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Philadelphia Teen's Study Finds Mozart's Melodies Least Predictable

4 hours ago

Linus Chen-Plotkin, 18, analysed 600+ classical works for a Regeneron project and found Mozart's melodies broke patterns faster than his peers.

An 18-year-old from Philadelphia analysed more than 600 classical compositions and found that Wolfgang Amadeus Mozart's melodies are harder to predict than those of Joseph Haydn, Ludwig van Beethoven and Franz Schubert. Linus Chen-Plotkin built the study for the Regeneron Science Talent Search, applying statistical tools to a question musicologists have argued over for centuries.

His project, titled "Predictability and Statistical Memory in Classical Sonatas and Quartets," measured how much earlier notes in a piece influence the notes that follow. Chen-Plotkin designed three statistical tests to track what he calls a melody's "memory" - the degree to which one note depends on the notes before it.

High memory means earlier phrases keep shaping what comes next, making the music more predictable. Low memory means a composer changes direction quickly, keeping listeners slightly off-balance. Chen-Plotkin ran the tests across more than 600 piano sonatas and string quartets by the four composers.

Mozart's music returned shorter memory across all three tests. His melodies broke their patterns faster, making them less predictable than those of his contemporaries. The finding suggests Mozart followed classical form while repeatedly slipping something unexpected past the listener's ear.

The result offers a measurable angle on a debate that has long relied on words like "genius" and "innovation" without defining them. Chen-Plotkin's tests give a way to quantify what set Mozart apart: not predictability, but a controlled unpredictability that stayed within the rules of his era.

From wrestling mat to research desk

Chen-Plotkin is the son of Joshua Plotkin and Alice Chen-Plotkin and a student at the Germantown Friends School in Philadelphia. He is a varsity wrestler and an advanced Mandarin student, and he is not a trained conservatory musician. Instead, he approached classical music as a dataset, treating centuries of musical tradition as raw material for mathematical analysis.

He argues that quantitative methods can sharpen fields that have traditionally been studied through interpretation alone, including music theory, art history and literature. Music, however, is not foreign to him. He sings and plays blues guitar at The Twisted Tail, a bourbon bar, in his spare time. He is a former member and soloist of the Philadelphia Boys Choir & Chorale, and he holds a licence as an amateur radio operator.

Applying the method to Chaucer

Chen-Plotkin plans to extend the approach beyond music. He is working with a Johns Hopkins professor to analyse the grammatical rules of rhyme and metre in the medieval poetry of Geoffrey Chaucer. He intends to use the same statistical method on poems from different centuries.

The underlying question stays largely the same across both projects: whether it is possible to measure what makes great work great. By turning notes and verse into data, Chen-Plotkin is testing whether the qualities long described as artistic excellence can be counted rather than only felt.

Mozart, Linus Chen-Plotkin, Regeneron Science Talent Search, melody predictability, classical music study, statistical memory, Germantown Friends School, Philadelphia student

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