Associate Professor of Psychology
B.A. (2007) Harvard University
Ph.D. (2012) Columbia University
I work on the high-level aspects of perceptual decision making. My research attempts to elucidate the brain mechanisms that influence what we perceive, as well as build computational models that explain current findings and lead to novel testable predictions. Specific areas of emphasis include visual metacognition, neural network models of vision, high-level processes like expectation and attention, and the role of large-scale brain networks in cognition.
To understand how perception emerges in the brain, I use functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS). Recently, I have combined these methods by delivering TMS simultaneously with fMRI. Although technically challenging, this method is very exciting for its power to combine the causal inferences associated with directly perturbing brain function with understanding of how such perturbations affect activity across the entire brain.
To understand the principles behind perception, I use computational models built on signal detection theory, drift diffusion, convolutional neural networks, and Bayesian inference. I am especially interested in how these different approaches relate to each other, as well as how they can be combined to explain accuracy, reaction time, and confidence within the same framework.
APA Distinguished Scientific Award for an Early Career Contribution to Psychology (2021), Student Recognition of Excellence in Teaching: Class of 1934 Award (2020), NSF CAREER Award (2019)
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 (in press). Consensus goals for the field of visual metacognition. Perspectives on Psychological Science. Data and Code.
Haddara, N. & Rahnev, D. (in press). The impact of feedback on perceptual decision making and metacognition: Reduction in bias but no change in sensitivity. Psychological Science. Data and Code. Preregistration.
Shekhar, M. & Rahnev, D. (2021). The nature of metacognitive imperfection in perceptual decision making. Psychological Review, 128, 45-70. Data and Code.
Shekhar, M. & Rahnev, D. (2021). Sources of metacognitive inefficiency. Trends in Cognitive Science, 25(1):12-23.
Rahnev, D. (2021). Response bias reflects individual differences in sensory encoding. Psychological Science, 32(7):1157-1168. Data and Code.
Rafiei, F., Safrin, M., Wokke, M.E., Lau, H., & Rahnev, D. (2021). Transcranial magnetic stimulation alters multivoxel patterns in the absence of overall activity changes. Human Brain Mapping, 42(12):3804-3820. Data and Code.
Bang, J.W. & Rahnev, D. (2021). Awake suppression after brief exposure to a familiar stimulus. Communications Biology, 4:348. Data and Code.
Rahnev, D., Desender, K., Lee, A., … Zylberberg, A. [83 authors] (2020). The Confidence Database. Nature Human Behaviour, 4:317-325. Data and Code.
Yeon, J. & Rahnev, D. (2020). The suboptimality of perceptual decision making with multiple alternatives. Nature Communications, 11:3857. Data and Code.
Bang, J.W., Shekhar, M., & Rahnev, D. (2019). Sensory noise increases metacognitive efficiency. Journal of Experimental Psychology: General, 148(3):437-452. Data and Code.