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Attention and the
refinement of auditory
expectationsPsyche Loui
Wesleyan University
Hafterfest at ASA
December 5, 2013
The Principles of Psychology
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.William James
(1842-1910)
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
Position 3 deviant:
Medium expectation
Position 5 deviant:
Low expectation
Local vs. Global attention:
Local: pick out top line
Global: overall preference
Training effects:
Musical training (5+ years)
Vs.
No musical training
Global sensitivity to expectation:
Independent of musical training
Loui et al, (2007) Perception & Psychophysics
Local sensitivity to expectation:
Effects of musical training
RT’s reveal Expectation * Training interaction
Training refines expectation for local, not global attention
Loui et al, (2007) Perception & Psychophysics
What is the source of musical
knowledge?
PitchHarmony
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
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 et al, 2010, Music Perception
A new tuning system – the BP scale
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
Composing in the Bohlen-Pierce scale
10 7 10 10
6 4 7 6
0 0 3 0
F = 220 * 3 n/13
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
10 7 10 10
6 4 7 6
0 0 3 0
Composing melody from harmony –
applying a finite-state grammar
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
No. of melodies
12740100No. of repetitions
5 10 15 400
40%
50%
60%
70%
80%
90%
100%
Pe
rce
nt C
orr
ect
0
0.2
0.4
0.6
0.8
1
1.2
Diffe
ren
ce
in ra
ting
(fam
iliar -
un
fam
iliar)
recognition
generalization
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
1
2
3
4
5
6
7
0 1 2 3 4 5 6 7 8 9 10 11 12
Probe tone
Ra
tin
g
0
200
400
600
800
1000
1200
Rating
Exposure
Fre
quency o
f exposure
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
Ra
tin
g
0
200
400
600
800
1000
1200
Rating
Exposure
Fre
qu
en
cy o
f exp
osu
re
Loui, Wessel & Hudson Kam, 2010, Music Perception
Correlations improve after exposure
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Pre
Co
rre
latio
n (
r)
Post
Exposure
Loui, Wessel & Hudson Kam, 2010, Music Perception
**
** p < 0.01
Structural and functional neural signatures of
new music learning
Right ventral arcuate
fasciculus reflects
individual differences in
learning (DTI).
Trac
t v
olu
me
Learning performance
Loui et al, 2011, NeuroImage
2
0
-2
[µV]
0 500 [ms]
2
0
-2
[µV]
0 500 [ms]
Fz
Fz
Before
Learning
After
Learning
Loui et al, 2009, Journal of Neuroscience
Rapid statistical learning of
new musical system over 1
hour (ERP).
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.
Acknowledgements
Wesleyan University
Music, Imaging, and Neural Dynamics (MIND) Lab
Lauren Seo
Katy Abel
Berit Lindau
Charles Li
Harvard Medical School
Gottfried Schlaug
David Alsop
Music and Neuroimaging Lab
Ethan Pani
Jan Iyer
Charles Li
Matt Sachs
Anna Zamm
Xin Zheng
University of California at Berkeley
David Wessel
Center for New Music & Audio Technologies
Erv Hafter
Auditory Perception Lab
Bob Knight
Helen Wills Neuroscience Institute
Frank GuentherBoston University
Carla Hudson Kam
University of British Columbia
Ellen WinnerBoston College
Carol Krumhansl
Cornell University
Marty Woldorff
Duke University
NIDCD