
  • 25. What are the neural substrates of music learning? STG IFG Superior Temporal Gyrus (STG) Inferior Frontal Gyrus (IFG) Mandell et al, 2007; Hyde et al, 2007
  • 27. Tone-deafness – regions of interest STG IFG MTG Loui, Alsop, & Schlaug, 2009, Journal of Neuroscience Superior AF Inferior AF
  • 28. Control Tone-deaf Normal vs. tone-deaf AFs Loui, Alsop, & Schlaug, 2009, Journal of Neuroscience
  • 29. Tract volume reflects individual differences in learning Volume of right ventral arcuate fasciculus is correlated with generalization score, but not with recognition score. r = 0.53, p = 0.03 0 5 10 15 20 25 0 0.5 1 1.5 RIFG–RMTG Tractvolume(103mm3) Generalization (proportion correct) Loui, Li, & Schlaug (2011) NeuroImage r = 0.054, n.s. 0 5 10 15 20 25 0 0.5 1 Recognition (proportion correct)
  • 30. Crucial junction of arcuate fasciculus predicts learning behavior Search for Fractional Anisotropy correlates of generalization performance FA (white matter integrity) of temporal-parietal junction predicts individual differences in pitch-related learning. p < 0.05 FWE Loui, Li, & Schlaug (2011) NeuroImage
  • 31. Behavioral implications of individual differences in structural connectivity in statistical learning Normal Tone-deaf Tracts from right STG Loui, Alsop, & Schlaug, 2009, Journal of Neuroscience
  • 33. Experiments now available for download http://figshare.com/articles/Bohlen_Pierc e_scale_artificial_grammar_learning_expe riment/75772 Also at http://psycheloui.com/publications/down loads Max/MSP format Several versions with melodies included
  • 34. Acknowledgements Gottfried Schlaug BIDMC, HMS Music and Neuroimaging Lab (http://musicianbrain.com) Katy Abel Rob Ellis Anja Hohmann Jan Iyer Charles Li Berit Lindau Christoph Mathys Sang-Hee Min Matthew Sachs Catherine Wan Jasmine Wang Anna Zamm Xin Zheng David Alsop BIDMC, HMS Carol Krumhansl Cornell University University of California at Berkeley David Wessel Center for New Music & Audio Technologies Erv Hafter Auditory Perception Lab Carla Hudson Kam Language & Learning Lab Bob Knight Helen Wills Neuroscience Institute
  • 35. Take-home  Much of what we know and love about music is acquired via statistical sensitivity to the frequency and probability of occurrence of events in the auditory environment.  This statistical learning mechanism relies on intact white matter connectivity between temporal and frontal lobe regions, and may subserve multiple auditory-motor functions including language as well as music.

Editor's Notes

  1. I want to know how this happens.
  2. TONE-DEAFNESS: A DISORDER OF PITCH PERCEPTION – AND ACTIONCongenital amusia(Grant-Allen, 1878)Incidence: 4 – 17%Inability to sing in tuneInability to discriminate pitch &gt;1 semitone threshold
  3. These questions really get at the core of music cognition and neuroscience more generally, which is what are the core sources of knowledge in music? What exactly needs to be learned in order to be musical? Many here have asked that question and I think we can all agree that pitch is a fundamental source of musical information. So, part of musical competence is the ability to perceive pitch. And we already know that pitch perception is disrupted at least in some tone-deaf individuals. But we also know that pitches don’t exist in isolation. Pitches are strung together to form musical structure. Pitches that are important in a piece occur at a higher frequency, and this gives rise to harmony and tonality. Pitches that are highly probable given other pitches gives rise to melodic structures such as motifs. So to understand musical structure, it’s really the frequencies and probabilities, and how the brain learns to compute them implicitly, that we need to try to understand.
  4. So how do we go about trying to understand how the brain learns frequencies and probabilities of pitches? Well, as we said, most people have already had so much exposure to Western music that even people without musical training show implicit knowledge of the frequencies and probabilities of Western musical sounds. What we really need is a new system of pitches with new frequencies and probabilities that are different from Western music. And this would give us a high degree of experimental control, so that we can systematically manipulate what frequencies and probabilities they get exposed to. To that end, in the past few years we have developed an “alien” or a “Martian” musical scale based on an alternative musical system known as the Bohlen-Pierce scale. Then bycomparing tone-deaf people and matched controls in the way they learn the statistics of music, we can really get at the degree to which different types of musical knowledge might be learnable.
  5. Previous studies from our lab and others have found differences between tone-deaf people and controls in both the structure and the function of the brain. In particular, these brain differences center around two brain regions: the superior temporal gyrus (STG) and inferior frontal gyrus (IFG).
  6. Diffusion of water Infer connectivity about biological matterArcuate fasciculus
  7. Brain-behavioral correlations:If we believe that the right superior AF is predictive of tone-deafness, then people who are more tone-deaf should have less of that connection…What about that inferior branch of the right AF? Superior AF correlates with perceptual thresholdInferior AF correlates with perception-production mismatch
  8. N = 16, high-resolution DTI and learning and memory tasks
  9. Synaptic pruning or abnormal neuronalmigrationInsights into a pitch disability: perception and productionIf we know that this bundle of connections is directly related to language ability, then mapping its relationship with music is revolutionizing how we think about language and its disorders, and how we can rehabilitate them.
  10. Music requires learning from multiple sources of knowledgeMuch of what we know and love about music is acquired via statistical sensitivity to the frequency and probability of occurrence of events in the auditory environment. This statistical learning mechanism relies on intact white matter connectivity between temporal and frontal lobe regions, and may subserve multiple auditory-motor functions including language as well as music. By combining the approaches of an artificial musical grammar with musical disorders, the hope is to understand nature vs. nurture interactions in music in the brain.