Lets see what you think! Bayesian reconstruction of perceptual
experiences from human brain activity.

Jack Gallant, Thomas Naselaris, Ryan Prenger, Kendrick Kay, Michael Oliver 
UC Berkeley

A recent paper from our laboratory demonstrated that fMRI measurements
of hemodynamic brain activity contain far more information than was
believed previously. In fact those data suggested that fMRI might
provide enough information to permit true noninvasive reconstruction
of perceptual experiences: a direct window into visual
perception. This talk will focus on a new Bayesian decoder that can
reconstruct natural images seen by an observer from measured brain
activity. The decoder combines three elements: a structural encoding
model that characterizes signals from early visual areas; a semantic
encoding model that characterizes signals from higher visual areas;
and appropriate priors that incorporate statistical information about
the structure and semantics of natural scenes. By combining all these
elements the decoder produces reconstructions that accurately reflect
the distribution, structure and semantic category of the objects
contained in the original image. These results help clarify why the
brain contains many distinct representations of the visual world, and
highlight the important role of prior knowledge in visual
perception. This decoding framework might form the basis of practical
new brain-reading technologies.