AUTHOR=Drai-Zerbib Véronique , Ansart Manon , Grenot Clément , Poulin-Charronnat Bénédicte , Perra Joris , Baccino Thierry 

TITLE=Classifying musical reading expertise by eye-movement analysis using machine learning

JOURNAL=Frontiers in Cognition

VOLUME=Volume 3 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/cognition/articles/10.3389/fcogn.2024.1417011

DOI=10.3389/fcogn.2024.1417011

ISSN=2813-4532

ABSTRACT=Music reading is the key to literacy for musicians in the Western music tradition. This highlevel activity requires an efficient extraction of the visual information from the score to the current needs of the execution. Differences in eye movements between expert and nonexpert musicians during music reading have been shown. The present study goes further, using a machine learning approach to classify musicians according to their level of expertise in analyzing their eye movements and performance during sight-reading. We used a Support Vector Machine (SVM) technique to 1) investigate whether the underlying expertise in musical reading could be reliably inferred from eye movements, performance and subjective measures collected across five levels of expertise and 2) determine the best predictors for classifying expertise from 24 visual measures (e.g., number of progressive fixations, number of regressive fixations, pupil size, first-pass fixations, second-pass fixations..), 10 performance measures (e.g., eye-hand span, velocity, latency, play duration, tempo, false notes), and 4 subjective measures (perceived complexity and cognitive skills). Eye movements from 68 pianists being at five different levels of music expertise (according to their level in the Conservatory of music -from 1st cycle to professional) were co-registered with their piano performance via a MIDI interface, while they sight-read classical and contemporary music scores. Results revealed relevant classification by the SVM analysis. The model optimally classified the lower levels of expertise (1, 2) compared to the higher levels (3, 4, 5)  and the medium level (3) compared to higher levels (4, 5). Furthermore, the model identified across a total of 38 measures used, the four best predictors of the level of expertise: the sum of fixations by note, the number of blinks, the number of fixations and the average fixation duration. Thus, that it is possible to efficiently classify musical reading expertise from musicians' eye movements and performance, using SVM. The results have important theoretical and practical implications for music cognition and pedagogy, enhancing the specialized eye and performance behaviors required for an expert music reading.