I'm a PhD candidate at University of Rochester, in the department of Brain and Cognitive Sciences (since 2015), working with Robert Jacobs. I will be postdoc-ing with Sam Gershman at Harvard starting fall 2020. Previously, I was a post-baccalaureate researcher in the CoCoSci group at MIT. I have an undergraduate degree in mechanical engineering from Purdue University.
Research
My dissertation work explores what optimal lossy compression can tell us about perceptual representations, both in memory and attention. Compression is an important and ubiquitous technique from computer science and signal processing, and it allows us, for example, to stream audio and video with our limited connection speeds. By analogy, biological systems also have limited "bandwidth", and thus, if evolution has designed them to be efficient, they should follow the same theoretical principles that we have applied in the digital domain.
My approach has been facilitated by recent advances in deep learning. For example, a hypothesis I present is that different memory sub-systems can be viewed as falling along a spectrum, from somewhat compressed (shorter-term or newer memories) to very compression (longer-term or older memories). Deep learning approaches allow me to see what happens when I compress images by different amounts. The implication is that as our memories decay over time, they still try to retain as much of the most useful information as possible. My modeling work shows that often times this ends up being categorical information (e.g. you know you saw an apple, and not a banana, but forget many perceptual details), which matches well with what we know about long-term memory.
My work in "intuitive physics" asks whether people use generative models to understand and predict the physics of their environments. My colleagues and I extended the idea of a "game engine in the head" from the domain of rigid solids to the more complex and chaotic domain of liquids. We find that we can account well for people's predictive judgments about the dynamics of water and honey in complex scenes by assuming "quick-and-dirty" mental simulation that is reasonably close to the true physics. I also made a game just for fun (powered by liquidFun).
Talks
University of Pennsylvania (March 6, 2020) [video link]Publications
Bates, C. J., & Jacobs, R. A. (2020, April 23). Efficient Data Compression in Perception and Perceptual Memory. Psychological Review. Advance online publication. http://dx.doi.org/10.1037/rev0000197 [Paper]Bates, C.J., Jacobs, R. A. (2019). Efficient Data Compression Leads to Categorical Bias in Perception and Perceptual Memory. In Proceedings of the 41st Annual Meeting of the Cognitive Science Society. [Paper]
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. [Paper]
Bates, C. J., Yildirim, I., Tenenbaum, J. B., & Battaglia, P. (2019). Modeling human intuitions about liquid flow with particle-based simulation. PLOS Computational Biology, 15(7), e1007210. [Paper]
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. [Paper]
Bates, C. J., Battaglia, P., Yildirim, I., & Tenenbaum, J. B. (2015). Humans predict liquid dynamics using probabilistic simulation. In Proceedings of the 37th Annual Meeting of the Cognitive Science Society. [Paper]
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.