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What
is new in those “astonishing hypotheses?”
Materialism, the doctrine that everything
is physical and the world contains no soul substance or vital
spirit, has a long root in Western thinking that reaches back to
the pre-Socratics. In the eighteenth century, when people
worried strongly about the immortal soul, Joseph Priestley
argued, "the powers of sensation or perception, and thought, as
belonging to man, have never been found but in conjunction with
certain organized system of matter... [W]e could not but have
concluded, that in man thought is a property of the nervous
system, or rather of the brain."1 Priestley did no
lack supporters in the debate about mind and matter. Opposition
weakened with secularization and scientific progress. At the
end of the nineteenth century, the young Sigmund Freud reflected
the intellectual climate of his time in the opening declaration
of his A Project for Scientific Psychology: "The intention is to
furnish a psychology that shall be a natural science; that is,
to represent psychical processes as quantitatively determinate
state of specifiable material particles."
Strangely, by the end of the twentieth
century, materialism, the old commonplace, becomes a bold and
novel proposition. Looking at the scholarly literature, one
cannot help sharing the puzzle of Noam Chomsky, who observed:
"Every year or two a book appears by some distinguished
scientist with the 'startling conclusion' or 'astonishing
hypothesis' that thought in humans 'is a property of the nervous
system, or rather of the brain.'”2 What has gone
wrong with our times?
Francis Crick, who with James Watson
discovered the DNA structure, has turned to neuroscience. In a
popular book, he advanced what he called an "astonishing
hypothesis." "You, your joys and your sorrows, your memories
and your ambitions, your sense of personal identity and free
will, are in fact no more than the behavior of a vast assembly
of nerve cells and their associated molecules." In short:
"You're nothing but a pack of neurons."3 Nothing
but, that is the astonishing element that our age adds to
the time-honored materialism.
The claim that a thing is nothing but
something else is the crux of ideological reductionism, an
influential philosophical doctrine in this century, although its
prestige is being increasingly challenged, especially in the
study of complex systems. I prefer to call it nothing-butism,
to avoid confusion with the more productive meanings of
reduction as analysis or decomposition. Nothing-butism asserts
that a complex system is no more than its parts, so that we
understand the system exhaustively once we understand how its
parts behave. You are nothing but a pack of neurons, therefore
scientists can predict all your joys and sorrows once they know
how neurons work.
Eliminativism is the name of nothing-butism
in the philosophy of mind. It denies the possibility that
high-level properties that we ordinarily describe in mental
terms can emerge from the complex large-scale organization of
neurons. Banning emergence, mental properties become nothing
but neural properties. The mental vocabulary that we use in our
daily life should either be identified with neural terminology
or be eliminated from our "scientific" discourse.
For a while, nothing-butism seemed to make
sense. In the 1950s, cells specialized for certain functions
are discovered. For instance, the frog's retina contains cells
that are sensitive to the movement of small objects in a wide
range of illumination. These cells are called "bug detectors,"
although frogs do not differentiate bugs from other small moving
objects. David Marr recalled what happened: "People were able
to determine the functions of single elements of the brain.
There seemed no reason why the reductionist approach could not
be taken all the way." In their excitement, people speculated
that vision could be explained in terms of the excitation of
specialized neurons, for example the fabled "grandmother neuron"
that fires only when one's grandmother comes into sight. The
mind-brain identity thesis advocating "pain is nothing but
c-fiber firing" appeared around this time. Although the
philosophy survives today, the scientific excitement died
quickly as scientists realized that the behaviors of individual
neurons give no clue to how they contribute to vision as a
whole.
Neurons sensitive only to a specific
feature exist. Nevertheless, their behaviors are influenced by
mental behaviors such as an animal’s attention. For instance, a
direction-sensitive neuron fires only if its receptive field
contains an object moving from right to left. Experimentalists
use microelectrodes to measure the activity of a single neuron
in a monkey trained to attend to only one of two objects in its
view. The two objects move horizontally in opposite
directions. If the direction-sensitive neuron operates
autonomously, it should always respond in the same way, because
one of the two objects moves in its preferred direction. This
is not what happens. The neuron responds strongly only if the
attended-to object moved from in its preferred direction. Its
response is markedly weaker if the ignored object moved its
preferred direction. Attention and other global mental
properties made a big difference on the behavior of individual
neurons. Similar results obtain in many other experiments. They
refute nothing-butism, which, by dismissing mental effects such
as attention, makes a total mystery of why the
direction-sensitive neuron responds differently to the same
stimuli.
Are
you controlled by selfish genes?
The most famous version of nothing-butism
in genetics is Richard Dawkins's doctrine of the "selfish gene"
that features genes each fighting for its own benefits. The
organism as a whole is nothing but a dead vehicle controlled by
the selfish genes. Curiously, the genes are defined not by
their molecular structures but by their functions, as a gene for
intelligence or a gene for sexual preference. The gene for
homosexuality fends for itself regardless of other genes, the
structures of the genome, and the conditions of the organism.
The selfish gene story is popular because it is easy to
understand. Easiness, however, does not imply correctness.
Scientists occasionally find genes for
specific functions, but these are exceptions rather than the
rule. The most promising cases for selfish genes are the genes
for diseases; it is always easier for a single gene to foul
things up and lonely culprits are easiest for researchers to
track down. Even so, they are in the minority. Of some 5,000
known genetic disorders, only a fraction are attributable to
single genes, for examples cystic fibrosis and Huntington's
disease. Even in these cases, researchers warn that the
single-gene mutants are informative only when pursued within the
framework of gene linkage.
For more complex diseases, enthusiastic
genetic announcements often fall apart under scrutiny. Such is
the fate of the gene for schizophrenia and the gene for manic
depression. In the overview of a special issue on genetic
disease of the journal Current Opinions in Genetics and
Development, the editors wrote: "Complexity is now the watchword
of the day in disease genetics. . . . Even the apparent
simplicity of single-gene disorders is being clouded by the
specter of modifier genes that can influence disease
susceptibility, severity, or progression."4
It takes only a malfunctioning part to
disable a car, but many things must work to make the car run
properly. Traits for normal behaviors are more complicated than
diseases. Chances are much smaller that there are specific
genes for traits such as intelligence or sexual preference. The
gay gene or the gene for male homosexuality, announced with
fanfare in 1993, turns out to be a house of cards. It is almost
the rule that many genes contribute to a trait, and a gene is
expressed in many functions. Gene expression is a complicated
process involving many epigenetic and environmental factors.
All these are ignored by the story of the selfish gene.
The gene for homosexuality and the c-fiber
for pain share the central dogma of nothing-butism, which allows
only one level of description for complex systems. Sexual
preference and pain are traits of the organism, which belongs to
a higher level of organization than DNA molecules or neurons.
Nothing-butism tries to eliminate the level of organisms in
favor of the level of genes or neurons. What it achieves is
nothing but a confusion of properties on two distinct levels.
Atomization, construction, synthetic analysis
Atomization is different from analysis,
which many scientists call reduction and from which I take pain
to distinguish reductionism. To atomize a system is to demolish
it into an aggregate of elements. Nothing-butism takes the
extreme atomistic view that once we know the properties of
individual elements, we can combine them in various ways without
any idea of the whole. All we need are concepts for the
elements and their relations, concepts of the system are
superfluous. This is what it means by the system is nothing
but the elements. It is a purely bottom-up approach, where
one constructs from given elements.
In contrast to atomize, to analyze a system
is not to analyze it away. The aim of analysis is to understand
the system. To emphasize the point I call it synthetic
analysis. Suppose we analyze a geometric whole into two parts
by drawing the boundary R. R not merely
differentiates but simultaneously unites the two parts that it
delimits, and the parts are always understood as parts of a
whole. Further analysis reveals finer structures of the whole
by drawing more distinctions and delineating more intrinsically
related parts. In theoretical science, the "boundaries" that
cut up the whole into "parts" are concepts. In Plato's
metaphor, we conceptually carve nature at its joints. The
trick, as Plato said, is to observe the natural joins, not to
mangle it up like an unskilled butcher.
The synthetic analysis of complex systems
differs from atomization and bottom-up construction in several
major ways. First, it keeps in view at least two distinct
levels of organization, which it describes in different concepts
and tries to connect. Unlike atomization, which destroys the
systemic level, synthetic analysis never loses sight of the
whole system, even when it examines the parts. Second, it is a
round trip from the whole to the parts and back. Analysis
proceeds from the top down, but the analytic results must be
eventually synthesized to obtain desired answers for complex
systems. The final synthesis is not a purely bottom-up
construction because it is guided by the original analysis, so
that it is not overwhelmed by the combinatorial explosion.
Third, it is not bound by pre-fabricated parts. Scientists have
the freedom to choose the appropriate depth of analysis for the
explanation of certain system behaviors.
Consider for example genetics and molecular
biology. Nothing-butism demands them to start with a bunch of
nucleotides and try to piece them together. In contrast,
scientists start with the notion of inheritance of the traits of
organisms. They analyze organisms, identify chromosomes,
analyze them to find genes, and then analyze molecular
structures of the genes to find their nucleotide base
sequences. It is important that they do not stop with the base
sequences. They proceed to examine how the sequences code for
amino acids, which combine to form proteins, which facilitate
metabolism and keep the organism alive. The recent Human Genome
Project is a good example of analysis followed by synthesizing
the results to understand organisms. Notice that even in the
most detailed analysis where one talks about the sequence of
nucleotide bases, the bases are never isolated atoms but occur
in the context of the gene and genome.
The
genome and the brain
Almost all organisms share the same genetic
code. It is made up of four kinds of nucleotide bases: adenine,
guanine, cytosine, and thymine; A, G, C, T. The nucleotides
bind to each other, forming a long chain, a DNA molecule.
Usually the DNA molecules coil in a double helix. When they
uncoil, a long chain of DNA, AGTCCCAAGT . . . serves as a
template for reproduction and transcription. When a gene is
expressed, a segment of DNA is transcribed to an RNA molecule.
In complex organisms, most of the DNA does not code for
anything, and they form segments called introns. These introns
are cut out of the RNA to form a shorter message-RNA. The
message RNA is then translated into a protein. Three
consecutive nucleotide bases of the mRNA, e.g. AAG, constitute a
codon that specifies an amino acid. There are twenty kinds of
amino acid, which are the building blocks of proteins.
The genome of any organism, even a worm, is
a very complex system. C. elegans, a roundworm, is the
first multicellular animal whose genome has been completely
mapped. It has only about 900 cells, but it has some twenty
thousand genes. Humans are more complicated, but not by many
orders of magnitude. The human genome consists of about 3
billion nucleotide bases divided into 23 chromosomes. The 3
billion nucleotides are grouped into around 65,000 to 80,000
genes. The sizes of the genes vary greatly, but on the average,
a gene contains about 10 to 15 kilobases. Only 3% of the DNA
codes for proteins. Other portions regulate the expression of
the coding genes. However, the vast majority of the DNA bases
have no clear functions. Probably they are junk.
DNA and proteins are large biological
molecules, polymers. They distinguish themselves from other
polymers by their complexity. Many polymers are huge but
simple. For example, glycogen contains thousands of glucose
units but is simple, because all units are the same and are
connected in the same way into a one-dimensional chain. The
whole is simply the monotonous repetition of its parts. It has
very low information-content complexity. A system’s degree of
information-content complexity is measured by the length in bits
of the smallest program capable of specifying the system to a
computer.
In contrast, there are four kinds of base
units for the nucleic acids and twenty kinds of base units for
proteins. The succession of different bases in the chain
creates endless variety. The variety ensures high
information-content complexity; to specify a particular sequence
of DNA, we need almost to repeat all its nucleotides.
Variety itself does not guarantee
interest. Significance depends on the context where variety
occurs. The molecules in a glass of water have velocities that
vary infinitely. However, they sit in a system where their
variety can be averaged over to yield gross regularities. In
contrast, DNAs and proteins operate in a system, the organism,
where specific details of their individual structures can have
singular consequences. The demand for details and specificity
enhanced biological complexity.
The brain, by which I mean the central
nervous system, is even more complex than the genome. There are
more nerve cells, neurons, than nucleotides. More important,
there are far more kinds of neurons, each neuron has more
complicated structures and dynamics than a nucleotide, and the
relations among the neurons are far more intricate. The
nucleotides form a linear chain, so that each base is related
only to its two neighbors with unaltered bond strength. In
contrast, each neuron is connected to about a thousand other
neurons, forming a complicated web, and the connection strengths
are not fixed but change in time. In view of the variety of
neurons and synapses, the multiple connections, and the
ever-changing connective strengths, I think neuroscientists are
not exaggerating when they say that the brain is the most
complex system that we know.
Synthetic analysis of the genome and the brain
Actual approaches in brain or genome
research are synthetic analytic. They firmly retain the level
of the brain or genome when they delve into neurons or genes.
To analyze a system, one must first have some understanding of
the whole. This understanding constitutes the synthetic
framework in which scientists raise significant questions for
analysis to answer. We have seen some examples of synthetic
frameworks this morning. Hydrodynamics provides the synthetic
framework for the microanalysis of fluids. The state space
provides a theoretical framework for chaotic systems. The
synthetic frameworks in biology and neuroscience are not
mathematical, but they are no less important. Models in
psychology and our commonsense concepts of mental properties,
including perception, thinking, and consciousness, constitute
the framework for the analysis of the brain. The theories of
biological evolution, heritability, and genetics provide the
synthetic framework for the molecular analysis of the genome.
The most prevalent general conception that
represents the system in analysis is the notion of function.
Genes, for example, serve the function of inheritance.
Similarly, neuroscientists distinguish various regions of the
brain according to their functions, as the regions for vision or
language. Functional concepts imply a larger system in which
the service or contribution of an entity is significant, as the
functions of various organs in an organism. Even if the system
is tacit in some theoretical models, its demands hide in the
notion of an entity’s functions.
Vision and sexuality are traits of
organisms. Functional definitions such as visual neurons or gay
genes demand explanations for neural or genetic contributions to
organism behaviors. Usually, the contributions are very
complicated, because visual experience or sexual preference
involves many intricately tangled factors. With its notion of a
neuron for seeing one's grandmother or a gene for homosexuality,
nothing- butism covers up the complicated phenomena by
collapsing the level of the organism. It trivializes the notion
of function by simply identifying the organismic trait to be
explained with a fictitious neuron or gene. Its trick is a
generalization of the homuncular fallacy.
The homuncular fallacy is a common mistake
in discussions on mind. It explains how you recognize your
grandmother by positing a little man, a homunculus in your head,
who recognizes the grandmother. It is fallacious because it
claims to explain recognition, but it explains nothing. It
merely shifts the cognitive ability from the man to the little
man, therefore its "explanation" presupposes recognition, the
trait to claims to have explained. Nothing-butism commits the
same fallacy. The grandmother neuron plays the same role as the
fictitious homunculus. It covers up complex problems by
offering simplistic answers that, when examined, turn out to be
vacuous if not false.
Synthetic analysis in the Human Genome Project
The human genome project is arguably the
most significant project in biology. For historians and
philosophers of science, it has the added advantage of being a
coordinated research effort, so that its rationales are publicly
debated. Genomic research has tremendous practical
ramifications and involves ethical, legal, and social
questions. We will leave these questions aside and consider the
project only as a scientific campaign to tackle with a class of
complex system, namely, genomes of various organisms.
The major goal of the human genome project
is to map the entire human genome, that is, to determine the
sequence of all those 3 billions nucleotides and to identify the
tens of thousands of genes. This is the flagship of a battle
group, whose smaller vassals are responsible for sequencing the
genomes of some model bacteria and animals: the bacterium E.
coli, the single-celled yeast, the worm C. elegans,
the fruit fly drosophila, on which biologists have been
working for decades, and the mouse, on which most of our new
drugs are tested. The international project broke ground in
1990 and envisioned three five-year plans. Bucking the rule for
government projects, it runs ahead of schedule and below budget.
Already it has completed the genetic map for yeast and
recently, the worm. It expects to finish the sequencing of the
human genome by the end of 2003. It has to hurry, because it
has stiff competition from the private sector. The privately
owned gene factory of Celera, which has come on line a few
months ago, vows to sequence the human genome by the end of
2001.
The human DNA sequence, which fills 1,000
books each with 600 pages, analyzes genetics to the smallest
biological scale. It is the reductionist dreams come true. What
more does one need to understand human genetics? Nothing,
nothing-butists claim. Lots more, biologists counter.
From the inception of the project,
biologists argued that the sequence alone is worthless. Robert
Moyzis, director of the Center for Human Genome Studies at Los
Alamos, said: "Many individuals with a physical-science
background do not understand that a DNA sequence without a
genetic map is nearly useless." David Botstein, Chairman of the
Department of Genetics at Stanford, explained why: "the straight
sequencing approach was crazy because it ignores biology."5
The DNA sequence provides mainly
molecular knowledge. Its major value lies not in itself but
in its contribution to acquiring knowledge of biological
phenomena that occur on higher levels of organization. This is
eloquently expressed in the policy of Celera, which announces
that it will make its data on the DNA sequence freely accessible
online. Celera is not a philanthropist but a capitalist
enterprise out to make money. It figures that big profits lie
not in the sequence data per se but in making sense of
the data, patenting genes, and licensing the manageably packaged
information to pharmaceutical companies, which will mine the
data to find profitable medical applications.
The Human Genome Project’s approach is top
down. It starts with the genetic map and the physical map for
the genome, which serve as the synthetic framework that gives
biological meaning to the more analytic DNA sequence that
follows. A genetic map is obtained by studying the genetic
compositions of the members of families to find out how various
genes are passed on from parents to offspring. Several genes on
the same chromosome are often linked and inherited together.
Geneticists measure the "genetic distance" between two genes
according to the statistical frequency that they are separated
by crossing over at reproduction and therefore are not inherited
together. 1 cM (centiMorgan) is a unit of genetic distance. It
means that the two genes have a 1 percent probability of not
being inherited together. Genetic distance does not vary
linearly with physical distance on the chromosome. In humans,
1 cM roughly corresponds to 1 million nucleotide bases.
Although crude, the genetic map tells biologists how fragments
of DNA sequences are inherited. Without it, all the details of
the DNA sequence are useless.
The genetic map gives only the relative
"distance" between genes. The physical map fixes these genes to
specific positions of the chromosome by using sequence tagged
sites. Each sequence tagged site is a short and unique segment
of DNA, which serves as a signpost for a location in the
chromosome. The 52,000 tagged sites give a resolution of about
60 kb per marker. This is better than the 1 Mb resolution of
the genetic map, but it is still crude. The genetic map is like
a map of cities and towns, the physical map gives the streets in
the towns. Finally, the DNA sequence will give every house
address on the streets.
The procedure of the Human Genome Project
reveals a synthetic analytic process. It starts from the top
and identifies biological goals. Always keeping the genome as a
whole in sight, it performs deeper and deeper analysis, going
from the genetic map to the physical map to the final DNA
sequence.
Synthesis beyond the Human Genome Project
What happens when it reaches the analytic
bottom, when we know every one of the 3 billion bases in the
human DNA sequence? Only three percent of sequence codes for
proteins. The first step to make sense of the sequence is to
find those coding segments and identify the protein products
they code for. This scientists can do by taking cue from the
message-RNAs, and they are well on their way of identifying the
coding genes. However, gene identification is far from the end
of the story. Each cell in the body contains the whole genome,
but most of the genes in it are dormant. Cells being highly
differentiated, each cell has its own special needs. Only a
small portion of the genes are expressed at any time to produce
the specific proteins for a kind of cell. Gene expression is
the central scientific problem. Its complexity is overwhelming.
To make sense of DNA sequences, researchers
proceed to "functional genomic," which aims to interpret the
functions of the expressed DNA sequence on a genomic scale.
Here the nothing-butist claim is most pointedly battered. Genes
sitting on a DNA molecule are dead things by themselves. They
contribute to the life of an organism only when they are
expressed in protein products and thereby serve certain
functions. Complicated as gene structures are, gene expressions
are more complex, for they invariably involve gene
interactions. To elucidate the function of a gene, knowledge
about the gene's structure is no enough. A gene can have many
products, depending on the regulatory processes or on how its
message RNA is spliced. Frequently, cellular conditions beyond
the genome influence its expression. At what stage of
development and under what conditions is a gene active? What
are the regulatory and signaling mechanisms that activate it?
What is the protein it produces? What kind of cell or tissue
does the protein help grow? What are their effects on the
organism's physiology? These questions cannot be answered on a
gene-by-gene base. Scientists must take the "global view" that
accounts for the linkage among genes, the effects of the
structures of the chromosome, and other environmental factors.
In anticipation of the need for functional
studies, the Human Genome Project includes research on model
organisms. To probe the effects of genetic expressions on
physiology, scientists must to experiment on the organisms.
They knock out a gene or induce a genetic mutation and observe
its effects on the organism. Does the organism get a disease or
grow a leg on its head? Because scientists cannot experiment on
humans, mice and other model organisms come in handy. There is
a limit to the usefulness of model organisms. Humans and mice
share much genetic makeup, but differences are also great.
Discrepancies are enhanced by the fact that a gene’s behaviors
are influenced by the global conditions of the genome. Because
the genomes differ, a mutation of a key gene often produces
different effects in humans and mice. For example, when a
mutation in the putative "breast cancer gene" kills mice in the
embryo, but allows humans to undergo normal growth through much
of adulthood.
The complexity of mechanisms that regulate
gene expression is apparent even for the single-celled yeast.
Yeast has only 6,297 genes. However, the expression for more
than 300 genes can be altered by the mutation of a single gene.
Yeast synthesizes ethanol from sugar, an activity we utilize in
brewing. Its metabolism changes from anaerobic fermentation to
aerobic respiration when the sugar concentration in its
environment drops. Nothing-butists may tell the story of the
gene for fermentation switching off and the gene for respiration
switching on. Reality is far more interesting. During the
transition, the expressions of almost 30% of yeast genes
increase or decrease by two folds. Usually, the expression of a
gene is regulated by dozens of proteins. Some proteins act on
the gene specifically, others have broader effects; some bind to
the DNA all the time, other only briefly. The relative
concentrations of the proteins in turn depend on the function of
other genes and the cell conditions. In short, higher-level
structures exert certain feedback regulations on the activities
of genes. In view of such results, and anticipating that the
gene activity during human learning or memory will be even more
complex, biologists increasingly realize the futility to monitor
the activities of single genes alone. A synthetic view of the
genome or parts of the genome as a system is indispensable.
As in all complex systems, most genetic
complexity lies not in individual parts but in the interaction
among the parts. Interaction controls gene expression. Thus
systematic methods must be developed to study, simultaneously,
the activities of hundreds or thousands of genes. DNA sequencing
is not simple, but it appears trivial compared to the complexity
in the synthesis of gene expression. Biologists are working
very hard to develop experimental techniques for profiling gene
functions and expressions systematically. The front-runner now
is the microarray. An array contains thousands of identified
genes, whose activities can be studied under controlled
conditions. Experimental techniques are not enough. The human
genome project prides itself for its high throughput. It
generates an enormous amount of data, which will grow
exponentially with the combinatoric explosion of genetic
expressions. To avoid an information overload, there is a dire
need of synthetic concepts and methods to mine and organize the
data.
Two
Big Sciences and two unities of science
The Human Genome Project and its
descendents constitute a Big Science. It differs from the other
Big Science, elementary particle physics, in three ways.
Elementary particle physics is theoretically minded, pursuing
phenomena totally detached from our daily business. Genome
study is most practical, pursuing drugs for curing various
diseases. Physics is concerned with the simplicity of
fundamental physical laws, genome study confronts complexity.
Simplicity has a unified form, thus the personal composition of
physics is rather homogeneous. Complexity has many facets, thus
the effort to tackle complexity of gene expression becomes
increasingly inter-disciplinary. It spawns multidisciplinary
centers in major universities that draw together chemists,
biologists, physicists, and engineers to combat the complexity
of gene expression.
The unity of science has been an ideal for
centuries. Positivism, with its reductionist bent, advocates an
imperial unity where all authority flows from a set of
substantive laws and the reduced sciences are annexed if not
abolished. Imperial unity seems to suit the Big Science ideal
of elementary particle physics. However, it comes under fire
recently. The fashion in philosophy of science is the disunity
of science. Relativism proclaims an anarchy in which various
theories are incommensurate, people subscribing to them cannot
communicate rationally, and going from one to another is akin to
a religious conversion. Both reductionism and relativism are
too simplistic. An empire is not the only form of unity and
anarchy is not the only way to plurality. Unity can be achieved
as a federation of autonomous states with their own laws
agreeing to the general terms of a constitution. The building
boom of multidisciplinary centers is an example of the federal
unity of science at work. There are still biologists and
physicists in the centers for biophysics or physical bioscience,
but they are able to communicate with each other and collaborate
to achieve a unitary goal. Contrary to relativism, they prove
the rationality of science.
Notes
1. Priestley, J. (1777). Disquisitions Relating to Matter
and Spirit. New York: Garland Pun. Inc. (1976)
2. Chomsky, Language and Nature. Mind, 104, 1-61,
(1995).
3. Crick, The Astonishing Hypothesis. New York: Charles
Scribner's Sons (1994, pp. 3, 11).
4. Willard and Davids, "Genetics of disease: Complex genetics,
complex diseases." Current Opinion in Genetics and Development
8, 271-3 (1998).
5. In The Human Genome Project, ed. by N. G. Cooper,
Mill Valley, CA: University Science Books, (1994), p. 71.
Talk presented in the Department of History
and Philosophy of Science
University of Sydney
May 1999
Sunny Y. Auyang
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