I am a cognitive scientist who uses computational modeling and behavioral experimentation to investigate those complex forms of cognition that are uniquely human, and indeed make us human. My primary line of research is in mathematical and computational thinking, where my students and I investigate the cognitive mechanisms underlying our ability to understand abstract mathematical concepts (e.g., in geometry and topology) and to reason about computationally hard problems (e.g., the travelling salesperson problem). My lab also works on problem solving, with an increasing focus on bounded rationality and the interface between the internal cognitive system and the structure of the environment. Our research on language understanding is characterized by the development of computational models and the use of machine learning techniques.
Rao, V. N. V., Bye, J. K., & Varma, S. (2021). Categorical perception of p-values. Topics in Cognitive Science.
Marupudi, V., Harsch, R., Rao, V., Bye, J. K., Park, J., & Varma, S. (2021). The role of clustering in the efficient solution of small Traveling Salesperson Problems. In Fitch, T., Lamm, C., Leder, H., & Tessmar, K. (Eds.), Proceedings of the 43rd Annual Conference of the Cognitive Science Society (pp. 1865-1871).
Schmied, A., Varma, S., & Dubinsky, J. M. (2021). Acceptability of neuroscientific interventions in education. Science and Engineering Ethics, 27, e52.
Patel, P. J., & Varma, S. (2018). How the abstract becomes concrete: Irrational numbers are understood relative to natural numbers and perfect squares. Cognitive Science, 42, 1642-1676.
Varma, S., Blair, K. P., & Schwartz, D. L. (2019). Cognitive science foundations of integer understanding and instruction. In A. Norton & M. Alibali (Eds.), Constructing number: Merging perspectives from psychology and mathematics education (pp. 307-327). Switzerland: Springer.