Over the past few years, cross-cultural comparative work has made various claims about the universality of aspects of music, aesthetic preferences, and emotion (Fritz 2009, Brown & Jordania 2013, Savage et al. 2015). However, recent work suggests that features that were previously regarded as universal...
Our auditory and visual memory systems encode information selectively due to limited resources, resulting in systematic distortions and biases. Understanding these biases allows us to characterize the latent geometry of our mind, namely, to better understand how the external world is mapped onto internal representations.
Over 90% of psychology experiments between 2003-2007 were conducted on WEIRD subjects, hailing from Western, Educated, Industrialized, Rich, and Democratic societies (Arnett 2008). Henrich et al. (2010) have argued that these populations constitute an extremely biased sample across several critical dimensions, manifested in paradigms from basic visual and spatial perception to social cognition.
Combining methodologies from machine learning and deep learning, music information retrieval, and information theory, this project aims to create tools to analyze and extract latent structure and syntax in music in order to answer fundamental questions pertaining to meter, scale structures and harmony.