evolutionary and computational theories of mind

C.J.L. Wolf C.J.L.Wolf at ncl.ac.uk
Sat Jan 19 12:09:52 EST 2002

> > 
> Why should there be computational ciruits in the brain? Computers have
> only been around for 50 years, might nature not have given us
> something slightly more honed?  The computational theory makes the
> very big suppoition that the outputs we get from our minds are the
> result of logical operations upon the sensory stuff that went in. 
> However, the output only seems *right* becuase everyone's is generally
> the same or similar.  If everyone behaved totally differently then
> that would be reagrded as the *right* output too.
I think this is rather a misconception of what computational neuroscience
actually is. Few if any computational theorists would argue that the brain
operates in the binary, intensely logical way that a computer does; rather
computational neuroscience recognises that brains essentially do
operations on information - and tries to make out what those operations
must be.

For example: 

If you are in a darkened room, looking at a blue object, does
it appear blue because it reflects more short wavelength light than long /
medium wavelength light? Or could it be that the light shining on it is

Computational neuroscience first defines what info is available to your
brain as it tries to solve the ambiguity. For example, it may be that
there is a little mirror like reflection on the object (specularity).
There are other cues too - but I'm just going to talk about this one.

You then describe the algorithm - look at the reflection; check that it is
a specularity (it should be bright, and have fuzzy edges, and if you focus
on it, then the rest of the object will be out of focus). If it is blue,
then the lighting is blue, and the object should probably appear greyer
than first assumed. But if the reflection is white, then the object should
be blue.

The final stage is to implement the algorithm. You could easily do it on a
computer; that certainly isn't to imply your brain would implement it in
the same way if indeed it implements it at all. But you could look at how
well the computer algorithm performs and compare that to a human
observer's performance - this is more the realms of psychology. At the
very least, if we find that the algorithm doesn't work in its computer
implementation, this tells us that we got the algorithm wrong, or at least

For a good introduction, try David Marr's book, 'Vision'. I believe that
he also discussed evolutionary reasons why modularity is desirable. It
centered on being able to make changes module by module - if one didn't
have the degree of finesse that this bestows, it would be very difficult
to make changes to one system (e.g. vision) without making changes to all
systems (e.g. the auditory system also) that could very well be

As an example, I heard that there is a sort of sheep that was bred in
yorkshire to have short legs to save work in building high stone walls to
keep them in. If different genes governed the development of front legs
and back legs (or even worse - left and right legs - though this is the
case for haggis, of course) then some rather awkward animals could have
resulted so it's just as well that leg development is pleiotrophic. On the
other hand, if one also wanted this sheep to have long ears so it could
hear predators from far away, and if ear length was governed by the genes
for leg length, then it would be very difficult to breed sheep with short
legs and long ears, even though in the environment of a Yorkshire dale,
this was desirable.


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