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From: meekerdb [email protected]
[snip]
>> Although reductionism has recently received a lot
>> of bad press from supermarket tabloids and new age
>> gurus the fact remains that if you want to study
>> something complex you've got to break it into
>> simpler parts and then see how the parts fit
>> together. And in the final analysis things
>> happen for a reason or they don't happen for a
>> reason; and if they did then it's deterministic and
>> if they didn't then it's random.
Perhaps your final analysis is a bit too shallow and
self limiting. Why you cling so tenaciously to this need
for definitive causality chains (or else it must be
complete randomness) is amusing, but is not misguided.
You cannot show definitive causality for most of
what goes on in most of the universe. You can
hypothesize a causal relationship perhaps, but you
cannot prove one for all manner of phenomenon arising
out of chaotic systems. The brain is a noisy chaotic
system and you are attempting to impose your Newtonian
order on it.
Your approach does not map well onto the problem domain.
And what you say has no predictive value; it does not
help unravel how the brain works... or how the mind
arises within it.
>> It does help. There's no evidence that the brain can't be understood as
>> a parallel computer plus some randomness. The problem with John's
>> formulation is he insists there is either *a* reason or not *a* reason.
>> Hardly anything can be thought of as having *a* reason. In the case of
>> human behavior, each instance almost certainly has many different causes,
>> some in memory, some in the immediate environment, and some which are
>> random and don't have an effective cause. I think of the person,
>> brain/body/etc, plus immediate environment narrow down the probable
>> actions to a few, e.g. 1 to 20, and then some quantum randomness realizes
>> one of those. So it's not deterministic like Laplace's clockwork world,
>> but it's not anything-is-possible either.
Sure reductionist approach can gain you a partial understanding; you can slice
the brain up; analyze processes and try to classify and drill down to smaller
and down into increasingly tightly focused problem domains within the larger
problem domain of how the brain works. But this approach fails to capture the
holistic dynamic processes and subtle interplays between rapidly forming and
also rapidly subsiding synchronized firing networks that pull together
coalitions of neurons from many different brain regions. The brain is not only
massively parallel -- it is a superbly tight packed one hundred trillion
connection machine with 86 billion operating nodes in the network -- it is also
incredibly noisy and seemingly chaotic.
The simple deterministic causality approach cannot model a vastly parallel and
very noisy chaotic system such as the brain. The brain is not operating on
deterministic principles -- or at least not completely so. Without modeling the
chaos -- and chaos is modeled all the time and predictive statements can be
made about chaotic systems (say the chaotic airflow over an air foil for
example). But these models and the equations that comprise them account for
chaos and often rely on probabilistic and consensus based algorithms.
I am not arguing that the brain is beyond study or cannot be understood,
analyzed or modeled. What I am arguing is that it is not a simple deterministic
system in which state X will always lead to outcome Y; nor can it always be
determined based on knowing an outcome Y in the brain what the causational
state was that ultimately lead to that outcome. Even if there may be causation
the processes by which the brain operates are so distributed and
inter-dependent and the system is so incredibly noisy (and it really is a very
high noise to signal ratio) that any attempt to work backwards from some
outcome down the causal chain of neural activity that resulted in it rapidly
breaks down and grows geometrically more difficult with each remove from the
final result and back into the densely nested forest of potential network
branches.
John keeps insisting that X is Y or X is not Y. True, but so what? It does not
provide any great insight into how the brain works as a dynamic entity.
Basically based on reading his posts on the subject what I am stating is that
he would not be hired to help work out the problem based on his views of how
the brain can be understood. In fact he would not make it past the initial
screening interview -- IMO. I am not calling him stupid -- though he does
question my intelligence -- but for some reason (which I know not of) he clings
to this simplistic view of what is in fact a highly dynamic, noisy, chaotic and
vastly parallelized system.
Cheers,
Chris
[snip]
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