Lu Lucuța @lu-lucuta.bsky.social · 02/09/2026Crucially, we propose that combining methodological strengths across research traditions to model both the acoustic and statistical properties within music and speech stimuli can provide a more nuanced glimpse into how the interplay between content and timing is instantiated at a neural level ✨ 020
Lu Lucuța @lu-lucuta.bsky.social · 02/09/2026For future studies, we recommend the careful consideration of multiple experimental decisions: the properties of the auditory stimuli used 🔊♫💬, the algorithmic and implementational principles behind neural data modelling ⚙️🧠, and the theoretical interpretation of the observed results💡📖. 110
Lu Lucuța @lu-lucuta.bsky.social · 02/09/2026We used Marr's 'Three Levels' to assess how content and timing predictions could be instantiated at a neural level within each framework. Interestingly, the main modelling difference isn't between the theories per se, but whether the proposed implementation relies on neural oscillations or not. 🖥️🧠 110
Lu Lucuța @lu-lucuta.bsky.social · 02/09/2026While content and timing predictions have been widely investigated, the neural bases of content-timing interactions remain unclear🎶💬. To improve our understanding of their interplay, we turned to theoretical frameworks, primarily #PredictiveCoding and #DynamicAttendingTheory (but not just!). 110
Lu Lucuța @lu-lucuta.bsky.social · 02/09/2026Big news if interested in the theories behind predictive processing in #music and #speech! We recently published a preprint of our critical review on the Neural Bases of Prediction in Music and Speech. @anna5ash.bsky.social @annekosem.bsky.social @perikeller.bsky.social osf.io/preprints/ps... 1151