Megan Peters

Megan Peters Ph. D.

Assistant Professor, Bioengineering

Postdoctoral researcher, University of California, Los Angeles, 2014-2017
Ph.D., Computational Cognitive Neuroscience, University of California, Los Angeles, 2014
M.A., Cognitive Neuroscience, University of California, Los Angeles, 2010
B.A., Cognitive Science, Brown University, 2006

Research Areas

Neuroimaging, Computational Modeling, Machine Learning, Perception & Awareness, Neural Representations of Uncertainty

Contact Information

Department of Bioengineering

Dr. Peters received her Ph.D. in computational cognitive neuroscience from UCLA in 2014.  Her research aims to reveal how the brain represents and uses uncertainty and uncertain information to perform probabilistic computations that produce adaptive behavior, perception, and awareness. Dr. Peters uses neuroimaging, computational modeling, machine learning and neural stimulation techniques to study these topics.


  1. Peters, M.A.K., Kentridge, R.W., Phillips, I., & Block, N. (2017). Does unconscious perception really exist? Continuing the ASSC20 debate. Neuroscience of Consciousness.
  2. Peters, M.A.K.*, Thesen, T.*, Ko, Y.D.*, Maniscalco, B., Carlson, C., Davidson, M., Doyle, W., Kuzniecky, R., Devinsky, O., Halgren, E., & Lau, H. (2017). Perceptual confidence neglects decision-incongruent evidence in the brain. Nature Human Behaviour. [*shared first authorship]
  3. Peters, M.A.K., Fesi, J., Amendi, N., Knotts, J.D., Lau, H., & Ro, T. (2017). Transcranial magnetic stimulation to visual cortex induces subpotimal introspection. Cortex. doi:10.1016/j.cortex.2017.05.017
  4. Peters, M.A.K., Ma, W.J., & Shams, L. (2016). The Size-Weight Illusion is not anti-Bayesian after all: A unifying quantitative Bayesian account. PeerJ 4:e2124 doi:10.7717/peerj.2124.
  5. Peters, M.A.K., Ro, T., & Lau, H. (2016). Who’s afraid of response bias? Neuroscience of Consciousness. doi:10.1093/nc/niw001.
  6. Maniscalco, B., Peters, M.A.K., & Lau, H. (2016). Heuristic use of perceptual evidence leads to dissociation between performance and metacognitive sensitivity. Attention, Perception, & Psychophysics. doi:10.3758/s13414-016-1059-x.
  7. Peters, M.A.K., & Lau, H. (2015). Human observers have optimal introspective access to perceptual processes even for visually masked stimuli. eLife. doi:10.7554/ eLife.09651
  8. Peters, M.A.K., Balzer, J., & Shams, L. (2015). Smaller = denser, and the brain knows it: Natural statistics of object density shape weight expectations. PLoS ONE 10(3), e0119794.
  9. Balzer, J., Peters, M.A.K., & Soatto, S. (2013). Volumetric reconstruction applied to perceptual studies of size and weight. WACV14: IEEE Winter Conference on Applications of Computer Vision. arXiv:1311.2642.
  10. Peters, M.A.K., Thompson, B., Merabet, L.B., Wu, A.D., & Shams, L. (2013). Anodal tDCS to V1 blocks visual perceptual learning consolidation. Neuropsychologia, 51(7), 1234 – 1239.
  11. Kim, R., Peters, M.A.K., & Shams, L. (2012). 0+1 > 1: how adding non-informative sound improves performance on a visual task. Psychological Science, 23(1), 6 – 12.


Organization for Human Brain Mapping Merit Abstract Award (2017) 
UCLA Brain Research Institute Fine Science Tools Travel Award (2016)  
UCLA Best Paper in Psychology Award (2016) 
UCLA Department of Psychology Dissertation Year Fellowship (2013-2014)
National Science Foundation Graduate Research Fellowship (2010-2013)
National Institute of Health Behavioral Neuroscience Training Fellowship (2009-2010)
UCLA Chancellor’s Prize (2009-2010)
Reed College Young Scholars Program Vollum Scholarship (2001-2002)