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

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

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

Behavioral and DTI Studies on Normal and Impaired Learning of Musical Structure
A talk for CogSci 2013 in Berlin, August 1, 2013
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  • I want to know how this happens.
  • TONE-DEAFNESS: A DISORDER OF PITCH PERCEPTION – AND ACTIONCongenital amusia(Grant-Allen, 1878)Incidence: 4 – 17%Inability to sing in tuneInability to discriminate pitch >1 semitone threshold
  • 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.
  • 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.
  • 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).
  • Diffusion of water Infer connectivity about biological matterArcuate fasciculus
  • 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
  • N = 16, high-resolution DTI and learning and memory tasks
  • 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.
  • 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.

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

  • + 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.