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Attention and the refinement of auditory expectations: Hafter festschrift talk

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Attention and the refinement of auditory expectations: Symposium talk in honor of Erv Hafter at Acoustical Society of America in San Francisco, December 5, 2013
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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

Attention and the refinement of auditory expectationsPsyche LouiWesleyan University

Hafterfest at ASADecember 5, 2013

Great great great grandfather2The Principles of PsychologyEvery 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

Complex auditory stimuliAnalytical vs. synthetic stimuli4Attention and the refinement of musical expectations

High expectationPosition 3 deviant:Medium expectationPosition 5 deviant:Low expectationLocal vs. Global attention:Local: pick out top lineGlobal: overall preferenceTraining effects:Musical training (5+ years)Vs.No musical training

Analytic vs. synthetic listening5Global sensitivity to expectation:Independent of musical training

Loui et al, (2007) Perception & Psychophysics6Local sensitivity to expectation: Effects of musical training

RTs reveal Expectation * Training interactionTraining refines expectation for local, not global attentionLoui et al, (2007) Perception & PsychophysicsSynthetic vs. analytical7

What is the source of musical knowledge?FrequencyProbabilityPitchHarmonyMelodyPerceptionI 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.

9We need a system to assess implicit music learningExisting musical systems confound learning with memoryTest learning with new frequencies & probabilities

New musical systemSo 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 by comparing 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. 10Bohlen-PierceA new tuning system the BP scaleF = 220 * 2 n/12F = 220 * 3 n/13 200300400500600700012345678910111213increments (n)frequency (Hz)Western

Loui et al, 2010, Music Perception

11A new tuning system the BP scale200300400500600700012345678910111213increments (n)frequency (Hz)F = 220 * 3 n/13Bohlen-Pierce3 : 5 : 712Composing in the Bohlen-Pierce scale107101064760030F = 220 * 3 n/1313Composing melody from harmony applying a finite-state grammar 10710106476003014Melody: 6 4 7 7 7 6 10 10 107101064760030Composing melody from harmony applying a finite-state grammar

15Learning a musical system: Probability sensitivityPre-test Exposure Post-test Can we remember old melodies?2-AFC test of recognition Can we learn new melodies?2-AFC test of generalization

16Double dissociation between learning and memoryNo. of melodies12740100No. of repetitions5101540040%50%60%70%80%90%100%Percent Correct00. in rating (familiar - unfamiliar)recognitiongeneralizationLoui & Wessel, 2008, Musicae ScientiaeLoui et al, 2010, Music Perception17Learning a new musical system: Frequency sensitivityCan we learn to expect frequent tones?Probe tone ratings test Rate how well the last tone fit the preceding melody

Krumhansl, 1990

18Pre-exposure probe tone ratings12345670123456789101112Probe toneRating020040060080010001200RatingExposureFrequency of exposureF = 220* 3n/13Loui, Wessel & Hudson Kam, 2010, Music Perception19Post-exposure probe tone ratings12345670123456789101112Probe toneRating020040060080010001200RatingExposureFrequency of exposureLoui, Wessel & Hudson Kam, 2010, Music Perception20Correlations improve after exposure00. (r)PostExposureLoui, Wessel & Hudson Kam, 2010, Music Perception**** p < 0.0121Within 30 minutes of exposure to new musical system, acquired sensitivity to statistical structure of their auditory environment. Improvement of correlations did not interact with musical training. Both musicians and non-musicians could do this. ConclusionsLong-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.

Bill: Ear Club family23

AcknowledgementsWesleyan UniversityMusic, Imaging, and Neural Dynamics (MIND) LabLauren SeoKaty AbelBerit LindauCharles Li

Harvard Medical SchoolGottfried SchlaugDavid Alsop

Music and Neuroimaging LabEthan PaniJan IyerCharles LiMatt SachsAnna ZammXin Zheng

University of California at BerkeleyDavid WesselCenter for New Music & Audio TechnologiesErv Hafter Auditory Perception LabBob KnightHelen Wills Neuroscience Institute

Frank GuentherBoston University

Carla Hudson KamUniversity of British Columbia

Ellen WinnerBoston College

Carol KrumhanslCornell University

Marty WoldorffDuke University


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