Research

I’m interested in using computational techniques to explore how the brain learns from sensory experience and extracts meaningful representations from complex temporal stimuli.

So far, my research in music cognition has looked at topics such as modeling listeners’ tonal expectations and identifying cross-cultural similarities in musical scales. Some relevant publications:

• Verosky, N. J., & Morgan, E. (2021). Pitches that wire together fire together: Scale degree associations across time predict melodic expectations. Cognitive Science, 45(10). [Web]
• Verosky, N. J. (2021). Interpreting the tonal hierarchy through corpus analysis. Psychomusicology: Music, Mind, and Brain. [Web]
• Verosky, N. J. (2019). Corpus-based learning of tonal expectations with expectation networks. Journal of New Music Research, 48(2), 145-158. [Web]
• Verosky, N. J. (2017). Hierarchizability as a predictor of scale candidacy. Music Perception, 34(5), 515-530. [PDF] [Web]

My ongoing projects at the Objects and Knowledge Lab include investigating expertise in music notation reading and representations of categorical knowledge in convolutional neural networks.