Learning expectations and the time scale of Bayesian priors (PhD)

I am investigating the acquisition and influence of perceptual expectations, which in a Bayesian framework are known as prior probabilities (or simply priors). My approach is both experimental (psychophysics) and theoretical (modelling – abstract and normative at first but later grounded to biologically plausible mechanisms). This project proposes to use psychophysics and modelling approaches to understand the extent to which the acquisition and effects of expectations can be explained as learning and application of Bayesian priors. Also, how do these effects relate to perceptual learning effects, which share many similarities but are usually evaluated differently? This work follows on from my MSc thesis, initially focusing on making that work sufficiently robust to be publishable, and then extending it in several ways. Some work will be done at the lab of Aaron Seitz in California, and in general Aaron is expected to give feedback on experimental design and analysis. Eventually, a model based on neural anatomy could be pursued, linking normative and mechanistic explanations. One of the long-term objectives of my research is to unify the above two directions, ie delineate the link between perceptual learning and probabilistic inference.

Related Themes

Related Publications and Presentations

  • Grigorios Sotiropoulos, Aaron Seitz, and Peggy Series, “How plastic is the “slow speeds prior”?”, Computational and Systems Neuroscience (Cosyne) 2012, 2012.
  • Grigorios Sotiropoulos, Aaron Seitz, and Peggy Series, “Contrast dependency and prior expectations in human speed perception”, Vision Research, 2014, 97, 16-23.
  • Grigorios Sotiropoulos, Aaron Seitz, and Peggy Series, “Changing expectations about speed alters perceived motion direction”, Current Biology, 2011, 21(21), R883-R884.

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