From hubeyh at mail.montclair.edu  Sat Oct 19 22:32:14 2002
From: hubeyh at mail.montclair.edu (H.M. Hubey)
Date: Sat, 19 Oct 2002 18:32:14 -0400
Subject: [language] Darwinism & Evolution of Language]
Message-ID: <SAT.19.OCT.2002.183214.0400.HUBEYH@MAIL.MONTCLAIR.EDU>

<><><><><><><><><><><><>--This is the Language List--<><><><><><><><><><><><><>


  Did this show up on the LinguistList mail?


-------- Original Message --------
Subject: Re: 13.2673, Disc: Darwinism & Evolution of Language
Date: Thu, 17 Oct 2002 11:41:29 -0400
From: "H.M. Hubey" <hubeyh at mail.montclair.edu>
To: linguist at linguistlist.org
References: <20021017031623.19762.qmail at linguistlist.org>



LINGUIST List wrote:

>
>Date:  Wed, 16 Oct 2002 12:43:47 EDT
>From:  Nemonemini at aol.com
>Subject:  Re: 13.2645, Disc: Darwinism & Evolution of Language
>
>
>Part of your argument is invalid; evolution as seen by Darwinism or
>Evolution Theory is not simply "random genetic evolution", but
>mutation AND selection. The former is random, the latter obviously
>not.
>
>
>
Actually this is simply a matter of definition. It goes like this:

random + deterministic = random
random*deterministic = random

The former is "additive noise" and the latter "multiplicative noise",
and both are random. "Random" is not
equal to "uniforrmly random", thus one can find broadbrush patterns in
random phenomena.





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From hubeyh at mail.montclair.edu  Mon Oct 21 00:42:52 2002
From: hubeyh at mail.montclair.edu (H.M. Hubey)
Date: Sun, 20 Oct 2002 20:42:52 -0400
Subject: [language] [Fwd: [evol-psych] On Grammar vs. Language in
 Neurolinguistics]
Message-ID: <SUN.20.OCT.2002.204252.0400.HUBEYH@MAIL.MONTCLAIR.EDU>

<><><><><><><><><><><><>--This is the Language List--<><><><><><><><><><><><><>




-------- Original Message --------
Subject: [evol-psych] On Grammar vs. Language in Neurolinguistics
Date: Fri, 18 Oct 2002 18:26:56 -0500
From: Ian Pitchford <ian.pitchford at scientist.com>
Reply-To: Ian Pitchford <ian.pitchford at scientist.com>
Organization: http://human-nature.com/
To: evolutionary-psychology at yahoogroups.com



_________________________________________________________________


 From Scienceweek October 4, 2002 Vol.6 Number 40


=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=

5. On Grammar vs. Language in Neurolinguistics

Massimo Piattelli-Palmarini (University of Arizona, US) discusses
grammar vs. language, the author making the following points:

1) Two styles of explaining the science of mind and behavior
have been competing for as long as anyone cares to remember:
empiricist, centering on habit formation, statistical learning,
imitation and association; and rationalist, focusing on the
projection of internally represented rules. Despite relentless
effort, the former has delivered rather meager results, whereas
the latter, with its pivotal concept of an internally
represented grammar, has produced the solid "conceptual
cognitive revolution".

2) For a rationalist cognitive scientist, a grammar is a finite
mental object, systematically assigning abstract structures to
all the well-formed expressions of a language --that is, to each
member of a set that, for natural languages (such as Chinese or
Italian), is infinite and discrete. Infinite, because every
speaker of a language can produce and understand an unlimited
number of new grammatical sentences. Discrete, because
continuous modification of a sentence to change it into another
is impossible. No sentence could be halfway between "It's a good
car, but they don't sell it" and "It's a good car, but they
don't tell it."

3) A grammar capable of generating complex structures for all
well-formed sentences of a natural language must have recursive
rules, because phrasal constituents can contain other phrasal
constituents of the same or higher kinds ("The young doctor's
three beautiful sisters" is a noun phrase containing another
noun phrase; "The spy who came in from the cold" is a noun
phrase containing a sentence). Moreover, structural rules of
sentence formation can be applied recursively to embed relative
clauses embedding other relative clauses, without limit (as in
"This is the cat that killed the rat that ate the malt that lay
in the house that Jack built"). Because such grammars are
finite, whereas the languages they generate are infinite and
contingently shaped by use, it is advantageous, and
methodologically cogent, to consider the concept of grammar as
primary, and that of language as derived.

4) Since the mid-1950s, powerful formal criteria, derived from
analysis of the artificial languages of mathematics and computer
programming, have been applied to the study of natural languages
to determine principles by which a given class of grammars can
generate a given target language. A universal ('Chomsky')
hierarchy of grammars (automata) was established: the most
powerful class contains as a subclass the immediately less
powerful one, and so on. In tune with the dominant
empiricist-inductivist tradition of the 1950s, the first
grammars to be explored at the lowest level in the hierarchy
were probabilistic and finite-state. From a very large corpus of
ascertained utterances of the language, one can compute the
conditional probability that a word (or string of words) will
follow another.

References:

1. Wasow, T. in Foundations of Cognitive Science (ed. Posner,
M.) 161-205 (MIT Press, Cambridge, Massachusetts, 1991)

2. Chomsky, N. The Minimalist Program (MIT Press, Cambridge,
Massachusetts, 1995)

3. Pullum, G. K. & Scholtz, B. C. Nature 413, 367 (2001)

Nature 2002 416:129

Web Links: neurolinguistics Chomsky

Related Background Brief:

MORE THAN WORDS. In the popular view, a language is merely a
fixed stock of words. Purists worry about foreign loanwords;
conservatives decry slang; and groundless claims that there are
hundreds of Eskimo words for snow are constantly made in popular
writing, as if nothing matters about languages but their
lexicons. But the popular view cannot be right, because (as
linguist Paul Postal has observed) membership in the word stock
of a natural language is open. Consider this example: "GM's new
Zabundra makes even the massive Ford Expedition look
economical." If English had an antecedently given set of words,
then this expression would not be an English sentence at all,
because 'Zabundra' is not a word (we just invented it). Yet the
sentence is not just grammatical English, it is readily
interpretable (it clearly implies that the Zabundra is a large,
fuel-hungry sports utility vehicle produced by General Motors).
Similar points could be made regarding word borrowing, personal
names, scientific nomenclature, onomatopoeisis, acronyms, loaned
words, and so on; English is not a fixed set of words. A more
fundamental reason that a language cannot just be a word stock
is that expressions have syntactic structure. For example, in
most languages, the order of words can be significant: "Mohammed
will come to the mountain" contains the same words as "The
mountain will come to Mohammed", but the expressions are very
different. Geoffrey K. Pullum: Nature 2001 413:367.

Related Background:

ON THE ACQUISITION OF LANGUAGE BY CHILDREN

J.R. Saffran et al (University of Wisconsin Madison, US) discuss
the acquisition of language by children, the authors making the
following points:

1) Before infants can begin to map words onto objects in the
world, they must determine which sound sequences are words. To
do so, infants must uncover at least some of the units that
belong to their native language from a largely continuous stream
of sounds in which words are seldom surrounded by pauses.
Despite the difficulty of this reverse-engineering problem,
infants successfully segment words from fluent speech from
approximately 7 months of age.

2) How do infants learn the units of their native language so
rapidly? One fruitful approach to answering this question has
been to present infants with miniature artificial languages that
embody specific aspects of natural language structure. Once an
infant has been familiarized with a sample of this language, a
new sample, or a sample from a different language, is presented
to the infant. Subtle measures of surprise (e.g., duration of
looking toward the new sounds) are then used to assess whether
the infant perceives the new sample as more of the same or
something different. In this fashion, we can ask what the infant
extracted from the artificial language, which can lead to
insights regarding the learning mechanisms underlying the
earliest stages of language acquisition.

3) Syllables that are part of the same word tend to follow one
another predictably, whereas syllables that span word boundaries
do not. In a series of experiments, it has been found that
infants can detect and use the statistical properties of
syllable co-occurrence to segment novel words. More
specifically, infants do not detect merely how frequently
syllable pairs occur, but rather the probabilities with which
one syllable predicts another. Thus, infants may find word
boundaries by detecting syllable pairs with low transitional
probabilities. What makes this finding astonishing is that
infants as young as 8 months begin to perform these computations
with as little as 2 minutes of exposure. By soaking up the
statistical regularities of seemingly meaningless acoustic
events, infants are able to rapidly structure linguistic input
into relevant and ultimately meaningful units.

Proc. Nat. Acad. Sci. 2001 98:12874

ScienceWeek http://www.scienceweek.com

=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=


Top Books - Behavioral Sciences
http://www.amazon.com/exec/obidos/redirect?tag=darwinanddarwini&path=tg/browse/-/226685
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From hubeyh at mail.montclair.edu  Mon Oct 21 14:06:05 2002
From: hubeyh at mail.montclair.edu (H.M. Hubey)
Date: Mon, 21 Oct 2002 10:06:05 -0400
Subject: [language] Re: 13.2704, Disc: Darwinism & Evolution of Lang
Message-ID: <MON.21.OCT.2002.100605.0400.HUBEYH@MAIL.MONTCLAIR.EDU>

<><><><><><><><><><><><>--This is the Language List--<><><><><><><><><><><><><>




Hubey  wrote:

>Actually this is simply a matter of definition. It goes like this:
>
>random + deterministic = random
>random*deterministic = random
>
>The former is "additive noise" and the latter "multiplicative noise",
>and both are random. "Random" is not equal to "uniforrmly random",
>thus one can find broadbrush patterns in random phenomena.
>
>

Looking at the Eonix page where I read:

At a time when theories of evolution are undergoing renewed controversy,
discussion is hampered by the remoteness of the phenomenon of evolution,
and the use of indirect inference to speculate about natural selection
in processes that have never been observed. Adherents of Darwinism often
defend textbook versions of the theory that have, in any case, often
been held in question. The assumption that evolution occurs, and must
occur,  by random mutation and (non-random) natural selection  is the
crux of the dispute, and one unreasonably confused with issues of
religion and secularization.  The demonstration of non-random evolution
in the eonic effect must severely caution Darwin's incomplete theory.


I realize I have to expand what I wrote.

It is best explained via equations. Let f(t) be some function of time,
and r(t) be a random function of time.
Then if  y(t)= r(t)*f(t) and x(t)=r(t)+f(t), both x(t), and y(t) are
random processes. This is due simply to
definition of randomness.  To apply directly to evolution, let the
"evolution" of something very simple
be given by the equation

                   dz(t)/dt + a(t)*z(t) = f(t)

This is the simplest, first-order, linear, ordinary differential
equation and has a solution in the most
general case i.e. a(t) is a function of time (not constant).  Here, a(t)
is a coefficient of the DE, and
f(t) is known as the "forcing function" or "source term". The reason for
it is physical. The DE can be
solved without f(t) and that is known as the homogeneous solution. It is
an exponentially decaying
solution, that is,  it goes to zero as time goes to infinity. However,
if f(t), say, is sin(t), then this sinusoidal
function "drives" the system (e.g. the value of z(t)) in the sense that
it does not go to zero. That is
why f(t) is also known as "forcing". It forces the system to behave in a
way that it would not behave
if left alone. In other words, without f(t) the equation describes the
behavior of the system itself, and
f(t) is then considered external to the system but which obviously
affects the behavior of the system.

Rewrite it as L(t)z(t)=f(t) where L(t) is a (linear) operator.
Obviously, L(t) is nothing more that
d/dt + a(t).  This "operates" on the system (i.e. z(t)). To generalize,
suppose z(t) is now a vector.
It is a set of variables. This particular way of looking at a system is
that z(t) is a set of variables that
describes the system, and at any time the specific values of these
variables is the "state" of the system.
So, we can think of evolutionary states in similar ways. For example, it
could be 30,000 dimensions
for humans. That is the state of each gene. Or better yet, let the state
of the system be 30,000*N where
N is the number of humans in the world. Then the "state" is the set of
all genes of all humans.

So then if a(t) (which is also a vector) is random, then the operator
L(t) now generates random
mutations in the gene pool of humanity.  And here is the crux of the
matter: the f(t)  now
determines which direction evolution moves by forcing the system state
to some direction.
So mathematically we now have a description. Caveats:

1. It is linear and simple. Real evolution is likely not. But the
mathematical description can be
extended to nonlinearity easily.

2. Both mutation (a(t)) and environment (f(t)) are now part of the
description.

3. Because the solution is a function of both f(t), and a(t) it is still
random.

4. We see that f(t) models environment, however, nobody can predict the
environment. As
Bohr quipped "prediction is difficult, especially the future". So
unfortunately, f(t) must also
be thought of as a random variable.  The effects of f(t) at any time is
direct, but knowing what
it is or will be, mathematically it must be modeled as a random
variable. The difference is this:
a(t) acts on a fast scale, but f(t) acts on a slower scale.



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From hubeyh at mail.montclair.edu  Thu Oct 24 01:41:51 2002
From: hubeyh at mail.montclair.edu (H.M. Hubey)
Date: Wed, 23 Oct 2002 21:41:51 -0400
Subject: [language] Re: 13.2735,
 Disc: 2nd to Last Posting - Darwinism & Evolution
Message-ID: <WED.23.OCT.2002.214151.0400.HUBEYH@MAIL.MONTCLAIR.EDU>

<><><><><><><><><><><><>--This is the Language List--<><><><><><><><><><><><><>




Nemonemini at aol.com wrote:

>
>
>

I  feel that I should make an attempt to respond to three emails of  Mr.
Landon. But I will have
to be brief because this is an extremely complex topic.  However, for
those interested in
related topics I will point out this reference:

Hubey, H.M. "Evolution of Intelligence: Direct modeling....",
Kybernetes, Vol 31, No. 3/4, 2002.


>Date:  Mon, 21 Oct 2002 22:09:07 EDT
>From:  Nemonemini at aol.com
>Subject:  Re: 13.2704, Disc: Darwinism & Evolution of Lang
>
>
>
>>Actually this is simply a matter of definition. It goes like this:
>>
>>random + deterministic = random
>>random*deterministic = random
>>
>>The former is "additive noise" and the latter "multiplicative noise",
>>and both are random. "Random" is not equal to "uniforrmly random",
>>thus one can find broadbrush patterns in random phenomena.
>>
>>
>>
>
>I recommend a good biography of Isaac Newton, when you start trying to
>figure out both reality and the methodology with respect to reality at
>the same time. You will discover that science as we know is monkey see
>monkey do plus ingenious math tricks.
>

I think I know a lot more about science than you imagine and I see no
reason to pursue this line. You are
welcome to join the "Language" list and pursue your arguments there. To
join, send email to this address:
majordomo at csam-lists.montclair.edu, and in the body of the message put
"subscribe language" (without the
quotes).

What is randomness and what is deterministic is very clearly defined and
I wrote it above. No need
for confusion.


>-------------------------------- Message 2 -------------------------------
>
>Date:  Mon, 21 Oct 2002 23:43:49 EDT
>From:  Nemonemini at aol.com
>Subject:  Re: 13.2721, Disc: Darwinism & Evolution of Lang
>
>
>RE Linguist 13.2721
>
>
>
>>I realize I have to expand what I wrote.
>>
>>It is best explained via equations. Let f(t) be some function of time,
>>and r(t) be a random function of time.  Then if y(t)= r(t)*f(t) and
>>x(t)=r(t)+f(t), both x(t), and y(t) are random processes. This is due
>>simply to definition of randomness.  To apply directly to evolution,
>>let the "evolution" of something very simple be given by the equation
>>
>>          dz(t)/dt + a(t)*z(t) = f(t)
>>
>>
>>
>
>I will study this but I should say that....
>

>The flaw, to me, can be seen in the old edition of Hartl
>on pop gen, where the 'force' is brought in relation to natural
>selection. The problem is that natural selection is not a force, and
>evolution is not physics, and the equations don't really explain
>evolution.  What is this 'force', i.e. what's our game in terms of
>foundational concepts?  We can't say until we have the facts.
>

I explained all this. And soon I will be working on "population
genetics" models with a colleague whose
specialty is dynamics (in fact, population dynamics) and we will be
working on language evolution.
To add more, the  partial differential equations for the pdf (prob
density function) of random processes
are the so-called diffusion models, and I have already done work in this
field and understand it
reasonably well. You can also read about them in both of these books:

Hubey, H.M. 1999, Mathematical Foundations of Linguistics, Lincom Europa
Hubey, H.M. 1994 Mathematical and Computational Linguistics, Mir Domu
Tvoemu, Moscow
(this later published by Lincom Europa in 1999).

Longer and more detailed versions of these books (with examples and
exercises suitable for
teaching) will be eventually published.

>Consider finally a puzzle. The pieces are scrambled, and seem
>random. Then we see the relation of certain pieces to each other, and
>solve a corner of the puzzle. Then we see the pieces cohere, and are
>not random.  Thus my usage is quite ordinary, and takes common sense
>derivations. The term random is hardly a numerical issue at all.
>
It can't be anything else. It is exactly because it is so difficult to
explain that so much precision and
detail are needed. There are tons of mathematical materials on
probability theory, stochastic processes,
plausible reasoning (See Jaynes' book), etc. It is a very rich field and
also very powerful. Today most of
datamining, model-building, search for patterns, knowledge discovery are
all tied together within the
view point of probability theory, namely Bayesian reasoning. See the
book by Hastie, Tibshirani, and
Feldman for the last 30 years of research in this field.


>------------------------
>
>Date:  Tue, 22 Oct 2002 08:02:18 EDT
>From:  Nemonemini at aol.com
>Subject:  Re: 13.2721, Disc: Darwinism & Evolution of Lang
>
>Re: Linguist 13.2721
>
>
>
>>It is best explained via equations. Let f(t) be some function of time,
>>and r(t) be a random function of time.  Then if y(t)= r(t)*f(t) and
>>x(t)=r(t)+f(t), both x(t), and y(t) are random processes. This is due
>>simply to definition of randomness.  To apply directly to evolution,
>>let the "evolution" of something very simple be given by the equation
>>
>>          dz(t)/dt + a(t)*z(t) = f(t)
>>
>>
>>
>
>??? And? To rescue the idea of randomness here in this fashion seems
>beside the point.
>

Randomness needs no rescue. Perhaps those who do not understand it might
wish to be rescued.

>
>


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From hubeyh at mail.montclair.edu  Sat Oct 19 22:32:14 2002
From: hubeyh at mail.montclair.edu (H.M. Hubey)
Date: Sat, 19 Oct 2002 18:32:14 -0400
Subject: [language] Darwinism & Evolution of Language]
Message-ID: <SAT.19.OCT.2002.183214.0400.HUBEYH@MAIL.MONTCLAIR.EDU>

<><><><><><><><><><><><>--This is the Language List--<><><><><><><><><><><><><>


  Did this show up on the LinguistList mail?


-------- Original Message --------
Subject: Re: 13.2673, Disc: Darwinism & Evolution of Language
Date: Thu, 17 Oct 2002 11:41:29 -0400
From: "H.M. Hubey" <hubeyh at mail.montclair.edu>
To: linguist at linguistlist.org
References: <20021017031623.19762.qmail at linguistlist.org>



LINGUIST List wrote:

>
>Date:  Wed, 16 Oct 2002 12:43:47 EDT
>From:  Nemonemini at aol.com
>Subject:  Re: 13.2645, Disc: Darwinism & Evolution of Language
>
>
>Part of your argument is invalid; evolution as seen by Darwinism or
>Evolution Theory is not simply "random genetic evolution", but
>mutation AND selection. The former is random, the latter obviously
>not.
>
>
>
Actually this is simply a matter of definition. It goes like this:

random + deterministic = random
random*deterministic = random

The former is "additive noise" and the latter "multiplicative noise",
and both are random. "Random" is not
equal to "uniforrmly random", thus one can find broadbrush patterns in
random phenomena.





---<><><><><><><><><><><><>----Language----<><><><><><><><><><><><><>
Copyrights/"Fair Use":  http://www.templetons.com/brad/copymyths.html
The "fair use" exemption to copyright law was created to allow things
such as commentary, parody, news reporting, research and education
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Intent, and damage to the commercial value of the work are
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From hubeyh at mail.montclair.edu  Mon Oct 21 00:42:52 2002
From: hubeyh at mail.montclair.edu (H.M. Hubey)
Date: Sun, 20 Oct 2002 20:42:52 -0400
Subject: [language] [Fwd: [evol-psych] On Grammar vs. Language in
 Neurolinguistics]
Message-ID: <SUN.20.OCT.2002.204252.0400.HUBEYH@MAIL.MONTCLAIR.EDU>

<><><><><><><><><><><><>--This is the Language List--<><><><><><><><><><><><><>




-------- Original Message --------
Subject: [evol-psych] On Grammar vs. Language in Neurolinguistics
Date: Fri, 18 Oct 2002 18:26:56 -0500
From: Ian Pitchford <ian.pitchford at scientist.com>
Reply-To: Ian Pitchford <ian.pitchford at scientist.com>
Organization: http://human-nature.com/
To: evolutionary-psychology at yahoogroups.com



_________________________________________________________________


 From Scienceweek October 4, 2002 Vol.6 Number 40


=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=

5. On Grammar vs. Language in Neurolinguistics

Massimo Piattelli-Palmarini (University of Arizona, US) discusses
grammar vs. language, the author making the following points:

1) Two styles of explaining the science of mind and behavior
have been competing for as long as anyone cares to remember:
empiricist, centering on habit formation, statistical learning,
imitation and association; and rationalist, focusing on the
projection of internally represented rules. Despite relentless
effort, the former has delivered rather meager results, whereas
the latter, with its pivotal concept of an internally
represented grammar, has produced the solid "conceptual
cognitive revolution".

2) For a rationalist cognitive scientist, a grammar is a finite
mental object, systematically assigning abstract structures to
all the well-formed expressions of a language --that is, to each
member of a set that, for natural languages (such as Chinese or
Italian), is infinite and discrete. Infinite, because every
speaker of a language can produce and understand an unlimited
number of new grammatical sentences. Discrete, because
continuous modification of a sentence to change it into another
is impossible. No sentence could be halfway between "It's a good
car, but they don't sell it" and "It's a good car, but they
don't tell it."

3) A grammar capable of generating complex structures for all
well-formed sentences of a natural language must have recursive
rules, because phrasal constituents can contain other phrasal
constituents of the same or higher kinds ("The young doctor's
three beautiful sisters" is a noun phrase containing another
noun phrase; "The spy who came in from the cold" is a noun
phrase containing a sentence). Moreover, structural rules of
sentence formation can be applied recursively to embed relative
clauses embedding other relative clauses, without limit (as in
"This is the cat that killed the rat that ate the malt that lay
in the house that Jack built"). Because such grammars are
finite, whereas the languages they generate are infinite and
contingently shaped by use, it is advantageous, and
methodologically cogent, to consider the concept of grammar as
primary, and that of language as derived.

4) Since the mid-1950s, powerful formal criteria, derived from
analysis of the artificial languages of mathematics and computer
programming, have been applied to the study of natural languages
to determine principles by which a given class of grammars can
generate a given target language. A universal ('Chomsky')
hierarchy of grammars (automata) was established: the most
powerful class contains as a subclass the immediately less
powerful one, and so on. In tune with the dominant
empiricist-inductivist tradition of the 1950s, the first
grammars to be explored at the lowest level in the hierarchy
were probabilistic and finite-state. From a very large corpus of
ascertained utterances of the language, one can compute the
conditional probability that a word (or string of words) will
follow another.

References:

1. Wasow, T. in Foundations of Cognitive Science (ed. Posner,
M.) 161-205 (MIT Press, Cambridge, Massachusetts, 1991)

2. Chomsky, N. The Minimalist Program (MIT Press, Cambridge,
Massachusetts, 1995)

3. Pullum, G. K. & Scholtz, B. C. Nature 413, 367 (2001)

Nature 2002 416:129

Web Links: neurolinguistics Chomsky

Related Background Brief:

MORE THAN WORDS. In the popular view, a language is merely a
fixed stock of words. Purists worry about foreign loanwords;
conservatives decry slang; and groundless claims that there are
hundreds of Eskimo words for snow are constantly made in popular
writing, as if nothing matters about languages but their
lexicons. But the popular view cannot be right, because (as
linguist Paul Postal has observed) membership in the word stock
of a natural language is open. Consider this example: "GM's new
Zabundra makes even the massive Ford Expedition look
economical." If English had an antecedently given set of words,
then this expression would not be an English sentence at all,
because 'Zabundra' is not a word (we just invented it). Yet the
sentence is not just grammatical English, it is readily
interpretable (it clearly implies that the Zabundra is a large,
fuel-hungry sports utility vehicle produced by General Motors).
Similar points could be made regarding word borrowing, personal
names, scientific nomenclature, onomatopoeisis, acronyms, loaned
words, and so on; English is not a fixed set of words. A more
fundamental reason that a language cannot just be a word stock
is that expressions have syntactic structure. For example, in
most languages, the order of words can be significant: "Mohammed
will come to the mountain" contains the same words as "The
mountain will come to Mohammed", but the expressions are very
different. Geoffrey K. Pullum: Nature 2001 413:367.

Related Background:

ON THE ACQUISITION OF LANGUAGE BY CHILDREN

J.R. Saffran et al (University of Wisconsin Madison, US) discuss
the acquisition of language by children, the authors making the
following points:

1) Before infants can begin to map words onto objects in the
world, they must determine which sound sequences are words. To
do so, infants must uncover at least some of the units that
belong to their native language from a largely continuous stream
of sounds in which words are seldom surrounded by pauses.
Despite the difficulty of this reverse-engineering problem,
infants successfully segment words from fluent speech from
approximately 7 months of age.

2) How do infants learn the units of their native language so
rapidly? One fruitful approach to answering this question has
been to present infants with miniature artificial languages that
embody specific aspects of natural language structure. Once an
infant has been familiarized with a sample of this language, a
new sample, or a sample from a different language, is presented
to the infant. Subtle measures of surprise (e.g., duration of
looking toward the new sounds) are then used to assess whether
the infant perceives the new sample as more of the same or
something different. In this fashion, we can ask what the infant
extracted from the artificial language, which can lead to
insights regarding the learning mechanisms underlying the
earliest stages of language acquisition.

3) Syllables that are part of the same word tend to follow one
another predictably, whereas syllables that span word boundaries
do not. In a series of experiments, it has been found that
infants can detect and use the statistical properties of
syllable co-occurrence to segment novel words. More
specifically, infants do not detect merely how frequently
syllable pairs occur, but rather the probabilities with which
one syllable predicts another. Thus, infants may find word
boundaries by detecting syllable pairs with low transitional
probabilities. What makes this finding astonishing is that
infants as young as 8 months begin to perform these computations
with as little as 2 minutes of exposure. By soaking up the
statistical regularities of seemingly meaningless acoustic
events, infants are able to rapidly structure linguistic input
into relevant and ultimately meaningful units.

Proc. Nat. Acad. Sci. 2001 98:12874

ScienceWeek http://www.scienceweek.com

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From hubeyh at mail.montclair.edu  Mon Oct 21 14:06:05 2002
From: hubeyh at mail.montclair.edu (H.M. Hubey)
Date: Mon, 21 Oct 2002 10:06:05 -0400
Subject: [language] Re: 13.2704, Disc: Darwinism & Evolution of Lang
Message-ID: <MON.21.OCT.2002.100605.0400.HUBEYH@MAIL.MONTCLAIR.EDU>

<><><><><><><><><><><><>--This is the Language List--<><><><><><><><><><><><><>




Hubey  wrote:

>Actually this is simply a matter of definition. It goes like this:
>
>random + deterministic = random
>random*deterministic = random
>
>The former is "additive noise" and the latter "multiplicative noise",
>and both are random. "Random" is not equal to "uniforrmly random",
>thus one can find broadbrush patterns in random phenomena.
>
>

Looking at the Eonix page where I read:

At a time when theories of evolution are undergoing renewed controversy,
discussion is hampered by the remoteness of the phenomenon of evolution,
and the use of indirect inference to speculate about natural selection
in processes that have never been observed. Adherents of Darwinism often
defend textbook versions of the theory that have, in any case, often
been held in question. The assumption that evolution occurs, and must
occur,  by random mutation and (non-random) natural selection  is the
crux of the dispute, and one unreasonably confused with issues of
religion and secularization.  The demonstration of non-random evolution
in the eonic effect must severely caution Darwin's incomplete theory.


I realize I have to expand what I wrote.

It is best explained via equations. Let f(t) be some function of time,
and r(t) be a random function of time.
Then if  y(t)= r(t)*f(t) and x(t)=r(t)+f(t), both x(t), and y(t) are
random processes. This is due simply to
definition of randomness.  To apply directly to evolution, let the
"evolution" of something very simple
be given by the equation

                   dz(t)/dt + a(t)*z(t) = f(t)

This is the simplest, first-order, linear, ordinary differential
equation and has a solution in the most
general case i.e. a(t) is a function of time (not constant).  Here, a(t)
is a coefficient of the DE, and
f(t) is known as the "forcing function" or "source term". The reason for
it is physical. The DE can be
solved without f(t) and that is known as the homogeneous solution. It is
an exponentially decaying
solution, that is,  it goes to zero as time goes to infinity. However,
if f(t), say, is sin(t), then this sinusoidal
function "drives" the system (e.g. the value of z(t)) in the sense that
it does not go to zero. That is
why f(t) is also known as "forcing". It forces the system to behave in a
way that it would not behave
if left alone. In other words, without f(t) the equation describes the
behavior of the system itself, and
f(t) is then considered external to the system but which obviously
affects the behavior of the system.

Rewrite it as L(t)z(t)=f(t) where L(t) is a (linear) operator.
Obviously, L(t) is nothing more that
d/dt + a(t).  This "operates" on the system (i.e. z(t)). To generalize,
suppose z(t) is now a vector.
It is a set of variables. This particular way of looking at a system is
that z(t) is a set of variables that
describes the system, and at any time the specific values of these
variables is the "state" of the system.
So, we can think of evolutionary states in similar ways. For example, it
could be 30,000 dimensions
for humans. That is the state of each gene. Or better yet, let the state
of the system be 30,000*N where
N is the number of humans in the world. Then the "state" is the set of
all genes of all humans.

So then if a(t) (which is also a vector) is random, then the operator
L(t) now generates random
mutations in the gene pool of humanity.  And here is the crux of the
matter: the f(t)  now
determines which direction evolution moves by forcing the system state
to some direction.
So mathematically we now have a description. Caveats:

1. It is linear and simple. Real evolution is likely not. But the
mathematical description can be
extended to nonlinearity easily.

2. Both mutation (a(t)) and environment (f(t)) are now part of the
description.

3. Because the solution is a function of both f(t), and a(t) it is still
random.

4. We see that f(t) models environment, however, nobody can predict the
environment. As
Bohr quipped "prediction is difficult, especially the future". So
unfortunately, f(t) must also
be thought of as a random variable.  The effects of f(t) at any time is
direct, but knowing what
it is or will be, mathematically it must be modeled as a random
variable. The difference is this:
a(t) acts on a fast scale, but f(t) acts on a slower scale.



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From hubeyh at mail.montclair.edu  Thu Oct 24 01:41:51 2002
From: hubeyh at mail.montclair.edu (H.M. Hubey)
Date: Wed, 23 Oct 2002 21:41:51 -0400
Subject: [language] Re: 13.2735,
 Disc: 2nd to Last Posting - Darwinism & Evolution
Message-ID: <WED.23.OCT.2002.214151.0400.HUBEYH@MAIL.MONTCLAIR.EDU>

<><><><><><><><><><><><>--This is the Language List--<><><><><><><><><><><><><>




Nemonemini at aol.com wrote:

>
>
>

I  feel that I should make an attempt to respond to three emails of  Mr.
Landon. But I will have
to be brief because this is an extremely complex topic.  However, for
those interested in
related topics I will point out this reference:

Hubey, H.M. "Evolution of Intelligence: Direct modeling....",
Kybernetes, Vol 31, No. 3/4, 2002.


>Date:  Mon, 21 Oct 2002 22:09:07 EDT
>From:  Nemonemini at aol.com
>Subject:  Re: 13.2704, Disc: Darwinism & Evolution of Lang
>
>
>
>>Actually this is simply a matter of definition. It goes like this:
>>
>>random + deterministic = random
>>random*deterministic = random
>>
>>The former is "additive noise" and the latter "multiplicative noise",
>>and both are random. "Random" is not equal to "uniforrmly random",
>>thus one can find broadbrush patterns in random phenomena.
>>
>>
>>
>
>I recommend a good biography of Isaac Newton, when you start trying to
>figure out both reality and the methodology with respect to reality at
>the same time. You will discover that science as we know is monkey see
>monkey do plus ingenious math tricks.
>

I think I know a lot more about science than you imagine and I see no
reason to pursue this line. You are
welcome to join the "Language" list and pursue your arguments there. To
join, send email to this address:
majordomo at csam-lists.montclair.edu, and in the body of the message put
"subscribe language" (without the
quotes).

What is randomness and what is deterministic is very clearly defined and
I wrote it above. No need
for confusion.


>-------------------------------- Message 2 -------------------------------
>
>Date:  Mon, 21 Oct 2002 23:43:49 EDT
>From:  Nemonemini at aol.com
>Subject:  Re: 13.2721, Disc: Darwinism & Evolution of Lang
>
>
>RE Linguist 13.2721
>
>
>
>>I realize I have to expand what I wrote.
>>
>>It is best explained via equations. Let f(t) be some function of time,
>>and r(t) be a random function of time.  Then if y(t)= r(t)*f(t) and
>>x(t)=r(t)+f(t), both x(t), and y(t) are random processes. This is due
>>simply to definition of randomness.  To apply directly to evolution,
>>let the "evolution" of something very simple be given by the equation
>>
>>          dz(t)/dt + a(t)*z(t) = f(t)
>>
>>
>>
>
>I will study this but I should say that....
>

>The flaw, to me, can be seen in the old edition of Hartl
>on pop gen, where the 'force' is brought in relation to natural
>selection. The problem is that natural selection is not a force, and
>evolution is not physics, and the equations don't really explain
>evolution.  What is this 'force', i.e. what's our game in terms of
>foundational concepts?  We can't say until we have the facts.
>

I explained all this. And soon I will be working on "population
genetics" models with a colleague whose
specialty is dynamics (in fact, population dynamics) and we will be
working on language evolution.
To add more, the  partial differential equations for the pdf (prob
density function) of random processes
are the so-called diffusion models, and I have already done work in this
field and understand it
reasonably well. You can also read about them in both of these books:

Hubey, H.M. 1999, Mathematical Foundations of Linguistics, Lincom Europa
Hubey, H.M. 1994 Mathematical and Computational Linguistics, Mir Domu
Tvoemu, Moscow
(this later published by Lincom Europa in 1999).

Longer and more detailed versions of these books (with examples and
exercises suitable for
teaching) will be eventually published.

>Consider finally a puzzle. The pieces are scrambled, and seem
>random. Then we see the relation of certain pieces to each other, and
>solve a corner of the puzzle. Then we see the pieces cohere, and are
>not random.  Thus my usage is quite ordinary, and takes common sense
>derivations. The term random is hardly a numerical issue at all.
>
It can't be anything else. It is exactly because it is so difficult to
explain that so much precision and
detail are needed. There are tons of mathematical materials on
probability theory, stochastic processes,
plausible reasoning (See Jaynes' book), etc. It is a very rich field and
also very powerful. Today most of
datamining, model-building, search for patterns, knowledge discovery are
all tied together within the
view point of probability theory, namely Bayesian reasoning. See the
book by Hastie, Tibshirani, and
Feldman for the last 30 years of research in this field.


>------------------------
>
>Date:  Tue, 22 Oct 2002 08:02:18 EDT
>From:  Nemonemini at aol.com
>Subject:  Re: 13.2721, Disc: Darwinism & Evolution of Lang
>
>Re: Linguist 13.2721
>
>
>
>>It is best explained via equations. Let f(t) be some function of time,
>>and r(t) be a random function of time.  Then if y(t)= r(t)*f(t) and
>>x(t)=r(t)+f(t), both x(t), and y(t) are random processes. This is due
>>simply to definition of randomness.  To apply directly to evolution,
>>let the "evolution" of something very simple be given by the equation
>>
>>          dz(t)/dt + a(t)*z(t) = f(t)
>>
>>
>>
>
>??? And? To rescue the idea of randomness here in this fashion seems
>beside the point.
>

Randomness needs no rescue. Perhaps those who do not understand it might
wish to be rescued.

>
>


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