candrew123.bsky.social
Postdoctoral researcher at NYU, working on computational cognitive neuroscience, audition (music and speech), and real-world communication. đčđŒđšđŠđșđž
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There is an excellent cross-cultural study on this topic by @norijacoby.bsky.social. A lay summary of the paper can be found here: www.aesthetics.mpg.de/en/research/...
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Thanks for your comment. Yes there are several recent studies suggesting that chroma is not really an innate or universal property of pitch perception. Our study cannot answer this question, but we indeed found that the effect of chroma is much weaker than height.
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In short: By combining machine learning and MEG, we show how the brainâs dynamic pitch representation echoes ideas proposed over 100 years ago. Feels like completing a full circle in music cognitive neuroscience! Huge thanks to my collaborators! End/n
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The helix model reflects the idea that pitches separated by an octave (e.g., the repeating piano keys) are perceived as inherently similar. This concept was first explored in the early 1900s by GĂ©za RĂ©vĂ©sz, laying the groundwork for modern music cognition! đ§ đč 6/n
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The brain doesnât process pitch in an unstructured way. Typically, it represents pitches in a mostly linear structureâthink piano keyboard layout. BUTâjust 0.3 seconds after hearing a sound, something wild happens: the brain briefly represents pitch in a helix-like structure! 5/n
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This animation shows the reconstruction of how the brain dynamically represents musical pitches. The pitches that are closer in space are perceived as more similar at a given moment. 4/n
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We used machine learning to decode how the brain represents musical pitches during an #MEG scan. Our model reconstructed how the brain represents the similarity between different pitches and how this representation changes over time. 3/n
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Why does pitch matter? Itâs essential not just for music, but for speech perception & sound segregation too! Understanding how our brain dynamically encodes pitch is a major research in auditory cognitive neuroscience. 2/n
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In short: By combining machine learning and MEG, we show how the brainâs dynamic pitch representation echoes ideas proposed over 100 years ago. Feels like completing a full circle in music cognitive neuroscience! Huge thanks to my collaborators! End/n
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The helix model reflects the idea that pitches separated by an octave (e.g., the repeating piano keys) are perceived as inherently similar. This concept was first explored in the early 1900s by GĂ©za RĂ©vĂ©sz, laying the groundwork for modern music cognition! đ§ đč 6/n
comment in response to
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The brain doesnât process pitch in an unstructured way. Typically, it represents pitches in a mostly linear structureâthink piano keyboard layout. BUTâjust 0.3 seconds after hearing a sound, something wild happens: the brain briefly represents pitch in a helix-like structure! 5/n
comment in response to
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This animation shows the reconstruction of how the brain dynamically represents musical pitches. The pitches that are closer in space are perceived as more similar at a given moment. 4/n
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We used machine learning to decode how the brain represents musical pitches during an #MEG scan. Our model reconstructed how the brain represents the similarity between different pitches and how this representation changes over time. 3/n
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Why does pitch matter? Itâs essential not just for music, but for speech perception & sound segregation too! Understanding how our brain dynamically encodes pitch is a major research in auditory cognitive neuroscience. 2/n
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Link to the Academic Minute: academicminute.org/2024/10/andr...
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Iâm incredibly grateful for these opportunities to share my research on using computational and ML approaches to understand #music, #speech, and human communication. Moments like these remind me how rewarding this journey has been and how much support and mentorship Iâve received.