Location:
BioX/Clark Center, Room S360,
Title: "Inferential Processes
and the Architecture of Visual Cortex"
Provost of Science, Agriculture & Engineering
University of Newcastle
Abstact:
A striking aspect of vision is that observers do not
see what their eyes tell their brains (e.g. Young 1994). This mismatch is
clearly evident in the fact that apparently detailed coloured
texture is seen in extra-foveal vision, where such
information is very modest or absent in the input from the retina, and in other
cases, where information at one location and time influences the likelihood
that information will be present at a different location, and at a later time.
These considerations, and detailed observations of what neuroanatomy
can tell us of the processing architecture in visual cortex, suggest that the
visual system may undertake an inferential style of computation, rather than analysing bottom-up information from the eyes, which latter
model is in the ascendant in visual neurophysiology.
We have been exploring the different predictions that
the inference and traditional models make, when the prior probabilities of
stimuli are manipulated, as they are during normal vision. In one paradigm, we
use spatiotemporal sequences of oriented bars to provide the visual system with
"reason to believe" that a bar of particular orientation will be
presented at a particular time. We then vary the congruence, incongruence and
presence of the bar in the RF in relation to these prior expectations. The
results so far are striking: V1 neurons are often more modulated by these prior
and distant events - in many cases more than 400ms, and 6-10 degrees away from
their classical RF - than they are by events within their RF. Similar results
relate to an experimental paradigm that tests whether V1 neurons really suffer
from the "aperture problem". The interaction between priors and
likelihood functions is well fitted by a Bayesian model, and only poorly fitted
by the traditional view that V1 cells are local filters of one kind or another.
Similarly, Bayesian models fit better than mismatch models, such as that of Rao and Ballard. These results suggest that V1 neurons are
signaling the posterior probability of visual stimuli, on the basis both of
prior knowledge about the world and information from the eye, effectively
imputing feature constellations to the visual world, rather than simply analysing local contours.