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

So far, my research using musical stimuli has looked at topics such as modeling listeners’ statistical expectations and identifying cross-cultural similarities in pitch organization. Some relevant publications:

  • Verosky, N. J. (In press). Associative learning of an unnormalized successor representation. Neural Computation.
  • Verosky, N. J. (2022). Essen as a corpus of early musical experience. Empirical Musicology Review, 17(2), 154-164. [Web]
    • See also, Kragness, H. E. (2022). Developmental considerations in children’s song exposure: A commentary on Verosky (2022). Empirical Musicology Review, 17(2). 165-168. [Web]
  • 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 visual expertise in music notation reading and representations of categorical knowledge in convolutional neural networks.