Dobromir Rahnev

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.