IDyOMpy: A new Python-based model for statistical analysis of musical expectations

Guilhem Marion; Fei Gao; Benjamin P Gold; Giovanni M Di Liberto; Shihab Shamma

doi:10.1016/j.jneumeth.2024.110347

IDyOMpy: A new Python-based model for statistical analysis of musical expectations

J Neurosci Methods. 2024 Dec 19:110347. doi: 10.1016/j.jneumeth.2024.110347. Online ahead of print.

Authors

Guilhem Marion¹, Fei Gao², Benjamin P Gold³, Giovanni M Di Liberto⁴, Shihab Shamma⁵

Affiliations

¹ Laboratoire des Systèmes Perceptifs, Département d'Étude Cognitive, École Normale Supérieure, PSL, Paris, France; Department of Psychology, New York University, New York City, USA; Institute for Systems Research, Electrical and Computer Engineering, University of Maryland, College Park, USA. Electronic address: guilhem.marion@ens.fr.
² Department of Computational Linguistics, University of Zurich, Zurich, Switzerland.
³ Neuroimaging & Brain Dynamics Lab, Vanderbilt University, Nashville, USA.
⁴ School of Computer Science and Statistics, Trinity College, The University of Dublin, Ireland; ADAPT Centre, Ireland; Trinity College Institute of Neuroscience, Dublin, Ireland.
⁵ Laboratoire des Systèmes Perceptifs, Département d'Étude Cognitive, École Normale Supérieure, PSL, Paris, France; Institute for Systems Research, Electrical and Computer Engineering, University of Maryland, College Park, USA.

PMID: 39709074
DOI: 10.1016/j.jneumeth.2024.110347

Abstract

Background: IDyOM (Information Dynamics of Music) is the statistical model of music the most used in the community of neuroscience of music. It has been shown to allow for significant correlations with EEG (Marion, 2021), ECoG (Di Liberto, 2020) and fMRI (Cheung, 2019) recordings of human music listening. The language used for IDyOM -Lisp- is not very familiar to the neuroscience community and makes this model hard to use and more importantly to modify.

New method: IDyOMpy is a new Python re-implementation and extension of IDyOM. This new model allows for computing the information content and entropy for each melody note after training on a corpus of melodies. In addition to those features, two new features are presented: probability estimation of silences and enculturation modeling.

Results: We first describe the mathematical details of the implementation. We extensively compare the two models and show that they generate very similar outputs. We also support the validity of IDyOMpy by using its output to replicate previous EEG and behavioral results that relied on the original Lisp version (Gold, 2019; Di Liberto, 2020; Marion, 2021). Finally, it reproduced the computation of cultural distances between two different datasets as described in previous studies (i.e. Pearce, 2018).

Comparison with existing methods and conclusions: Our model replicates the previous behaviors of IDyOM in a modern and easy-to-use language -Python. In addition, more features are presented. We deeply think this new version will be of great use to the community of neuroscience of music.

Keywords: Expectations; IDyOM; Music cognition; Statistical model of music.