The computational and neural basis of visual metacognitioncogsadmin
The talk on computational and neural basis of visual metacognition was given by Dobromir Rahnev, Associate Professor of Psychology, Georgia Institute of Technology. It was held on 26 Aug 2021, 4 PM IST
During the talk, Dr Rahnev introduced metacognition as the ability to judge the accuracy of our own decisions. Metacognitive ability is known to be imperfect but the nature of this imperfection is still not understood. He presented a new model of metacognitive imperfection that assumed two separate noise sources: sensory noise that affects both the perceptual decision and the confidence ratings, and metacognitive noise that only affects the confidence ratings. The model makes the counterintuitive prediction that higher sensory noise should lead to better metacognitive efficiency. He presented a series of studies that confirm this prediction and shed light on the nature of this metacognitive noise. Finally, he linked different components of our confidence model to the function of different areas of the prefrontal cortex. Together, these results build the foundation for a mechanistic understanding of visual metacognition.
About the speaker: Dr. Rahnev received his Ph.D. in Psychology from Columbia University in 2012. After completing a 3-year post-doctoral fellowship at UC Berkeley, he joined Georgia Tech in 2015. His research focuses on perceptual decision making – the process of internally representing the available sensory information and making decisions on it. Dr. Rahnev uses a wide variety of methods such as functional magnetic resonance imaging (fMRI), transcranial magnetic stimulation (TMS), simultaneous TMS-fMRI, psychophysics, and computational modeling. Dr. Rahnev’s work appears in high-impact journals such as Behavioral and Brain Sciences, PNAS, Nature Communications, and Nature Human Behavior. He has received over 3 million dollars in funding, including PI grants from NIH, NSF, and the Office of Naval Research.