霍华德·加德纳 - 麻省理工学院人工智能研究专业

霍华德·加德纳

What we do & don’t know
about learning

Suppose that we were commissioned

to create a museum of learning. I don’t
mean a stuffy, hands-off collection of
old manuscripts or ½lms, but rather a
state-of-the-art exploratorium that
displayed the full spectrum of learning
types and, in vivid form, everything that
is known about learning.1 Suppose, fur-
ther, that we had a budget for consul-
tants and were able to hire the seven
experts whose essays are collected here.
Presumably we would have in mind a
number of guiding questions, 之中
他们: What examples of learning
should we include? How should we
conceptualize this enterprise? 什么
progress has taken place in our under-
standing of learning in the last century

霍华德·加德纳, John H. 和伊丽莎白A.
Hobbs Professor in Cognition and Education at
哈佛大学教育研究生院, 是
best known in educational circles for his theory of
multiple intelligences, a critique of the notion that
there exists but a single human intelligence that
can be assessed by standard psychometric instru-
评论. His most recent books are “Changing
Minds” (2004), “Making Good” (2004), 和
“Good Work” (with Mihaly Csikszentmihaly and
威廉·达蒙, 2001). Gardner has been a
Fellow of the American Academy since 1995.

or so, and how can these revolutionary
insights inform the education of future
几代人? 最后, what puzzles
remain?

In all probability our museum’s ½rst
displays would show humans learning:
infants crawling, 步行, 说; tod-
dlers engaged in rough-and-tumble or
imaginative play; youngsters (or old-
sters) at school, learning their 3Rs and
going on to master the disciplines and
perhaps engage in interdisciplinary
工作. Casting our net more widely, 我们
might exhibit a child learning to play a
musical instrument, an apprentice work-
ing alongside a master builder, 一个医疗
student attending rounds, a recruit in
the military, a physically injured person
recovering the ability to walk, a victim of
a stroke learning to talk or reason again.
Casting our net more widely still, 到

encompass the full range of learning
among animals, our museum might in-
clude exhibits of sea slugs that learn to
move in certain directions while avoid-
ing others; ½sh that ‘imprint’ on certain
physical forms and trail after these privi-
leged forms throughout development;

1 This supposition is not idle. At Harvard
Project Zero, a research group concerned with
learning with which I have been af½liated since
1967, we are planning to construct such a muse-
um–at ½rst virtually, ultimately in bricks and
mortar.

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霍华德
加德纳
在
学习

rats that learn their way around complex
natural and man-made mazes; pigeons
that can play ping-pong, trace missiles,
and recognize human beings in photo-
图表; chimpanzees that can use sticks
to wipe off termites or to hide treasures
from their fellow chimps, and that seem
able to learn some language-like sys-
特姆斯.

We will also need to create displays
that show the organic structure of the
brain and explain how learning takes
place at microscopic levels: in regions
of the brain (like the hippocampus),
in neural column networks (像那些
involved in recognizing lines of different
orientation), and even in single nerve
cells that form synaptic connections to
other nerve cells and have those connec-
tions bolstered or weakened as a result
of experience.

And our museum will also have to ex-
hibit machines that learn and think, 所以
to speak: smart machines that can play
(and improve in) chess, that can under-
stand much of natural language, 相互影响
with human beings, and engage in scien-
ti½c problem solving.

As the range of exhibits and displays

suggests, our imaginary museum, 喜欢
the group of consultants it has retained,
reflects a wide range of theories about
learning and about the appropriate level
at which to analyze and understand the
现象.

仍然, as we plan our museum, 这是
important to step back and to provide
both a de½nition and a little history. 作为
a rough and ready approximation, 学习-
ing occurs under the following condi-
的: An organism or entity represents a
certain amount of information or data at
time X; at time Y it can represent new or
additional or transformed information
that brings it closer to a goal–either a
goal of the entity’s own choosing or a

goal that is intended by another entity,
which we may designate a teacher or
trainer. Such a formulation allows us to
distinguish learning experiences from
the sheer accumulation of mud on a tire,
一方面, and from a computer
program that may accomplish amazing
feats, but always in precisely the same
方式, 在另一.

From studies of preliterate cultures
and naive children, we can with some
con½dence delineate the major folk the-
ories of learning: Human beings learn by
observing others who are more knowl-
edgeable and by imitating, implicitly or
explicitly, what they do; asking ques-
tions of and listening to what more
knowledgeable individuals say; practic-
ing a skill and noting its improvement;
receiving clear rewards or punishments
that signal which course of behavior
should be pursued and which should be
eschewed. In literate cultures, the theo-
ries of learning expand to include the
reading of texts and the taking of class-
英语; and there are of course more idiosyn-
cratic theories that call attention to the
learning potential embodied in dreams,
药物, and deities. It is interesting to
note the extent to which more formal
theories of psychology and pedagogy
venture beyond these ‘commonsense’
views of the learning process.

Before there was a formal psychology

or biology of learning, it was philoso-
phers who addressed issues of knowl-
edge–perception, 学习, 记忆,
and the like. With Greek and Roman
thinkers as background, the philoso-
phers of the Renaissance and the En-
lightenment carved out positions that
continue to serve as points of orienta-
tion today. Descartes took a strong men-
talistic position, arguing that the mind
operated according to its own principles
and that it came stocked with innate
ideas. We see echoes of this perspective

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What we
做 & don’t
know about
学习

in the writings of Noam Chomsky and
Jerry Fodor, self-identi½ed nativists.
The British empiricists, led by Locke,
took an opposing perspective, 根据
to which the mind was initially a blank
slate; experience etched ideas onto the
slate and these ideas become associated
with one another. Twentieth-century be-
haviorists like the Russian physiologist
Ivan Pavlov, the American psychologist
乙. F. Skinner, and the ‘learning theorists’
portrayed here by Jerome Bruner sub-
scribed to this empiricist point of view.
In the eighteenth century, two new
perspectives on learning took hold. 我是-
manuel Kant described the basic episte-
mological categories–time, 空间, 编号-
误码率, causality–that human beings neces-
sarily imposed on their sensations and
perceptions. Individuals did learn from
经验, but that experience was
necessarily apprehended in temporal,
空间的, and causal ways. The Kantian
problematic had a great effect on the
research program of twentieth-century
Swiss developmental psychologist Jean
Piaget, who sought to describe the devel-
opment in infants and young children
of these categories of experience. Piaget
was also influenced by the writing of
his countryman Jean-Jacques Rousseau,
who discerned genius in the mind of
the child and believed that knowledge
should be allowed to unfold within,
rather than be imposed didactically
之上, 孩子.

Studies of learning were influenced
enormously by the rise of evolutionary
思维, chiefly emanating from the in-
sights of Charles Darwin and, to a lesser
extent, other British scholars like Alfred
华莱士, Thomas Huxley, and Herbert
Spencer. These writers all stressed the
continuities between human beings and
other animals, and the importance of
mental capacities that allowed individ-
ual organisms to survive until reproduc-

的. 为了确定, instincts were crucial
for lower organisms. But it was the ver-
tebrates–and especially mammals–ca-
pable of problem solving and planning
who emerged as victors in the struggle
for survival. As soon as the implications
of Darwin’s writings became clear, 他的
way of thinking came to dominate both
the theories and the empirical work of
scientists interested in learning.

The ½rst generation of modern schol-

ars of learning did not shrink from at-
tending to the more complex forms of
reasoning in human beings and other
primates. But beginning in the early
二十世纪, the territory of learn-
ing was largely ceded to those research-
ers who stressed the continuity of learn-
ing across the animal kingdom; 避免的
issues of language, consciousness, 和
higher-order ratiocination; and strove to
explain any intellectual achievement in
the most parsimonious and reductionist
fashion. 有趣的是, this was true not
only for those experimentalists who
worked primarily with rats and pigeons
(the two most common ‘model organ-
isms’) but also for those, like Edward
Lee Thorndike, who studied the acquisi-
tion of skills in school-age children. 为了
the ½rst half of the twentieth century,
this approach to learning held sway.

确实, it might still hold sway
today had it not been for the develop-
ment of high-speed computers and the
complex programs that have permitted
these electronic entities to compute
and solve various kinds of human-scale
问题. Once it became clear that
computers could mimic human thought
processes and–in the view of many–
bootstrap themselves over time to a
higher level of performance, 科学家
could no longer withhold such intellec-
tual competences from human beings.
Thus was born the cognitive revolution,
an important intellectual movement

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霍华德
加德纳
在
学习

among whose forefathers were the com-
puter scientists Herbert Simon and Mar-
vin Minsky, the linguist Noam Chomsky,
and the psychologists George Miller and
Jerome Bruner. I view the cognitive revo-
lution as a contemporary interdiscipli-
nary effort to provide scienti½c answers
to long-standing epistemological ques-
系统蒸发散, such as our present consideration
–the nature of learning, why it is possi-
布莱, how it occurs.

In a broad sense, all of the consultants
to our museum–the contributors to this
issue of Dædalus, and the majority of
current workers on issues of learning–
are offspring of this intellectual revolu-
tion of ½fty years ago. They recognize
the relationship between the long-
standing philosophical agenda sketched
多于, 一方面, and discoveries
in psychology, 语言学, 人类学,
神经科学, cognitive science, 和
other relevant disciplines, 在另一.
And they believe that progress is being
made in understanding the nature of
various kinds of learning, though they
may differ on how best to describe that
learning and the nature of that progress.

Now that I’ve surveyed the historical

context to our current understanding of
学习, it is timely to suggest the major
dimensions against which to evaluate
the speci½c contributions of our consul-
tants–as well as those of some other
consultants who might have been re-
泰内德.

Two dimensions seem particularly
有用: the learning of species or entities
to which these consultants compare hu-
man learning; and the type and extent of
reductionism entailed in their efforts to
explain all manner of learning. Continu-
ing in the tradition laid out by Darwin
and his successors, Daniel Povinelli and
Michael Tomasello ½nd it productive to
delineate the nature of learning in chim-

潘吉斯. While both have documented
the impressive capacities of chimpan-
zees, they elect in their essays here to
focus on the fault line between chim-
panzees and children. Povinelli claims
that chimpanzees are incapable of ab-
stract thought; that all of their achieve-
ment is the result of observations of con-
crete objects and events. Tomasello doc-
uments that chimpanzees have only the
most meager capacities to imitate mod-
这, to infer the motives of others, 并
transmit any kind of cultural knowledge.
The chimpanzee emerges in their ac-
counts as an organism that is incredibly
skilled at making use of the information
at hand, but that is unable either to con-
ceptualize what is not present or to make
use of the incidental knowledge attained
by other individuals in its group. 这里
lies the huge fault line that separates
chimpanzees from human children, WHO
from early on can engage in pretend play,
imitate elders, and rapidly assimilate the
knowledge that earlier generations have
accumulated.

Not represented in this collection but
worthy of note is the recent claim by pri-
matologist Marc Hauser and his col-
leagues Noam Chomsky and Tecumseh
Fitch that they have identi½ed a crucial
capacity that is absent in nonhuman pri-
伙伴: the capacity for recursion.2 Bo-
nono apes are able to master language-
like strings of symbols, provided that the
syntax of the string does not depend on
the capacity to embed one unit within
其他. 例如, an ape may appre-
ciate the logic in the proposition “Mom-
my sleeps,” but could be completely sty-
mied by an expression like “Baby said
that Mommy sleeps,” let alone “Daddy
said that Baby said that Mommy sleeps.”

2 Marc Hauser, Noam Chomsky, and Tecum-
seh Fitch, “The Faculty of Language: What Is
它, Who Has It, and How Did It Evolve?“ 科学
298 (2002): 1569–1579.

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What we
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学习

In her essay here Alison Gopnik focus-

es on the characteristics of young chil-
德伦, but she brings to bear an entirely
different comparison group. Like several
other contemporary developmental psy-
chologists, Gopnik ½nds it useful to
think of the child in comparison to the
working scientist–a worker rather like
她自己. In making this analogy, Gopnik
revisits a theme introduced decades ago
by Jean Piaget (as well as themes ½rst
articulated by the philosopher of science
Thomas Kuhn in his discussion of para-
digmatic scienti½c revolutions). 如何-
曾经, Gopnik goes well beyond Piaget,
who tended to emphasize the limitations
in childrens’ thinking and the disconti-
nuities between child tinkerer and adult
科学家. She argues that, like scientists,
very young children are capable of put-
ting forth theories, carrying out experi-
评论, observing the experiments of
其他的, and discerning statistical pat-
燕鸥. In an accompanying essay, Su-
san Carey characterizes the conceptual
growth from child to scientist as a boot-
strapping operation; only through such
self-constructing operations can chil-
dren proceed, 例如, from early
intuitions about quantity to a full-
blown sense of number.

Possibly because they are philoso-
phers, Patricia Churchland and Clark
Glymour are less concerned with the
speci½cs of experiments involving in-
fants or chimpanzees. Churchland dis-
cusses learning at the level of individual
神经细胞, while Glymour describes the
powerful operations that can be carried
out by high-speed computers.

From my vantage point, the key issue
their juxtaposed essays raise is the ques-
tion of reductionism. To put it sharply,
can human learning and thinking be
adequately reduced to the operations of
神经元, 一方面, or to chips of
silicon, 在另一? Or is something

crucial missing, something that calls for
an explanation at the level of the human
organism?

As I read her essay, Churchland stress-
es the importance of understanding the
nervous system and chides those philos-
ophers who do place the same value on
它. But she does not feel the need to dis-
pense with a psychological level of ex-
planation. For most of his essay, Gly-
mour embraces a tougher-minded re-
ductionism: we should stop trying to
solve problems that are too compli-
cated for us and instead turn them over
to those ever smarter computers. 但在
the end, Glymour acknowledges that the
problems solved by computers are ones
that human beings have formulated
然后, at both ends of the process,
we need human judgment after all.

As one who surveyed the cognitive sci-
ences in some detail twenty years ago,3
I am very impressed by the knowledge
that has accumulated in the past few de-
你跌倒了. Thanks to theoretical and empiri-
cal researchers like those represented
这里, we know a great deal more about
the nature of early learning and under-
standing in human beings, and can point
with far greater precision to the ways in
which humans differ from their closest
biological relatives. Our accumulating
knowledge of the nervous system is
even more impressive, and the bridges
between cognitive science and neuro-
science are sturdier. The accomplish-
ments of computers are also striking;
and while some of these accomplish-
ments are achieved by methods quite
remote from those used by Homo sapi-
ens, we are beginning to have software
and hardware that in important respects
learn in ways that resemble our own

3 霍华德·加德纳, The Mind’s New Science: A
History of the Cognitive Revolution (纽约:
基础书籍, 1985).

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霍华德
加德纳
在
学习

学习. In fact I have heard that good
chess masters now study computer
games and learn new strategies from
those inanimate models. 最终, 的
课程, debates similar to those between
the rationalists and the empiricists in the
eighteenth century, and between the
learning theorists and the Gestaltists in
the ½rst half of the twentieth century,
are still being waged. 然而, as Jerome
Bruner indicates in his essay, 当前的
debates are conducted in much more so-
phisticated ways.

But as one who seeks to build a muse-
um of learning, I am struck by the limi-
tations, reflected in the essays collected
这里, of the still evolving cognitive sci-
恩塞斯. Let me mention the principal
那些.

First of all, these disciplines almost
always deem the scienti½c mind as the
proper model of human thought. 这
claim that all cognition, 学习, 开发-
选项, and intelligence are best rep-
resented by those of the scientist was
Piaget’s great contribution, and also his
weakness. And we see his sentiments in
most of the essays here. But human
thinking and learning is achieved as
well by artists, musicians, 政治家,
businesspeople, inventors, 宗教的
领导者, and dreamers–we must under-
stand their forms of learning and the
ways in which they may differ from the
cognition of the theoretical physicist or
the benchtop chemist.

第二, the instances of learning that
are most frequently examined typically
take place over brief periods of time:
nanoseconds in the case of computers,
milliseconds in the case of nerve cells,
an hour or less in the case of most exper-
imental trials. Yet the forms of human
thought that are most valued often re-
quire the investment of months or even
年. What of the learning involved in

Albert Einstein’s coming up with the
theory of relativity; Marcel Proust’s pen-
ning À la recherche de temps perdu; 安德鲁
Wiles’s solution of Fermat’s theorem; 或者
Mahatma Gandhi’s creation of peaceful
nonviolence; 或者, 的确, Ramon y Cajal’s
studies of the nervous system, or John
von Neumann’s formulation of the na-
ture of computer programming? 甚至
the high-speed computers that can
handle far more variables than ordinary
mortals can do not illuminate the nature
of original artistic, 科学的, 或政治
想法.

而且, the contributions of cog-

nitive science to schooling–the chief
institution devoted to learning–remain
谦虚的. As one who spends his life at a
school of education and has devoted
much time to school reform, I could not
help but be struck by the virtual absence
here of any reference to schools, formal
教学, the 3Rs, and the scholarly dis-
ciplines–in short, the realms that most
individuals think of nowadays when
they think of learning. Part of the ex-
planation for this is undoubtedly that
schools are very complex institutions
and the processes of learning that
are supposed to take place there over
months or years are dif½cult to capture
in scienti½c research.

仍然, I think that more can be said

about how our current understanding of
learning might influence education, 作为
well as the obstacles that make such ap-
plications dif½cult. In my own work I
have recently focused on two lines of
研究. The ½rst outlines the various
misconceptions that readily arise in ear-
ly childhood and that considerably com-
plexify the mastery of the disciplines.
It turns out that young children readily
embrace creationist accounts of the ori-
gin of the species, a phenomenon that

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What we
做 & don’t
know about
学习

makes dif½cult the learning of evolu-
tionary theory; embrace Aristotelian ac-
counts of the behavior of physical mat-
特尔, which render the mastery of New-
tonian physics problematic; and readily
embrace one-dimensional accounts of
historical and political events, thus mak-
ing it dif½cult to appreciate complex and
multicausal accounts. 认可
the early emergence and dogged persis-
tence of these misconceptions is an es-
sential component of effective pedagogy.
Teachers must directly confront these
misconceptions and give students ample
opportunities to air their understand-
ings and misunderstandings, to discover
where they are inadequate and where
they require revisions.4

I have also collected evidence against
the contention that intelligence is a sin-
gle, all-encompassing human capacity.
I favor an alternative account: 这一切
human beings possess a range of intel-
ligences and that we differ from one
another in our intellectual pro½les.
Throughout most of history, 教育工作者
have ignored this possibility and have
taught subjects in one way–thereby
inevitably favoring students who are
strong in linguistic and logical ways of
思维. It is possible to reverse this
uniform approach, and so reach more
学生, by presenting materials in a
multitude of ways and giving students
options in how they may convey their
own understandings.

Alas, even if we had exquisitely de-
tailed and powerful theories of learning,
this would be no guarantor that they
would be adopted widely in schools. 作为
David Olson argues in his recent book,
schools are bureaucratic organizations

4 See Howard Gardner, The Unschooled Mind
(纽约: 基础书籍, 1991) and The Disci-
plined Mind (纽约: Penguin Putnam
图书, 2000).

that respond principally to political
pressures and institutional impera-
tives.5 This, in short, is the reason that
politicians in America talk incessantly
about test scores and international com-
parisons, and rarely if ever mention
what has been learned about learning.
Independent schools have somewhat
greater latitude in what they prescribe
but they are by no means immune from
these social pressures. Only in home-
schooling or individual tutoring can the
student readily bene½t from our growing
understanding of learning. And only
when ways are found to bridge the gap
between the knowledge being accumu-
lated by scholars and the typical opera-
tions of schools in the nation-state will
the pipeline between research and prac-
tice be opened.

最后, I believe that there is also a

conceptual gap that needs to be ad-
dressed–both by our consultants and
by our hypothetical museum of learning.
That gap concerns the fact that human
beings are social, 文化, and historical
creatures as much as we are neurologi-
卡尔, psychological, and computational
生物. We evolved to do many things
well–but we did not evolve to create
calculus or write the U.S. 宪法
or compose classical music or invent
airplanes and the pill. Nor could anyone
have anticipated, even ½fty years ago,
the civil rights revolution, or the femi-
nist revolution, or the fall of commu-
nism, or the proliferation of nuclear
武器, or the rise of the World Wide
Web in an increasingly globalized civi-
化. Yet somehow individuals grow-
ing up in the early twenty-½rst century
must be able to master these bodies of

5 David Olson, Psychological Theory and Educa-
tional Reform (纽约: 剑桥大学
按, 2003).

代达罗斯冬天 2004

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霍华德
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在
学习

knowledge and cope with these events
of historical signi½cance. Little in our
sciences of learning addresses issues of
this scale; our cultural, historical, 和
literary studies do not make much con-
tact with our scienti½c approaches; 一个
interdisciplinary span across these broad
disciplinary terrains still eludes us.

简而言之, there will be a great deal of
interest on display in our hypothetical
museum of learning. But there will also
be a large number of empty rooms–for
there is still so much that we have to
learn about learning.

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代达罗斯冬天 2004
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