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Attention and the
refinement of auditory
expectations
Psyche Loui
Wesleyan University
Hafterfest at ASA
December 5, 2013
Attention and the refinement of auditory expectations: Hafter festschrift talk
The Principles of Psychology

William James
(1842-1910)

Every one knows what attention is. It is the
taking possession by the mind of one out
of what seem several simultaneously
possible objects or trains of thought. It
implies withdrawal from some things in
order to deal effectively with others, and is
a condition which has a real opposite in the
confused, dazed, scatterbrained state
which in French is called distraction, and
Zerstreutheit in German.

Auditory attention: the listener's ability to
extract relevant features of the auditory
scene (Hafter et al., 2007)
Attention: Global vs. local stimuli
Attention and the refinement of musical
expectations
High expectation

Local vs. Global attention:
Local: pick out top line
Global: overall preference
Position 3 deviant:
Medium expectation
Training effects:
Musical training (5+ years)
Vs.
No musical training

Position 5 deviant:
Low expectation
Global sensitivity to expectation:
Independent of musical training

Loui et al, (2007) Perception & Psychophysics
Local sensitivity to expectation:
Effects of musical training

RTs reveal Expectation * Training interaction
Training refines expectation for local, not global attention
Loui et al, (2007) Perception & Psychophysics
Attention and the refinement of auditory expectations: Hafter festschrift talk
What is the source of musical
knowledge?
Harmony
Pitch

Melody
We need a system to assess implicit
music learning
Existing musical systems confound learning with memory
Test learning with new frequencies & probabilities

New musical system
A new tuning system  the BP scale
Bohlen-Pierce

Western

700

F = 220 * 3 n/13
frequency (Hz)

600
500
400

F = 220 * 2 n/12
300
200
0

1

2

3

4

5

6

7

8

9

10

11

12

13

increments (n)
Loui et al, 2010, Music Perception
A new tuning system  the BP scale
Bohlen-Pierce
700

F = 220 * 3 n/13
3:5:7

frequency (Hz)

600
500
400
300
200
0

1

2

3

4

5

6

7

8

increments (n)

9

10

11

12

13
Composing in the Bohlen-Pierce scale
F = 220 * 3 n/13
10
6
0

7
4
0

10
7
3

10
6
0
Composing melody from harmony 
applying a finite-state grammar

10

7

10

10

6

4

7

6

0

0

3

0
Composing melody from harmony 
applying a finite-state grammar

10

7

10

10

6

4

7

6

0

0

3

0

Melody: 6  4  7  7  7  6  10  10
Learning a musical system:
Probability sensitivity
Pre-test  Exposure  Post-test
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
recognition
generalization
100%

1.2

80%

0.8

70%

0.6

60%

0.4

50%

0.2

40%

Percent Correct

1

0

No. of melodies

5

10

15

400

No. of repetitions

100

40

27

Difference in rating
(familiar - unfamiliar)

90%

1
Loui & Wessel, 2008, Musicae Scientiae
Loui et al, 2010, Music Perception
Learning a new musical system:
Frequency sensitivity
 Can we learn to expect frequent tones?
 Probe tone ratings test
 Rate how well the last tone fit the preceding melody

Krumhansl, 1990
Pre-exposure probe tone ratings
6

1000

5
Rating

1200

800

4

600

3

400

2

Rating
Exposure

200

1

Frequency of exposure

7

0
0

1

2

3

4

5

6

7

Probe tone

8

9

10

11

12

F = 220* 3n/13

Loui, Wessel & Hudson Kam, 2010, Music Perception
Post-exposure probe tone ratings

6

1000

5
Rating

1200

800

4

600

3

400

2

Rating
Exposure

200

1

Frequency of exposure

7

0
0

1

2

3

4

5

6

7

8

9

10

11

12

Probe tone

Loui, Wessel & Hudson Kam, 2010, Music Perception
Correlations improve after exposure
**

1
0.9
0.8

Correlation (r)

0.7
0.6
0.5
0.4

0.3
0.2
0.1
0
Pre

Post
Exposure

** p < 0.01

Loui, Wessel & Hudson Kam, 2010, Music Perception
Structural and functional neural signatures of
new music learning
Fz

Rapid statistical learning of
new musical system over 1
hour (ERP).

Before
Learning

[袖V]
-2
0
2
0

Tract volume

After
Learning

[ms]

0

Fz

Loui et al, 2009, Journal of Neuroscience

500

500

[ms]

[袖V]

-2
0
2

Right ventral arcuate
fasciculus reflects
individual differences in
learning (DTI).
Learning
performance

Loui et al, 2011, NeuroImage
Conclusions
 Long-term training refines attention towards expected
sounds in one's culture.

 Refinement of expectation entails sensitivity to
frequency and probability of occurrence of events.

 This statistical learning mechanism may subserve
multiple auditory-motor functions including language as
well as music.
Attention and the refinement of auditory expectations: Hafter festschrift talk
Acknowledgements
Wesleyan University
Music, Imaging, and Neural Dynamics
(MIND) Lab
Lauren Seo
Katy Abel
Berit Lindau
Charles Li

University of California at Berkeley
David Wessel
Center for New Music & Audio Technologies

Erv Hafter
Auditory Perception Lab

Bob Knight
Helen Wills Neuroscience Institute

Harvard Medical School
Gottfried Schlaug
David Alsop

Frank Guenther

Music and Neuroimaging Lab

Carla Hudson Kam

Ethan Pani
Jan Iyer
Charles Li
Matt Sachs
Anna Zamm
Xin Zheng

University of British Columbia

Boston University

Ellen Winner

NIDCD

Boston College

Carol Krumhansl
Cornell University

Marty Woldorff
Duke University

More Related Content

Attention and the refinement of auditory expectations: Hafter festschrift talk

  • 1. Attention and the refinement of auditory expectations Psyche Loui Wesleyan University Hafterfest at ASA December 5, 2013
  • 3. The Principles of Psychology William James (1842-1910) Every one knows what attention is. It is the taking possession by the mind of one out of what seem several simultaneously possible objects or trains of thought. It implies withdrawal from some things in order to deal effectively with others, and is a condition which has a real opposite in the confused, dazed, scatterbrained state which in French is called distraction, and Zerstreutheit in German. Auditory attention: the listener's ability to extract relevant features of the auditory scene (Hafter et al., 2007)
  • 4. Attention: Global vs. local stimuli
  • 5. Attention and the refinement of musical expectations High expectation Local vs. Global attention: Local: pick out top line Global: overall preference Position 3 deviant: Medium expectation Training effects: Musical training (5+ years) Vs. No musical training Position 5 deviant: Low expectation
  • 6. Global sensitivity to expectation: Independent of musical training Loui et al, (2007) Perception & Psychophysics
  • 7. Local sensitivity to expectation: Effects of musical training RTs reveal Expectation * Training interaction Training refines expectation for local, not global attention Loui et al, (2007) Perception & Psychophysics
  • 9. What is the source of musical knowledge? Harmony Pitch Melody
  • 10. We need a system to assess implicit music learning Existing musical systems confound learning with memory Test learning with new frequencies & probabilities New musical system
  • 11. A new tuning system the BP scale Bohlen-Pierce Western 700 F = 220 * 3 n/13 frequency (Hz) 600 500 400 F = 220 * 2 n/12 300 200 0 1 2 3 4 5 6 7 8 9 10 11 12 13 increments (n) Loui et al, 2010, Music Perception
  • 12. A new tuning system the BP scale Bohlen-Pierce 700 F = 220 * 3 n/13 3:5:7 frequency (Hz) 600 500 400 300 200 0 1 2 3 4 5 6 7 8 increments (n) 9 10 11 12 13
  • 13. Composing in the Bohlen-Pierce scale F = 220 * 3 n/13 10 6 0 7 4 0 10 7 3 10 6 0
  • 14. Composing melody from harmony applying a finite-state grammar 10 7 10 10 6 4 7 6 0 0 3 0
  • 15. Composing melody from harmony applying a finite-state grammar 10 7 10 10 6 4 7 6 0 0 3 0 Melody: 6 4 7 7 7 6 10 10
  • 16. Learning a musical system: Probability sensitivity Pre-test Exposure Post-test Can we remember old melodies? 2-AFC test of recognition Can we learn new melodies? 2-AFC test of generalization
  • 17. Double dissociation between learning and memory recognition generalization 100% 1.2 80% 0.8 70% 0.6 60% 0.4 50% 0.2 40% Percent Correct 1 0 No. of melodies 5 10 15 400 No. of repetitions 100 40 27 Difference in rating (familiar - unfamiliar) 90% 1 Loui & Wessel, 2008, Musicae Scientiae Loui et al, 2010, Music Perception
  • 18. Learning a new musical system: Frequency sensitivity Can we learn to expect frequent tones? Probe tone ratings test Rate how well the last tone fit the preceding melody Krumhansl, 1990
  • 19. Pre-exposure probe tone ratings 6 1000 5 Rating 1200 800 4 600 3 400 2 Rating Exposure 200 1 Frequency of exposure 7 0 0 1 2 3 4 5 6 7 Probe tone 8 9 10 11 12 F = 220* 3n/13 Loui, Wessel & Hudson Kam, 2010, Music Perception
  • 20. Post-exposure probe tone ratings 6 1000 5 Rating 1200 800 4 600 3 400 2 Rating Exposure 200 1 Frequency of exposure 7 0 0 1 2 3 4 5 6 7 8 9 10 11 12 Probe tone Loui, Wessel & Hudson Kam, 2010, Music Perception
  • 21. Correlations improve after exposure ** 1 0.9 0.8 Correlation (r) 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 Pre Post Exposure ** p < 0.01 Loui, Wessel & Hudson Kam, 2010, Music Perception
  • 22. Structural and functional neural signatures of new music learning Fz Rapid statistical learning of new musical system over 1 hour (ERP). Before Learning [袖V] -2 0 2 0 Tract volume After Learning [ms] 0 Fz Loui et al, 2009, Journal of Neuroscience 500 500 [ms] [袖V] -2 0 2 Right ventral arcuate fasciculus reflects individual differences in learning (DTI). Learning performance Loui et al, 2011, NeuroImage
  • 23. Conclusions Long-term training refines attention towards expected sounds in one's culture. Refinement of expectation entails sensitivity to frequency and probability of occurrence of events. This statistical learning mechanism may subserve multiple auditory-motor functions including language as well as music.
  • 25. Acknowledgements Wesleyan University Music, Imaging, and Neural Dynamics (MIND) Lab Lauren Seo Katy Abel Berit Lindau Charles Li University of California at Berkeley David Wessel Center for New Music & Audio Technologies Erv Hafter Auditory Perception Lab Bob Knight Helen Wills Neuroscience Institute Harvard Medical School Gottfried Schlaug David Alsop Frank Guenther Music and Neuroimaging Lab Carla Hudson Kam Ethan Pani Jan Iyer Charles Li Matt Sachs Anna Zamm Xin Zheng University of British Columbia Boston University Ellen Winner NIDCD Boston College Carol Krumhansl Cornell University Marty Woldorff Duke University

Editor's Notes

  • #3: Great great great grandfather
  • #5: Complex auditory stimuliAnalytical vs. synthetic stimuli
  • #6: Analytic vs. syntheticlistening
  • #8: Synthetic vs. analytical
  • #10: 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. But we also know that pitches dont 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, its really the frequencies and probabilities, and how the brain learns to compute them implicitly, that we need to try to understand.
  • #11: 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.
  • #23: DTI and learning and memory tasks
  • #24: Bill: Ear Club family