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surprisingly is that brains are not, it's hard to say this but, brains are not
designed the way we would design any machine. They
are not built the way we would build a machine.
We donít take the parts and put them together to build a whole.
In fact, what happens is just the other way around.
The whole starts out, itís just undifferentiated. The parts arenít
distinguished from each other and they become more and more different from each
other. The system becomes more and
more complicated. The problem is
the process is very indirect.
is not like building something from a plan. Itís
a very indirect process. It became
very clear that this process is very much what we would call today, self
organization. A lot of the
information that goes into building brains is not actually there in the genes.
Itís sort of cooked up or whipped up on the fly as brains develop.
And so if one is to explain how a very complicated organ like the brain
actually evolved, changed itís function to be able to do something like
language, one has to understand it through this very complicated prism of self
organization and a kind of mini-evolution process that goes on as brains develop
in which cells essentially compete with each other for nutrients.
Some of them persist and some of them donít.
Some lineages go on to produce vast structures in the brain.
Other lineages get eliminated as we develop, in some ways just like a
selection process in evolution.
We are going to have to think, in a sense, like biology thinks, like
embryology works in this kind of self organizing, self evolving-like logic to
get back at what actually causes things to occur and develop in complex ways.
I think this is the case with language itself. As itís passed from
generation to generation it has a kind of self organizing and evolution-like
character to it.
Language has changed the environments in which brains have evolved.
That changes the picture radically because now one can look at the brain, so to
speak, with an inside out perspective of the problem and ask the question,
ďWhatís different about human brains and how might that difference tell us
something about the forces that shaped it?
Dr. Charles Perfetti: The brain will develop neuro-structures that
generally are adapted from other uses. Thereís no reason to think the brain
has any module in it in the beginning for reading. Written language is too
recent a human development to have allowed brain evolution to accommodate it.
So, I think this is another aspect of the difficulty.
The brain has resources it can use for vision, in particular, and it has to
recruit these resources for reading. Those visual resources actually are not
maximally tuned for distinguishing the difference between an E and a C,
for example. There are levels of the visual cortex that can perform that task
but itís quite different from being able to tell one face from another or one
building from another, and so on. The high spatial frequencies have to be
Now, once that part is done and once you connect these visual form
perceptions with your language, that part, speaking loosely, could be considered
a module. The connections are pretty constrained from visual areas to temporal
and frontal areas that together seem to support rapid word identification and so
some of those areas are shared with spoken language as well. So, at that point
once youíve got the visual forms, and not just the forms but mapping them to
the spoken language forms, then you can talk about a system requiring some
modularity; behaving as if itís a highly skilled in text system using
sub-brain networks that are in some sense specialized for that. But in the
beginning you have to recruit other resources and apparently that can be a
David Boulton: Those resources would also perform, following along your
trajectory here, the differentiation inside of word sounds in a way thatís
otherwise artificial and that is unique to the kind of distinctions in word
sounds necessary to map to the code. So, itís not the same kind of attention
to sound differences that are necessary to participate in oral conversation.
Dr. Charles Perfetti: Exactly. Right, and so again, itís attention to
form, a much more analytic form, little pieces of form that in themselves have
David Boulton: So, in a natural setting weíre more attuned to seeing the
difference in larger wholes rather than in making differences between such small
Dr. Charles Perfetti: Right. I think thatís a big part of it.
David Boulton: Thatís why overall what were trying to get a handle on is
whether we can describe this lower part of the process as if its job is to
simulate a stream of language inputs to comprehension and fabricate that
simulation according to the way the brain has learned to process the code
through the visual and auditory modalities, which is providing it with
information and instruction.
Dr. Charles Perfetti: I like that. I think thatís a good way to
describe it. I find that very compatible with the way I think about it. Those
are your words, not mine, but I think thatís a good way of thinking about it.
So, for me, taking the
inside out perspective and looking at brains for clues to language rather than
the other way around, I ask the question this way. This is probably the most
significant departure from any kind of behavior that weíve seen in any other
species and itís radically different in many ways from what other species can
do. Such a large-scale difference
and probably the biggest difference between us and other species ought to be
reflected in some of the biggest differences between human brains and non-human
that as a kind of guiding hypothesis, not even a hypothesis, a sort of heuristic
as to what to look for, I ask the question, ďSo
what is different about human brains, if one could categorize in a systematic
way, what is really different about human brains? Wouldnít that tell us
something about language?Ē
truth is we donít know much more now about intelligence and what its
relationship is to whole brains than we did a century ago. We donít really
know how whole brains work yet. We
donít have a sort of general theory that everybody agrees upon, or even is
close to agreeing upon.
On the other hand,
this major change, if itís not just blowing the thing up larger and adding a
whole lot more to everything, if it is somehow distributed in an interesting
way, that distribution of how the same parts got changed and enlarged with
respect to each other might tell us something about what that difference is. In
fact, it tells me something very important and that is when you change the size
of something you also change the relationships between the parts.
We know that a small business, for example, can have one kind of
organization for doing things whereas a large business, ultimately just simply
because of its size, has to have all kinds of middle managers and different
kinds of bureaucracy and people donít get to talk to everybody.
The brain is a different kind of brain because itís bigger.
The connections are going to be different because itís bigger. That is
going to be a clue to it as well.
began to look at quantitative issues and with a developmental perspective, how
changes in size might change the circuits, that is, how circuits might respond
to this. I think for me that has
been the biggest source of insights, the biggest source of clues.
Theyíre not answers, theyíre clues.
That is, I think that the connections are different because the brain
is bigger and because not all parts expanded at the same rate.
I think thatís the first inside out clue to what is important about
this language difference. What was the change in the hardware that supported
this new kind of communication and cognition?
language, of course, is broken up according to a logic that has to do with
communication, has to do with symbols, has to do with the constraints we have on
interacting, perhaps, with speech sounds, perhaps with gesture.
The logic of the brain is a very old logic and a very conserved logic.
Itís the logic of embryology. Itís
the logic of self organization. In
fact, itís the logic that has been shared with a common ancestor that goes
back well before vertebrates.
can find hints as to the organization of the genes that develop brains in fly
brains. That logic has probably not
changed much. That logic is the
logic of the organization of brains. Thereís
unlikely to be a nice, neat direct map between what we see in the external world
of language and what we see inside brains.In fact, the map may be very, very confused and very, very different
inside the brain, that is, how the brain does what we see externally in
Itís not easy to predict from whatís produced in the world whatís
going to happen inside, so to speak, in the body. I think we can say the
same about this troubling and complex dynamic in which language evolution and
brain evolution are working in tandem interfering with each other, confusing
each other, shaping each other. That dynamic is probably one that is not
going to be easy to predict.
symbol processing problem, the automatization, (that is the speeding up of the
automatic running of syntax and of analysis), the mnemonic problems, the short
term memory problems associated with it -
these are things that are going to be generally there.
And of course, typically the constraints of producing and hearing sound,
or producing it visually and manually and interpreting it visually.
clearly early language-like behavior had to involve much less vocalization
because the brains that preceded us, mammal brains, are not well suited to
organizing sound in precise, discrete and rapidly produced learned sequences.
is something that mammal brains, in effect, are poorly designed to do precisely
because the system we use to produce sound is a system that normally
should be running on autopilot so that we can breathe appropriately, so that we
donít choke, and so on and so forth.Thereís literally been a change in that circuitry to override those
systems in order for language to be possible.
I think itís
interesting that in the history of recent linguistics we have modeled language
processing on a Turing Machine. The
original generative grammar that
Chomsky pioneered and that made a radical advance in the field is modeled on
the way computers work, that is a simple set of instructions and rules to
generate something. Almost certainly
thatís radically different than the way our brains work.
The question is, can we use that descriptive tool, that engineering
model, to go back and try to figure out how brains are doing this?
Or, in fact, does that engineering model actually get in the way of
figuring out what the brainís logic is as opposed to Turing Machine logic?
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