language, vision, and connectionism

[Dave.Touretzky@B.GP.CS.CMU.EDU]

Steve Lehar's argument ignores a crucial point: low-level vision is a
specialized area with properties that make it more tractable for
computational modeling than most other aspects of intelligence.

First, low-level vision is concerned with essentially geometric phenomena,
like edge detection.  Geometric phenomena are especially easy to work with
because they involve mostly local computations, and the representations are
(relatively!) intuitive and straightforward.  Basically they are iconic
representations on retinotopic maps.  Compare this with high level vision
tasks (e.g., 3D object recognition), or language tasks, where the brain's
representations are completely unknown, probably not iconic, and almost
certainly not intuitive or straightforward.

A second aspect of low-level vision is that it involves only the earliest
stages of the visual system, which are (relatively!) easily explored in a
neuroscience lab.  It's not that hard now, after several decades of
practice, to make autoradiographs of ocular dominance patterns in LGN, for
example, or to work out the mapping from the retina to the surface of area
17.  It's much harder to try to explore the higher level visual areas where
objects are recognized, in part because the circuitry gets too complex as
you go deeper into the brain, and in part because, since the highest areas
don't use simple geometric representations, techniques like autoradiography
or receptive field mapping are not applicable.  As Jim Bower pointed out,
even in the earliest stages of vision there are lots of unanswered basic
questions, such as why the feedback connections from visual cortex to LGN
vastly outnumber the feedforward connections.  Also, the wiring of the
different layers of cells in primary visual cortex (only a few synapses in
from the retina) is not fully known.  If we know so little about vision
after decades of studies on cats and monkeys, think about how much less we
know about language.

-- Dave