Human brains have an enormous capacity to adapt to their environment. Brain plasticity relies on our ability to construct complex internal representations and to apply them to predict and act according to changing conditions in the world. Focusing largely on the auditory modality, the Research Group Jacoby explores the roles of experience and exposure in creating and affecting our perception of the world. Using novel methodologies including massive online experiments, transmission chains, and cross-cultural research we aim to characterize the ways in which people from diverse backgrounds internalize and process external conditions. Studying such differences and similarities can help us better ascertain the interaction of nature and nurture in generating individual sensory experiences.
The group comprises three main areas of inquiry:
- Cross-cultural auditory perception: this branch of the project integrates elements from neuroscience, psychology, cultural anthropology, and ethnomusicology with the aim of significantly increasing our knowledge about the role of experience in determining auditory perception.
- Characterizing internal representations with massive online experiments: Harnessing techniques from Bayesian perception and machine learning techniques (such as transmission chains and Monte Carlo Markov Chain with People) to the investigation of foundational questions in psychophysics, this area of research explores the geometry of internal representations of complex perceptual spaces.
- Computational music cognition: Using corpus analysis, syntax modeling, causal analysis, and behavioral experiments, this branch of the project investigates latent structures in musical performance, works, and improvisation.
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.