Neural representations of uncertainty for perception and learning (PhD)

This project aims to identify neural processes that accurately represent uncertainty in the cortex. This will be accomplished through a series of psychophysical experimental procedures that investigate the properties of induced expectations, in particular the limits of forming priors through fast statistical learning. The experimental data will be used to evaluate potential computational models, and propose a unified framework.

Related Themes

Related Publications and Presentations

  • Nikos Gekas, Matthew Chalk, Aaron Seitz, and Peggy Series, “Complexity and specificity of experimentally induced expectations in motion perception”, Journal of Vision, 2013.
  • Nikos Gekas, Aaron Seitz, and Peggy Series, “Investigating the specificity of experimentally induced expectations in motion perception”, Vision Sciences Society Annual Meeting 2012, 2012.

Related People