Dobromir Rahnev

Associate Professor, Associate Chair of Research

Education

B.A. (2007) Harvard University

Ph.D. (2012) Columbia University

Research Interests

Perceptual decision making, Visual metacognition, Computational modeling, Artificial Neural Networks (ANNs), Cognitive neuroscience

About

I study visual perception, decision making, and metacognition. My lab’s research centers on perceptual decision making – the minimal computation that converts uncertain sensation into action and thus exposes the canonical principles of brain computation. Our goal is to understand how sensory information is internally represented, how the brain makes decisions based on these representations, and how these decisions are evaluated using confidence ratings. Our work employs computational modeling, artificial neural networks (ANNs), psychophysics, fMRI, and TMS. The current focus of the lab is multi-alternative perceptual decision making, which represents a sweet spot where computational modeling is still tractable, but the complexity is high enough to allow the identification of internal mechanisms relevant in the real world. 

Awards

Federation of Associations in Behavioral & Brain Sciences (FABBS) Early Career Award (2023) Vision Sciences Society (VSS) Young Investigator Award (2022) American Psychological Association (APA) Distinguished Scientific Award for an Early Career Contribution to Psychology (2021) Student Recognition of Excellence in Teaching: Class of 1934 Award (received 3 times)

Selected Publications

Shekhar, M., Fung, H., Saxena, K., Rafiei, F., & Rahnev, D. (2025). Using artificial neural networks to reveal the human confidence computation. PLOS Computational Biology, 21(12):e1013827. Data and Code. 

Rahnev, D. (2025). A comprehensive assessment of current methods for measuring metacognition. Nature Communications, 16:701. Data and Code. 

Nakuci, J., Yeon, J., Kim, J.-H., Kim, S.-P., & Rahnev, D. (2025). Multiple brain activation patterns for the same perceptual decision-making task. Nature Communications, 16:1785. Data and Code. 

Xue, K., Shekhar, M., & Rahnev, D. (2024). Challenging the Bayesian confidence hypothesis. Proceedings of the National Academy of Sciences of the United States of America, 121(48): e2410487121. Data and Code. Preregistration. 

Shekhar, M. & Rahnev, D. (2024). Human-like dissociations between confidence and accuracy in convolutional neural networks. PLOS Computational Biology, 20(11):e1012578. Data and Code. 

Rafiei, F.*, Shekhar, M.*, & Rahnev, D. (2024). The neural network RTNet exhibits the signatures of human perceptual decision-making. Nature Human Behaviour, 8:1752-1770. Data and Code. Preregistration. 

Rahnev, D., Balsdon, T., Charles, L., de Gardelle, V., Denison, R.N., Desender, K., Faivre, N., Filevich, E., Fleming, S., Jehee, J., Lau, H., Lee, A.L.F., Locke, S.M., Mamassian, P., Odegaard, B., Peters, M.A.K., Reyes, G., Rouault, M., Sackur, J., Samaha, J., Sergent, C., Sherman, M., Siedlecka, M., Soto, D., Vlassova, A., & Zylberberg, A. (2022). Consensus goals for the field of visual metacognition. Perspectives on Psychological Science, 17(6):1746-1765. Data and Code. 

Shekhar, M. & Rahnev, D. (2021). The nature of metacognitive imperfection in perceptual decision making. Psychological Review, 128:45-70. Data and Code.