As part of a research project to better understand how the brain encodes music, I am working with Guilhem Marion's python implementation of IDyOM a statistical model originally made by Michael Pearce to quantify musical surprisal.
The objective of this project is to improve surprisal estimation fed into the TRF (temporal response function), yielding more accurate reconstructed EEG signals that better reflect real neural responses as recorded in the Di Liberto (2020) experiment.
This work will advance understanding of human auditory cognition, support the validity of Neural Resonance Theory, and explore how cultural familiarity and immediate context respectively shape neural responses to music and speech.