Christopher J. Bates

I'm a PhD candidate at University of Rochester, in the department of Brain and Cognitive Sciences (since 2015), and Robert Jacobs is my advisor. Computational modeling is my hammer, and I'm seeking nails. Previously, I was a post-baccalaureate researcher in the CoCoSci group at MIT. I have an undergraduate degree in mechanical engineering from Purdue University.


Two current interests are scene-understanding and memory. One ongoing project asks whether people use generative models to predict the physics of their environments. Is there a "physics engine in the head"? I have done work on a project that extends this idea to the domain of liquids. I also made a game just for fun (powered by liquidFun).

Another project investigates the adaptability of visual memory. Given its limited capacity, does visual memory seek to improve performance by adapting to new tasks and leveraging regularities in the environment? Intuitively, if your memory cannot store everything, it must pick and choose. Depending on the task at hand, you may want to store different aspects of a stimulus in order to perform well. Also, if you know the statistics of the stimuli you normally see, you can make better guesses. So we might expect to find that our memory quickly adapts to new tasks and learns statistics of stimuli. We have found some preliminary evidence of this. Publications to come!


Bates, C. J., Battaglia, P., Yildirim, I., & Tenenbaum, J. B. (2015). Humans predict liquid dynamics using probabilistic simulation. In CogSci.

Bates, C. J., Yildirim, I., Tenenbaum, J. B., & Battaglia, P. (2018). Modeling human intuitions about liquid flow with particle-based simulation. arXiv preprint arXiv:1809.01524.

Jacobs, R. A., & Bates, C. J. (2018). Comparing the Visual Representations and Performance of Humans and Deep Neural Networks. Current Directions in Psychological Science, 0963721418801342.

Bates, C. J., Lerch, R. A., Sims, C. R., & Jacobs, R. A. (2019). Adaptive allocation of human visual working memory capacity during statistical and categorical learning. Journal of Vision, 19(2), 11-11.

Bates, C.J., Jacobs, R. A. (submitted). Efficient Data Compression in Perception and Perceptual Memory.

From a previous life:
Cha, T. G., Baker, B. A., Salgado, J., Bates, C. J., Chen, K. H., Chang, A. C., ... & Choi, J. H. (2012). Understanding oligonucleotide-templated nanocrystals: growth mechanisms and surface properties. ACS nano, 6(9), 8136-8143.