The Perception, Neuroimaging, and Modeling lab (PI: Dr. Doby Rahnev, rahnevlab.gatech.edu) is hiring a postdoctoral fellow. The exact topic of research is flexible and could include investigating the neural and/or computational bases of perceptual decision making, metacognition, attention, expectation or learning. A special focus of the lab is how these processes are supported by large distributed brain networks. The Rahnev lab uses a wide range of methods such as fMRI, TMS, psychophysics, computational modeling and concurrent TMS-fMRI. The position is initially for 2 years with a possibility for extension. Candidates will be given the opportunity to conduct studies building on current lab research or developing their own projects ideas. The positions are available immediately. The Rahnev lab, at the Georgia Institute of Technology in Atlanta, has access to exceptional research facilities. The lab space is conveniently located just steps away from a 3T Prisma MRI scanner at the Center for Advanced Brain Imaging (CABI, cabiatl.com). The lab also houses its own TMS equipment and is pioneering the use of concurrent TMS-fMRI that allows TMS to be delivered inside the MRI scanner. Working in the Rahnev lab presents opportunities for collaborations across several Atlanta-based universities including Georgia Tech, Emory and Georgia State. Together, these universities have transformed Atlanta into a hub for psychological and neuroscience research with particular strengths in computational neuroscience, the study of special populations (disease, aging, children), ECoG, concurrent brain stimulation and brain recording, and animal research. Georgia Tech has an attractive campus in the heart of Atlanta, a large, vibrant, multicultural city that boasts impressive cultural, culinary, and entertainment opportunities. The Rahnev lab aims to create a supportive, fun and productive environment. We are especially interested in maintaining our already diverse team and therefore seek applications from qualified individuals from all demographics and backgrounds.

This postdoctoral position offers an exciting opportunity to combine computational modeling, psychophysics, and EEG to study the computational mechanisms underlying flexible evidence integration in perceptual decision making.

Decision-making in noisy environments with constant sensory evidence involves integrating sequentially-sampled evidence, a strategy formalized by diffusion models which is supported by decades behavioral and neural findings. By contrast, it is unknown whether this strategy is also used during decision-making when the underlying sensory evidence is expected to change. Here, we trained monkeys to identify the dominant color of a dynamically refreshed checkerboard pattern that doesn't become informative until after a variable delay. Animals' behavioral responses were briefly suppressed after an abrupt change in evidence, and many neurons in the frontal eye field displayed a corresponding dip in activity at this time, similar to the dip frequently observed after stimulus onset. Generalized drift-diffusion models revealed that behavior and neural activity were consistent with a brief suppression of motor output without a change in evidence accumulation itself, in contrast to the popular belief that evidence accumulation is paused or reset. These results suggest that a brief interruption in motor preparation is an important strategy for dealing with changing evidence during perceptual decision making.

As Socrates said that "I know that I know nothing," our mind's function to be aware of our ignorance is essential for abstract and conceptual reasoning. However, the biological mechanism to enable such a hierarchical thought, or meta-cognition, remained unknown. In the first part of the talk, I will demonstrate our studies on the neural mechanism for metacognition on memory in macaque monkeys. In reality, awareness of ignorance is essential not only for the retrospection of the past but also for the exploration of novel unfamiliar environments for the future. However, this proactive feature of metacognition has been understated in neuroscience. In the second part of the talk, I will demonstrate our studies on the neural mechanism for prospective metacognitive matching among uncertain options prior to perceptual decision making in humans and monkeys. These studies converge to suggest that higher-order processes to self-evaluate mental state either retrospectively or prospectively are implemented in the primate neural networks.