We propose to improve our understanding of visual perception and the underlying neural computation in a Bayesian framework. Knowledge of how visual perception and neural computation works in normally sighted people is important for identifying, describing, and developing behavioral therapies for the visually impaired. The Bayesian framework for the brain has become a popular way to model perceptual behavior due to its elegant simplicity, effectiveness, and ability to unite a broad range of topics. However, there are fundamental gaps in our understanding of if and how the brain could operate in a Bayesian manner. The Bayesian hypothesis, which we propose to test, is that prior knowledge about the world has been integrated into our nervous systems and is used in behavior.
The first aim of this proposal is to use a novel behavioral technique to quantitatively estimate the form of prior expectations used by observers in visual perception tasks. This will be compared to the statistics of the world to assess the extent that the visual system has integrated prior knowledge.
The second aim i s to determine the ways in which a neural population code could represent and compute with prior distributions, and to link it to our behavioral data. Finally, we discuss how priors could arise and the time courses over which they could adapt.
The proposed understanding of how the brain uses prior knowledge is pertinent to public health because the Bayesian framework stipulates that patients suffering from visual deficits (e.g. amblyopia, strabismus, and retinal degeneration) may rely more on prior knowledge than others. Our proposed research sets a foundation for addressing the question of whether these patients incur changes to their representations of prior knowledge or the way these representations are used. Additionally, we propose to investigate the time course over which prior expectations can be adapted, which could pave the way for behavioral therapies to assist patients exposed to dramatic changes (e.g. cataract removal surgery or retinal prostheses) by exposing them to tailored new environments.
|Girshick, Ahna R; Landy, Michael S; Simoncelli, Eero P (2011) Cardinal rules: visual orientation perception reflects knowledge of environmental statistics. Nat Neurosci 14:926-32|