we do not plan to recruit a doctoral student for the 2024 entering class.
we occasionally have openings for volunteer RA or Psych199 research positions — apply here:
how do brains represent and use uncertainty, and what does that have to do with metacognition and consciousness?
our brains continuously filter, quantify, categorize, and make “us” consciously aware of a deluge of sensory information with remarkable accuracy and precision. we behave adaptively in our environments and we learn. in many cases, the computations underlying these abilities appear to be mathematically optimal (but maybe not always). how do brains do this? how is noisy, ambiguous information represented in neuronal activity and neural connections? how does a brain know about or evaluate its own noise? how does a brain know whether it has interpreted incoming information correctly? how does it learn what to expect, and when to update those expectations? what can we learn from human and animal neural processing that will be beneficial to development of artificial systems?
these are the types of questions we try to answer in the lab. we use an interdisciplinary approach drawing insights and methodologies from cognitive science, computational neuroscience, psychology, neuroengineering, and philosophy. check out the projects page for more info.
some keywords about our research include the following: perception, machine learning, confidence, uncertainty, metacognition, consciousness, awareness, neuroimaging, computational modeling
watch a 4-minute introduction video about our work:
more videos on our media page!
techniques we use
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- neuroimaging: fMRI, EEG, & ECoG (humans) via collaborations: electrophysiology (primates, rats) calcium imaging (rodents)
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- computational modeling: generative models, monte carlo simulation, Bayesian decision theory, signal detection theory, neural networks, machine learning
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- neurostimulation: transcranial direct current stimulation (tDCS) transcranial magnetic stimulation (TMS)