Beau Sievers

Cognitive neuroscience research

Just as a hammer is good for hitting nails and bad for making a sandwich, the shapes of our ideas determine what we can do with them. I use brain imaging, behavioral experiments, and computational models to understand what ideas with different shapes can and cannot do.

My past research has shown why music and movement are deeply connected and how conversation aligns our minds and brains. My current work asks how we grasp the most gnarly and misshapen ideas, whose meanings depend on relations between their parts. What kinds of minds must humans or machines have to understand language, music, mathematics, and reason?

I am a postdoctoral researcher in the Dartmouth Social Systems Lab advised by Professor Thalia Wheatley and a research associate in Psychology at Harvard University advised by Professors Joshua Greene and Leslie Valiant. I completed my PhD in Cognitive Neuroscience at Dartmouth College, advised by Thalia Wheatley.

I am also a composer and performer on percussion and electronics, and was a founding organizer of Indexical.


Please feel free to email me. I am also on Twitter and Mastodon.





fmri_go is open source software for presenting timelocked stimuli in an fMRI scanner and recording participant responses using PsychoPy. This software is in active development—use at your own risk.

Bouncing Ball

Bouncing Ball is open source software for comparing the dynamics of music and movement as described in Sievers, B., Polansky, L., Casey, M., & Wheatley, T. (2013). Music and movement share a dynamic structure that supports universal expressions of emotion. Proceedings of the National Academy of Sciences, 110(1), 70-75.

Morphological Metrics

Morphological Metrics is a Ruby implementation of metrics described in Larry Polansky's article Morphological Metrics. Larry's work on Morphological Metrics is of interest for anybody who wants to quantitatively compare contours; I came to it as a composer and continue to return regularly as a scientist.

Ruby PCSet

Ruby PCSet is a simple Ruby library for performing musical pitch-class set theory operations. It has a few nice things which similar tools lack, including evaluation of some properties described by Balzano (coherence, uniqueness) and Huron (aggregate dyadic consonance).