from computers to anthills as models of human thought
With the development of computers in the 1940s and 1950s, a new model for human
thinking became available. The initial period of cognitive science was driven by the
analogy that the brain functions like a computer. Consequently, thinking was viewed as the
processing of symbols. This was also the methodology of classical artificial intelligence.
As a result of criticism of the symbol manipulation paradigm, there have recently been two
main kinds of reaction to it. The first one is connectionism, where thinking is modelled
as associations in artificial neuron networks. Some connectionist models are tightly
connected to developments in the neurosciences, while others are more general models of
cognitive processes such as concept formation. The second reaction consists of theories of
embodied and situated cognition, where cognition is seen as taking place not only in the
brain, but in interaction with the body and the surrounding world. In line with this,
modern studies of robotics are based on so called reactive systems, the actions of which
depend directly on the world instead of a symbolic model of it. The situated view on
cognition will also be central for future developments of man-machine interaction, in
particular in educational tools that exploit information technology.
1. Before cognitive science
2. The dawn of computers
3. 1956: cognitive science is born
4. The rise and fall of artificial intelligence
5. Mind: the gap
6. First heresy against high-church computationalism: thinking is
not only by symbols
7. Second heresy: cognition is not only in the brain
8. The future of cognitive science
About the author
The roots of cognitive science go as far back as those of philosophy. One way of
defining cognitive science is to say that it is just naturalised philosophy. Much of
contemporary thinking about the mind derives from René Descartes' distinction between the
body and the soul. They were constituted of two different substances and it was only
humans that had a soul and were capable of thinking. According to him, other animals were
Descartes was a rationalist: our minds could gain knowledge about the world by rational
thinking. This epistemological position was challenged by the empiricists, notably John
Locke and David Hume. They claimed that the only reliable source of knowledge is sensory
experience. Such experiences result in ideas, and thinking consists of connecting ideas in
Immanuel Kant strove to synthesize the rationalist and the empiricist positions. Our
minds always deal with phenomenal experiences and not with the external world. He
introduced a distinction between the thing in itself (das Ding an sich) and the thing
perceived by us (das Ding an uns). Kant then formulated a set of categories of thought,
without which we cannot organise our phenomenal world. For example, we must interpret what
happens in the world in terms of cause and effect.
Among philosophers, the favourite method of gaining insights into the nature of the
mind was introspection. This method was also used by psychologists at the end the 19th and
the beginning of the 20th century. In particular, this was the methodology used by Wilhelm
Wundt and other German psychologists. By looking inward and reporting inner experiences it
was hoped that the structure of the conscious mind would be unveiled.
However, the inherent subjectivity of introspection led to severe methodological
problems. These problems set the stage for a scientific revolution in psychology. In 1913,
John Watson published an article with the title "Psychology as the behaviourist views
it" which has been seen as a behaviourist manifesto. The central methodological tenet
of behaviourism is that only objectively verifiable observations should be allowed as
data. As a consequence, scientists should prudently eschew all topics related to mental
processes, mental events and states of mind. Observable behaviour consists of stimuli and
responses. According to Watson, the goal of psychology is to formulate lawful connections
between such stimuli and responses.
Behaviourism had a dramatic effect on psychology, in particular in the United States.
As a consequence, animal psychology became a fashionable topic. Laboratories were filled
with rats running in mazes and pigeons pecking at coloured chips. An enormous amount of
data concerning conditioning of behaviour was collected. There was also a behaviourist
influence in linguistics: the connection between a word and the objects it referred to was
seen as a special case of conditioning.
Analytical philosophy, as it was developed during the beginning of the 20th century,
contained ideas that reinforced the behaviourist movement within psychology. In the 1920s,
the so called Vienna Circle formulated a philosophical programme that had as its primary
aim to eliminate as much metaphysical speculation as possible. Scientific reasoning should
be founded on an observational basis. The observational data were obtained from
experiments. From these data knowledge could only be expanded by using logically valid
inferences. Under the headings of logical empiricism or logical positivism, this
methodological programme has had an enormous influence on most sciences.
The ideal of thinking for the logical empiricists was logic and mathematics, preferably
in the form of axiomatic systems. In the hands of people like Giuseppe Peano, Gottlob
Frege and Bertrand Russell, arithmetic and logic had been turned into strictly formalised
theories at the beginning of the 20th century. The axiomatic ideal was transferred to
other sciences with less success. A background assumption was that all scientific
knowledge could be formulated in some form of language.
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As a part of the axiomatic endeavour, logicians and mathematicians investigated the
limits of what can be computed on the basis of axioms. In particular, the focus was put on
the so-called recursive functions. The logician Alonzo Church is famous for his thesis
from 1936 that everything that can be computed can be computed with the aid of recursive
At the same time, Alan Turing proposed an abstract machine, later called the Turing
machine. The machine has two main parts: an infinite tape divided into cells, the contents
of which can be read and then overwritten; and a movable head that reads what is in a cell
on the tape. The head acts according to a finite set of instructions, which, depending on
what is read and the current state of the head, determines what to write on the cell (if
anything) and then whether to move one step left or right on the tape. It is Turing's
astonishing achievement that he proved that such a simple machine can calculate all
recursive functions. If Church's thesis is correct, this means that a Turing machine is
able to compute everything that can be computed.
The Turing machine is an abstract machine - there are no infinite tapes in the world.
Nevertheless, the very fact that all mathematical computation and logical reasoning had
thus been shown to be mechanically processable inspired researchers to construct real
machines that could perform such tasks. One important technological invention consisted of
the so-called logic circuits that were constructed by systems of electric tubes. The
Turing machine inspired John von Neumann to propose a general architecture for a real
computer based on logic circuits. The machine had a central processor which read
information from external memory devices, transformed the input according to the
instructions of the programme of the machine, and then stored it again in the external
memory or presented it on some output device as the result of the calculation. The basic
structure was thus similar to that of the Turing machine.
In contrast to earlier mechanical calculators, the computer stored its own instructions
in the memory coded as binary digits. These instructions could be modified by the
programmer, but also by the programme itself while operating. The first machines developed
according to von Neumann's general architecture appeared in the early 1940s.
Suddenly there was a machine that seemed to be able to think. A natural question was,
then: to what extent do computers think like humans? In 1943, McCulloch and Pitts
published an article that became very influential. They interpreted the firings of the
neurons in the brain as sequences of zeros and ones, in analogy with the binary digits of
the computers. The neuron was seen as a logic circuit that combined information from other
neurons according to some logical operator and then transmitted the results of the
calculation to other neurons.
The upshot was that the entire brain was seen as a huge computer. In this way, the
metaphor that became the foundation for cognitive science was born. Since the von Neumann
architecture for computers was at the time the only one available, it was assumed that
also the brain had essentially the same general structure.
The development of the first computers occurred at the same time as the concept of
information as an abstract quantity was developed. With the advent of various technical
devices for the transmission of signals, like telegraphs and telephones, questions of
efficiency and reliability in signal transmission were addressed. A breakthrough came with
the mathematical theory of information presented by Claude Shannon. He found a way of
measuring the amount of information that was transferred through a channel, independently
of which code was used for the transmission. In essence, Shannon's theory says that the
more improbable a message is statistically, the greater is its informational content
(Shannon and Weaver 1948). This theory had immediate applications in the world of zeros
and ones that constituted the processes within computers. It is from Shannon's theory that
we have the notions of bits, bytes, and baud that are standard measures for present-day
information technology products.
Turing saw the potentials of computers very early. In a classic paper of 1950, he
foresaw a lot of the developments of computer programmes that were to come later. In that
paper, he also proposed the test that nowadays is called the Turing test. To test whether
a computer programme succeeds in a cognitive task, like playing chess or conversing in
ordinary language, let an external observer communicate with the programme via a terminal.
If the observer cannot distinguish the performance of the programme from that of a human
being, the programme is said to have passed the Turing test.
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There are good reasons for saying that cognitive science was born in 1956. That year a
number of events in various disciplines marked the beginning of a new era. A conference
where the concept of Artificial Intelligence (AI) was used for the first time was held at
Dartmouth College. At this conference, Alan Newell and Herbert Simon demonstrated the
first computer programme that could construct logical proofs from a given set of premises.
They called the programme the Logical Theorist. This event has been interpreted as the
first example of a machine that performed a cognitive task.
Then in linguistics, later the same year, Noam Chomsky presented his new views on
transformational grammar, which were to be published in his book Syntactic Structures in
1957. This book caused a revolution in linguistics and Chomsky's views on language are
still dominant in large parts of the academic world. What is less known is that Chomsky in
his doctoral thesis from 1956 worked out a mapping between various kinds of rule-based
languages and different types of automata. He showed, for example, that an automaton with
only a finite number of possible states can correctly judge the grammaticality of
sentences only from so-called regular languages. Such languages allow, among other things,
no embedded phrases nor any couplings between separated parts of a sentence. However, such
structures occur frequently in natural languages. The most interesting of Chomsky's
results is that any natural language would require a Turing machine to process its
grammar. Again we see a correspondence between a human cognitive capacity, this time
judgments of grammaticality, and the power of Turing machines. No wonder that Turing
machines were seen as what was needed for understanding thinking.
Also in 1956, the psychologist George Miller published an article with the title
"The magical number seven, plus or minus two: some limits on our capacity for
processing information" that has become a classic within cognitive science. Miller
argued that there are clear limits to our cognitive capacities: we can actively process
only about seven units of information. This article is noteworthy in two ways. First, it
directly applies Shannon's information theory to human thinking. Second, it explicitly
talks about cognitive processes, something which had been considered to be very bad
manners in the wards of the behaviourists that were sterile of anything but stimuli and
responses. However, with the advent of computers and information theory, Miller now had a
mechanism that could be put in the black box of the brain: computers have a limited
processing memory and so do humans.
Another key event in psychology in 1956 was the publication of the book A Study of
Thinking, written by Jerome Bruner, Jacqueline Goodnow and George Austin, who had studied
how people group examples into categories. They reported a series of experiments where the
subjects' task was to determine which of a set of cards with different geometrical forms
belong to a particular category. The category was set by the experimenter, for example the
category of cards with two circles on them, but was not initially known to the subject.
The subjects were presented one card at a time and asked whether the card belonged to the
category. The subject was then told whether the answer was correct or not. Bruner and his
colleagues found that when the concepts were formed as conjunctions of elementary concepts
like "cards with red circles", the subjects learned the category quite
efficiently; while if the category was generated by a disjunctive concept like "cards
with circles or a red object" or negated concepts like "cards that do not have
two circles", the subjects had severe problems in identifying the correct category.
Note that Bruner, Goodnow and Austin focused on logical combinations of primitive
concepts, again following the underlying tradition that human thinking is based on logical
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Newell and Simon's Logical Theorist was soon to be followed by a wealth of more
sophisticated logical theorem-proving programmes. There was great faith in these
programmes: in line with the methodology of the logical positivists, it was believed that
once we have found the fundamental axioms for a particular domain of knowledge we can then
use computers instead of human brains to calculate all their consequences.
But thinking is not logic alone. Newell and Simon soon started on a more ambitious
project called the General Problem Solver, that, in principle, should be able to to solve
any well-formulated problem. The General Problem Solver worked by means-end analysis: a
problem is described by specifying an intial state and a desired goal state and the
programme attempts to reduce the gap between the start and the goal states. However, work
on the programme was soon abandoned since the methods devised by Newell and Simon turned
out not to be as general as they had originally envisaged.
One of the more famous AI programmes from this period was Terry Winograd's SHRDLU (see
Winograd 1972). This programme could understand a fairly large variety of sentences,
formulated in natural language, about a world consisting of different kinds of blocks and
perform (imagined) actions on the blocks like moving or stacking them. The programme also
had a capacity, stunning at the time, to answer questions about the current state of the
block world. Above all, Winograd's programme was impressive on the level of syntactic
parsing, which made it seemingly understand linguistic input. However, the programme had
no learning capacities.
The first robot programmes, like for example STRIPS developed at Stanford Research
Institute, also followed the symbolic tradition by representing all the knowledge of the
robot by formulas in a language that was similar to predicate logic. The axioms and rules
of the programme described the results of various actions together with the preconditions
for the actions. Typical tasks for the robots were to pick up blocks in different rooms
and stack them in a chosen room. However, in order to plan for such a task, the programme
needed to know all consequences of the actions taken by the robot. For instance, if the
robot went through the door of a room, the robot must be able to conclude that the blocks
that were in the room did not move or ceased to exist as a result of the robot entering
the room. It turned out that giving a complete description of the robot's world and the
consequences of its actions resulted in a combinatorial explosion of the number of axioms
required. This has been call the frame problem in robotics.
The optimism of AI researchers, and their high-flying promises concerning the
capabilities of computer programmes, were met with several forms of criticism. Already in
1960, Yehoshua Bar-Hillel wrote a report on the fundamental difficulties of using
computers to perform automatic translations from one language to another. And in 1967,
Joseph Weizenbaum constructed a seductive programme called ELIZA that could converse in
natural language with its user. ELIZA was built to simulate a Rogerian psychotherapist.
The programme scans the sentences written by the user for words like "I",
"mother", "love" and when such a word is found, the programme has a
limited number of preset responses (where the values of certain variables are given by the
input of the user). The programme does very little calculation and understands absolutely
nothing of its input. Nevertheless, it is successful enough to delude an unsuspecting user
for some time until its responses become too stereotyped.
Weizenbaum's main purpose of writing ELIZA was to show how easy it was to fool a user
that a programme has an understanding of a dialogue. We are just too willing to ascribe
intelligence to something that responds appropriately in a few cases - our anthropomorphic
thinking extends easily to computers. Weizenbaum was appalled that some professional
psychiatrists suggested ELIZA as a potential therapeutic tool which might be used in
practice by people with problems.
Another influential critic was Hubert Dreyfus who in 1972 published What Computer's
Can't Do: A Critique of Artificial Reason. From a basis in phenomenological philosophy, he
pointed out a number of fundamental differences between computers and human beings: humans
have consciousness, understand and tolerate ambiguous sentences, have bodily experiences
that influence thinking; they have motives and drives, become tired or lose interest.
Dreyfus argues that computer programmes cannot achieve any of these qualities.
In spite of the critics, AI lived on in, more or less, its classical shape during the
1970s. Among the more dominant later research themes were the so called expert systems
which have been developed in various areas. Such systems consist of a large number of
symbolic rules (that have normally been extracted from human experts) together with a
computerized inference engine that applies the rules recursively to input data and ends up
with some form of solution to a given problem.
The most well-known expert system is perhaps MYCIN which offers advice on infection
diseases (it even suggests a prescription of appropriate antibiotics). MYCIN was exposed
to the Turing test in the sense that human doctors were asked to suggest diagnoses on the
basis of the same input data, from laboratory tests, that was given to the programme.
Independent evaluators then decided whether the doctors or MYCIN had done the best job.
Under these conditions, MYCIN passed the Turing test, but it can be objected that if the
doctors had been given the opportunity to see and examine the patients, they would
(hopefully) have outperformed the expert system.
However, expert systems never reached the adroitness of human experts and they were
almost never given the opportunity to have the decisive word in real cases. A fundamental
problem is that such systems may incorporate an extensive amount of knowledge, but they
hardly have any knowledge about the validity of their knowledge. Without such
meta-knowledge, a system cannot form valid judgments that form the basis of sound
decisions. As a consequence, expert systems have been demoted to the ranks and are
nowadays called "decision support systems."
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A unique aspect of our cognitive processes is that we experience at least part of them
as being conscious. The problem of what consciousness is has occupied philosophers for
centuries and there is a plethora of theories of the mind.
Cartesian dualism, which treats the body and the mind as separate substances, has lost
much of its influence during the 20th century. Most current theories of the mind are
materialistic in the sense that only physical substances are supposed to exist. But this
position raises the question of how conscious experiences can be a result of material
processes. There seems to be a unbridgeable gap between our physicalistic theories and our
A theory of the mind that has been popular since the 1950s is the so called identity
theory which claims that conscious processes are identical with material processes in the
brain. As a consequence, the phenomenal is in principle reducible to the physical. It
should be noted that according the identity theory it is only processes in the brain that
can become parts of conscious experience.
However, the new vogue of cognitive theories based on the analogy between the brain and
the computer soon attracted the philosophers. In 1960, Hilary Putnam published an article
with the title "Minds and machines" where he argued that it is not the fact of
being of a brain or a computer that determines whether it has a mind or not, but only what
function that brain or computer performs. And since the function of a computer was
described by its programme, the function of the brain was, by analogy, also identified
with a programme. This stance within the philosophy of mind has become known as
The central philosophical tenet of the AI approach to representing cognitive processes
is that mental representation and processing is essentially symbol manipulation. The
symbols can be concatenated to form expressions in a language of thought sometimes called
Mentalese. The different symbolic expressions in the mental states of a person are
connected only via their logical relations. The symbols are manipulated exclusively on the
basis of their form - their meaning is not part of the process.
The following quotation from Fodor (1981, 230) is a typical formulation of the symbolic
paradigm that underlies traditional AI:
Insofar as we think of mental processes as computational (hence as formal
operations defined on representations), it will be natural to take the mind to be, inter
alia, a kind of computer. That is, we will think of the mind as carrying out whatever
symbol manipulations are constitutive of the hypothesized computational processes. To a
first approximation, we may thus construe mental operations as pretty directly analogous
to those of a Turing machine.
The material basis for these processes is irrelevant to the description of their
results - the same mental state can be realised in a brain as well as in a computer. Thus,
the paradigm of AI clearly presupposes the functionalist philosophy of mind. In brief, the
mind is thought to be a computing device, which generates symbolic expressions as inputs
from sensory channels, performs logical operations on these sentences, and then transforms
them into linguistic or non-linguistic output behaviours.
However, functionalism leaves unanswered the question of what makes certain cognitive
processes conscious or what gives them content. As an argument against the strongest form
of AI which claims that all human cognition can be replaced by computer programmes, John
Searle presents his "Chinese room" scenario. This example assumes that a person
who understands English but no Chinese is locked into a room together with a large set of
instructions written in English. The person is then given a page of Chinese text that
contains a number of questions. By meticulously following the instructions with respect to
the symbols thast occur in the Chinese questions, he is able to compose a new page in
Chinese which comprise answers to the questions.
According to functionalism (and in compliance with the Turing test) the person in the
room who is following the instructions would have the same capacity as a Chinese speaking
person. Hence functionalism would hold that the person together with the equipment in the
room understands Chinese. But this is potently absurd, claims Searle. For analogous
reasons, according to Searle, a computer lacks intentionality and can therefore not
understand the meaning of sentences in a language. Searle's argument has spawned a heated
debate, that is still going on, about the limits of functionalism and what it would mean
to understand something.
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6.1 Artificial neuron networks
For many years, the symbolic approach to cognition was totally dominant. But as
result of the various forms of criticism which led to a greater awareness of the
limitations of the "symbol crunching" of standard AI programmes, the ground was
prepared for other views on the fundamental mechanisms of thinking. We find the first
signs of heresy against what has been called "high-church computationalism".
For empiricist philosophers like Locke and Hume, thinking consists basically in the
forming of associations between "perceptions of the mind". The basic idea is
that events that are similar become connected in the mind. Activation of one idea
activates others to which it is linked: when thinking, reasoning, or day-dreaming, one
thought reminds us of others.
During the last decades, associationism has been revived with the aid of a new model of
cognition: connectionism. Connectionist systems, also called artificial neuron networks,
consist of large numbers of simple but highly interconnected units ("neurons").
The units process information in parallel in contrast to most symbolic models where the
processing is serial. There is no central control unit for the network, but all neurons
act as individual processors. Hence connectionist systems are examples of parallel
distributed processes (Rumelhart and McClelland 1986).
Each unit in an artificial neuron network receives activity, both excitatory and
inhibitory, as input; and transmits activity to other units according to some function of
the inputs. The behaviour of the network as a whole is determined by the initial state of
activation and the connections between the units. The inputs to the network also gradually
change the strengths of the connections between units according to some learning rule. The
units have no memory in themselves, but earlier inputs are represented indirectly via the
changes in strengths they have caused. According to connectionism, cognitive processes
should not be represented by symbol manipulation, but by the dynamics of the patterns of
activities in the networks. Since artificial neuron networks exploit a massive number of
neurons working in parallel, the basic functioning of the network need not be interrupted
if some of the neurons are malfunctioning. Hence, connectionist models do not suffer from
the computational brittleness of the symbolic models and they are also much less sensitive
to noise in the input.
Some connectionist systems are aiming at modelling neuronal processes in human or
animal brains. However, most systems are constructed as general models of cognition
without any ambition to map directly to what is going on in the brain. Such connectionist
systems have become popular among psychologists and cognitive scientists since they seem
to be excellent simulation tools for testing associationist theories.
Artificial neuron networks have been developed for many different kinds of cognitive
tasks, including vision, language processing, concept formation, inference, and motor
control. Among the applications, one finds several that traditionally were thought to be
typical symbol processing tasks like pattern matching and syntactic parsing. Maybe the
most important applications, however, are models of various forms of learning.
Connectionist systems brought a radically new perspective on cognitive processes:
cognition is distributed in the system. In contrast, a von Neumann computer is controlled
by a central processor. In favour of this architecture it has been argued that if the
brain is a computer, it must have a central processor - where would you otherwise find the
"I" of the brain? But the analogy does not hold water; there is no area of
the brain that serves as a pilot for the other parts: there is no one in charge. The
neuronal processes are distributed all over the brain, they occur in parallel and they are
to a certain extent independent of each other. Nevertheless, the brain functions in a
goal-directed manner. From the connectionist perspective, the brain is best seen as a
self-organising system. Rather than working with a computer-like programme, the
organization and learning that occur in the brain should be seen as an evolutionary
process (Edelman 1987).
On this view, the brain can be seen as an anthill. The individual neurons are the ants
who perform their routine jobs untiringly, but rather unintelligently, and who send
signals to other neurons via their dendrite antennas. From the interactions of a large
number of simple neurons a complex well-adapted system like an anthill emerges in the
brain. In other words, cognition is seen as a holistic phenomenon in a complex system of
distributed parallel processes.
Along with the development of symbolic and connectionist programming techniques, there
has been a rapid development in the neurosciences. More and more has been uncovered
concerning the neural substrates of different kinds of cognitive process. As the argument
by McCulloch and Pitts shows, it was thought at an early stage that the brain would
function along the same principles as a standard computer. But one of major sources of
influence for connectionism was the more and more conspicuous conclusion that neurons in
the brain are not logic circuits, but operate in a distributed and massively parallel
fashion and according to totally different principles than those of computers. For
example, Hubel and Wiesel's work on the signal-detecting functioning of the neurons in the
visual cortex were among the path-breakers for the new view on the mechanisms of the
brain. It is seen as one of the strongest assets of connectionism that the mechanisms of
artificial neuron networks are much closer to the functioning of the brain.
Another talented researcher that combined thorough knowledge about the the brain with a
computational perspective was David Marr. His book Vision from 1982 is a milestone in the
development of cognitive neuroscience. He worked out connectionist algorithms for various
stages of visual processing from the moment the cells on the retina react, until a
holistic 3D-model of the visual scene is constructed in the brain. Even though some of his
algorithms have been questioned by later developments, his methodology has led to a much
deeper understanding of the visual processes over the last two decades.
6.2 Non-symbolic theories of concept formation
There are aspects of cognitive phenomena for which neither symbolic representation nor
connectionism seem to offer appropriate modelling tools. In particular it seems that
mechanisms of concept acquisition, paramount for the understanding of many cognitive
phenomena, cannot be given a satisfactory treatment in any of these representational
forms. Concept learning is closely tied to the notion of similarity, which has also turned
out to be problematic for the symbolic and associationist approaches.
To handle concept formation, among other things, a third form of representing
information that is based on using geometrical or topological structures, rather than
symbols or connections between neurons, has been advocated (Gärdenfors, to appear). This
way of representing information is called the conceptual form. The geometrical and
topological structures generate mental spaces that represent various domains. By
exploiting distances in such spaces, judgements of similarity can be modelled in a natural
In the classical Aristotelian theory of concepts that was embraced by AI and early
cognitive science (for example, in the work of Bruner, Goodnow and Austin presented above)
a concept is defined via a set of necessary and sufficient properties. According to this
criterion, all instances of a classical concept have equal status. The conditions
characterising a concept were formulated in linguistic form, preferably in some symbolic
However, psychologists like Eleanor Rosch showed that in the majority of cases,
concepts show graded membership. These results led to a dissatisfaction with the classical
theory. As an alternative, prototype theory was proposed in the mid-1970's. The main idea
of this theory is that within a category of objects, like those instantiating a concept,
certain members are judged to be more representative of the category than others. For
example robins are judged to be more representative of the category "bird" than
are ravens, penguins and emus; and desk chairs are more typical instances of the category
"chair" than rocking chairs, deck chairs, and beanbag chairs. The most
representative members of a category are called prototypical members. The prototype theory
of concepts fits much better with the conceptual form of representing information than
with symbolic representations.
6.3 Thinking in images
Both the symbolic and the connectionistic approaches to cognition have their advantages
and disadvantages. They are often presented as competing paradigms, but since they attack
cognitive problems on different levels, they should rather be seen as complementary
When we think or speak about our own thoughts, we often refer to inner scenes or
pictures that we form in our fantasies or in our dreams. However, from the standpoint of
behaviourism, these phenomena were unspeakables, beyond the realm of the sober scientific
study of stimuli and responses. This scornful attitude towards mental images was continued
in the early years of AI. Thinking was seen as symbol crunching and images were not the
right kind of building blocks for computer programmes.
However, in the early 1970s psychologists began studying various phenomena connected
with mental imagery. Roger Shepard and his colleagues performed an experiment that has
become classical. They showed subjects pictures representing pairs of 3D block figures
that were rotated in relation to each other and asked the subjects to respond as quickly
as possible whether the two figures were the same or whether they were mirror images of
one another. The surprising finding was that the time it took the subject to answer was
linearly correlated with the number of degrees the second object had been rotated in
relation to the first. A plausible interpretation of these results is that the subjects
generate mental images of the block figures and rotate them in their minds.
Stephen Kosslyn (1980) and his colleagues have documented similar results concerning
people's abilities to imagine maps. In a typical experiment, subjects are shown maps of a
fictional island with some marked locations: a tree, a house, a bay, etc. The maps are
removed and the subjects are then asked to focus mentally on one location on the map and
then move their attention to a second location. The finding was that the time it takes to
mentally scan from one location to the other is again a linear function of the distance
between the two positions on the map. The interpretation is that the subjects are scanning
a mental map, in the same manner as they would scan a physically presented map.
Another strand of mental imagery has been developed within so called cognitive
semantics. In the Chomskian theory of linguistics, syntax is what counts and semantic and
pragmatic phenomena are treated like Cinderellas. In contrast, within cognitive semantics,
as developed by Ron Langacker (1987) and George Lakoff (1987) among others, the cognitive
representation of the meaning of linguistic expressions is put into focus. Their key
notion for representing linguistic meanings is that of an image schema. Such schemas are
abstract pictures constructed from elementary topological and geometrical structures like
"container", "link" and "source-path-goal". A common
assumption is that such schemas constitute the representational form that is common to
perception, memory, and semantic meaning. The theory of image schemas also builds on the
prototype theory for concepts. Again, this semantic theory replaces the uninterpreted
symbols of high-church computationalism with image-like representations that have an
inherent meaning. In particular, our frequent use of more or less conventional metaphors
in everyday language can be analysed in an illuminating way using image schemas.
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7.1 The embodied brain
The brain is not made for calculating - its primary duty it to control the body. For
this reason it does not function in solitude, but is largely dependent on the body it is
employed by. In contrast, when the brain was seen as a computer, it was more or less
compulsory to view it as an isolated entity. This traditional view is the starting point
for a number of science fiction stories where the brain is placed in a vat and connected
by cables to a printer or to loudspeakers. However, there is little hope that such a
scenario would ever work. As a consequence, there has recently been a marked increase in
studies of the embodied brain.
For example, the eye is not merely seen as an input device to the brain and the hand as
enacting the will of the brain, but the eye-hand-brain is seen as a coordinated system.
For many tasks, it turns out that we think faster with our hands than with our brains. A
simple example is the computer game Tetris where you are supposed to quickly turn, with
the aid of the keys on the keyboard, geometric objects that come falling over a computer
screen in order to fit them with the pattern at the bottom of the screen. When a new
object appears, one can mentally rotate it to determine how it should be turned before
actually touching the keyboard. However, expert players turn the object faster with the
aid of the keyboard than they turn an image of the object in their brains. This is an
example of what has been called interactive thinking. The upshot is that a human who is
manipulating representations in the head is not using the same cognitive system as a human
interacting directly with the represented objects.
Also within linguistics, the role of the body has attracted attention. One central
tenet within cognitive semantics is that the meanings of many basic words are embodied, in
the sense that they relate directly to bodily experiences. George Lakoff and Mark Johnson
show in their book Metaphors we Live By (1980) that a surprising variety of words, for
instance prepositions, derive their complex meaning from a basic embodied meaning which is
then extended by metaphorical mappings to a number of other domains.
7.2 Situated cognition
There is one movement within cognitive science, so-called situated cognition, which
departs even further from the traditional stance. The central idea is that in order to
function efficiently, the brain needs not only the body but also the surrounding world. In
other words, it is being there that is our primary function as cognitive agents (Clark
1997). Cognition is not emprisoned in the brain but emerges in the interaction between the
brain, the body and the world. Instead of representing the world in an inner model the
agent in most cases uses the world as its own model. For example, in vision, an agent uses
rapid saccades of the eyes to extract what is needed from a visual scene, rather than
building a detailed 3D model of the world in its head. In more general terms, the
interaction between the brain, the body and the surrounding world can be seen as a
dynamical system (Port and van Gelder 1995) along the same lines as other physical
In many cases it is impossible to draw a line between our senses and the world. The
captain of a submarine "sees" with the periscope and a blind person
"touches" with her stick, not with the hand. In the same way we
"think" with road signs, calendars, and pocket calculators. There is no sharp
line between what goes on inside the head and what happens in the world. The mind leaks
out into the world.
By arranging the world in a smart way we can afford to be stupid. We have constructed
various kinds of artifacts that help us solve cognitive tasks. In this way the world
functions as a scaffolding for the mind (Clark 1997). For example, we have developed a
number of memory aids: we "remember" with the aid of books, video tapes,
hard-disks, etc. The load on our memory is relieved, since we can retrieve the information
by reading a book or listening to a tape. In this way, memory is placed in the world. For
another practical example, the work of an architect or a designer is heavily dependent on
making different kinds of sketches: the sketching is an indispensable component of the
cognitive process (Gedenryd 1998).
The emphasis on situated cognition is coupled with a new view on the basic nature of
the cognitive structures of humans. Instead of identifying the brain with a computer, the
evolutionary origin of our thinking is put into focus. The key idea is that we have our
cognitive capacities because they have been useful for survival and reproduction in the
past. From this perspective, it becomes natural to compare our form of cognition with that
of different kinds of animals. During the last decade, evolutionary psychology has grown
considerably as a research area. The methodology of this branch is different from that of
traditional cognitive psychology. Instead of studying subjects in laboratories under
highly constrained conditions, evolutionary psychology focuses on data that are
ecologically valid in the sense that they tell us something about how humans and animals
act in natural problem-solving situations.
7.3 The pragmatic turn of linguistics
The role of culture and society in cognition was marginalised in early cognitive
science. These were regarded as problem areas to be addressed when an understanding of
individual cognition had been achieved. This neglect shows up especially clearly in the
treatment of language within cognitive science. For Chomsky and his followers, individuals
are Turing machines that process syntactic structures according to some, partly innate,
recursive system of grammatical rules. Questions concerning the meaning of the words, let
alone problems related to the use of language in communication, were seen as not properly
belonging to a cognitive theory of linguistics.
However, when the focus of cognitive theories shifted away from symbolic
representations, semantic and pragmatic research reappeared on the agenda. Broadly
speaking, one can find two conflicting views on the role of pragmatics in the study of
language. On the one hand, in mainstream contemporary linguistics (dominated by the
Chomskian school), syntax is viewed as the primary study object of linguistics; semantics
is added when grammar is not enough; and pragmatics is what is left over (context, deixis,
On the other hand, a second tradition turns the study programme up-side-down: actions
are seen as the most basic entities; pragmatics consists of the rules for linguistic
actions; semantics is conventionalised pragmatics; and finally, syntax adds grammatical
markers to help disambiguate when the context does not suffice to do so. This tradition
connects with several other research areas like anthropology, psychology, and situated
This shift of the linguistic programme can also be seen in the type of data that
researchers are considering. In the Chomskian research programme, single sentences
presented out of context are typically judged for their grammaticality. The judgments are
often of an introspective nature when the researcher is a native speaker of the language
studied. In contrast, within the pragmatic programme, actual conversations are recorded or
video-taped. For the purpose of analysis, they are transcribed by various methods. The
conversational analysis treats language as part of a more general interactive cognitive
The problem of constructing a robot is a good test of progress in cognitive science. A
robot needs perception, memory, knowledge, learning, planning and communicative abilities,
that is, exactly those capacities that cognitive science aims at understanding. Current
industrial robots have very little of these abilities: they can perform a narrow range of
tasks in a specially prepared environment. They are very inalterable: when faced with new
problems they just stall. To change their behaviour, they must be reprogrammed.
In contrast, nature has, with the stamina of evolution, solved cognitive problems by
various methods. Most animals are independent individuals, often extremely flexible. When
faced with new problems, an animal normally finds some solution, even if it is not the
optimal one. The simplest animals are classified as reactive systems. This means that they
have no foresight, but react to stimuli as they turn up in the environment. So, given
nature's solutions, why can we not construct machines with the capacity of a cockroach?
The first generation of robotics tried to build a symbol manipulating system into a
physical machine. As was discussed earlier, this methodology led to unsurmountable
problems, in particular the frame problem. The current trend in robotics is to start from
reactive systems and then add higher cognitive modules that amplify or modify the basic
reactive systems. This methodology is based on what Rodney Brooks calls the subsumption
architecture. One factor that was forgotten in classical AI is that animals have a
motivation for their behaviour. From the perspective of evolution, the utmost goals are
survivial and reproduction. In robotics, the motivation is set by the constructor.
Currently, one of the more ambitious projects within robotics is the construction of a
humanoid robot called COG at MIT. The robot is multi-modal in the sense that it has
visual, tactile and auditory input channels. It can move its head and it has two
manipulating arms. The goals for COG are set very high: in the original project plan it
was claimed that it would achieve some form of consciousness after a few years. However,
the robot is not yet conscious (and it can be doubted that, given its architecture, it
ever will be). Nevertheless, the COG project has set a new trend in robotics of
constructing full-blown cognitive agents that comply with the ideas of embodied and
situated cognition. One common feature of such robots is that that they learn by doing:
linguistic or other symbolic input play a minor role in their acquisition of new knowledge
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The goal of contemporary cognitive science is not primarily to build a thinking
machine, but to increase our understanding of cognitive processes. This can be done by
various methods, including traditional psychological experiments, observations of
authentic cognitive processes in practical action, or by simulating cognition in robots or
programmes. Unlike the early days of AI when it was believed that one single methodology,
that of symbolic representation, could solve all cognitive problems, the current trend is
to work with several forms of representations and data.
Furthermore, the studies tend to be closely connected to findings in neuroscience and
in other biological sciences. New techniques of brain imaging will continue to increase
our understanding of the multifarious processes going on in the brain. Other techniques,
like eye-tracking, will yield rich data for analysing our cognitive interaction with the
world and with the artifacts in it.
As regards the practical applications of cognitive science, a main area is the
construction of interfaces to information technology products. The aim is that IT products
should be as well adapted to the demands of human cognition as possible. In other words,
it should be the goal of information technology to build scaffolding tools that enhance
human capacities. To give some examples of already existing aids, pocket calculators help
us perform rapid and accurate calculations that were previously done laboriously with pen
and paper or even just in the head. And word processors relieve us from the strain of
retyping a manuscript.
I conjecture that the importance of cognitive design will be even greater in the
future. Donald Norman started a tradition in 1988 with his classical book The Design of
Everyday Things. He showed by a wealth of provocative examples that technical constructors
very often neglect the demands and limitations of human cognition. The user-friendliness
of computer programmes, mobile phones, remote TV controls, etc, have increased, but there
is still an immense potential to apply the findings of cognitive science in order to
create products that better support our ways of thinking and remembering. This means, I
also predict, that there will be a good employment opportunities for cognitive scientists
during the next decades. Another area where cognitive science ought to a have a great
impact in the future is education. There is a strong trend to equip schools at all levels
with more and more computers. Unfortunately, most efforts are spent on the technical and
economical aspects and very little on the question of how the computers should be used in
schools. A number of so-called educational computer programmes have been developed. With
few exceptions, however, these programmes are of a drill-and-exercise character. The
programmes are frighteningly similar to the Skinner boxes that were used to train pigeons
during the heyday of behaviourism.
In the drill-and-exercise programmes students are very passive learners. Much better
pedagogical results can be achieved if the students are given richer opportunities to
interact with the programmes. In particular, I believe that various kinds of simulation
programmes may be supportive for the learning process. For example, when teaching physics,
a programme that simulates the movements of falling bodies and displays the effects on the
screen, allowing the student to interactively change the gravitational forces and other
variables, will give a better grasp of the meaning of the physical equations than many
hours of calculation by hand.
The techniques of virtual reality have hardly left the game arcades yet. However, with
some further development, various uses of virtual reality may enhance the simulation
programmes and increase their potential as educational tools.
For the development of truly educational computer programmes, collaboration with
cognitive scientists will be mandatory. Those who design the programmes must have a deep
knowledge of how human learning and memory works, of how we situate our cognition in the
world and of how we communicate. Helping educationalists answer these questions will be
one of the greatest challenges for cognitive science in the future.
As a last example of the future trends of cognitive science, I believe that research on
the processing of sensory information will be useful in the development of tools for the
handicapped. The deaf and blind are each lacking a sensory channel. Through studies of
multi-modal communication, these sensory deficits can hopefully be aided. If we achieve
better programmes for speech recognition for example, deafness can be partly compensated
In conclusion, we can expect that in the future, cognitive science will supply man with
new tools, electronic or not, that will be better suited to our cognitive needs and that
may increase the quality of our lives. In many areas, it is not technology that sets the
limits, but rather our lack of understanding of how human cognition works.
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Peter Gärdenfors(PG) är professor i kognitionsforskning i Lund och en av Skandinviens
internationellt mest kända filosofer. Ledamot av The Royal Swedish Academy of Letters,
History and Antiquities sedan 1996. Författare till bl a Knowledge in Flux (1988), Blotta
tanken (1992) och Fängslande information (1996). PG är också en flitig skribent i
svenska dagstidningar och i fackpress.
PG:s nuvarande forskning rör huvudsakligen begreppsbildning, kognitiv semantik,
modeller för kunskap och information samt kognitionsutveckling.
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© Peter Gärdenfors 1999
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