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56 Human Consciousness and the Mind
The researcher referred to in the previous paragraph denies
this thinking. He denies it because, he says, what is considered a
representation is not actually used in the steam engine. He argues
that the angle is not used as a numerical value.
The author admits that the researcher is logically right. Indeed,
the story of the speed governor seems to suggest the existence of
“systems without representation,” as they are called in behaviorism.
Since what determines the performance of this autonomous
system is the amount of return and the time lag, it suffices in essence
if the autonomous system works as expected irrespective of whether
the existence of representation is agreed with or not.
In fact, we have today digital systems that use representations
that function in the same way as the speed governor.
I think this discussion on the speed governor will eventually lead
to the question of whether analog or digital control should be used.
Is this the reason why this is an essential discussion?
Speed governors incorporating a computer that is an aggregation
of representations are already being used in real vehicles to control
their engines.
The author therefore believes that, in essence, representations
may well be used if and when they are required in the study of
consciousness and the mind.
4.9 Qualia and Mirror Neurons
Qualia has been talked about much lately. In short, it refers to the feel
of a material. David J. Chalmers said that qualia were the hardest
problems in consciousness (Chalmers, 1996). He said so because
behaviorists agree on the existence of qualia, but it is impossible
for them to create, in a behavioristic manner, the qualia that appear
in consciousness. It is far easier for functionalists to explain, as
they need only to declare that a function to generate qualia exists
in the brain. Of course, there still remains a problem in that even
functionalism is unable to answer the question of how qualia are
created.
The discovery of mirror neurons is described here. Mirror
neurons have a special function and were discovered in 1996 by
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Qualia and Mirror Neurons 57
(a)
(b)
Figure 4.11. Mirror neuron experiments. Cited from the research of
Vittorio Gallese (1996).
G. Rizzolatti, an Italian brain science researcher (Rizzolatti, 1996).
Mirror neurons were accidentally discovered when a researcher was
having his lunch during an experiment and happened to notice that
the nerve cells of the monkey in the experiment were activated
vigorously. These particular nerve cells were also activated when
the monkey was eating its own food. Surprisingly, the brain cells
in question reacted in the same way as if the monkey itself was
eating by just seeing others eating (Fig. 4.11). Based on this finding,
the brain cell was named mirror neuron because it acts like a
mirror.
Kenichiro Mogi describes how he was shocked on hearing the
news of the discovery of mirror neurons. “Mirror neurons were a
leap over forward-thinking and system theory-oriented researchers,
and surprised many people by achieving the highest-level unifica-
tion of connecting motor information on the ‘behavior of the self’
to sensory information on the ‘behavior of the other.”’ He continues,
“Mirror neurons connect the actions of both the self and the other as
if they were reflected in a mirror. As a result, for example, ‘The other
is doing this action, so if I were doing the same action, I would feel
like such and such, and therefore, the other must also be feeling the
same way.’ The discovery seems to suggest that mirror neurons work
as a nerve module that supports the ability to suppose the mental
condition of others (the theory of the mind)” (Mogi, 2003). Mogi
assumes that the mirror neurons in monkeys are closely associated
with their behavior of pretending to be like humans.
The next topic is affordance theory and autopoiesis theory, which
are currently attracting general attention.
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58 Human Consciousness and the Mind
4.10 Affordance and Autopoiesis Theory
Affordance is explained in detail by Toru Nishigaki (Nishigaki, 1999).
American experimental psychologist James Gibson introduced
the theory of affordance in the 1960s. Affordance is an attempt
to understand the problem of human consciousness and the mind
from the viewpoint of interactions between the human and the
environment. According to affordance theory, an object in the
environment acts on (“affords”) the human (the subject), thereby
allowing the human to understand the object. For example, when
an individual looks at an object that is a baseball, the individual
is given the meaning from the baseball that “this is something for
playing with that is thrown, caught, and hit.” When a dog sees the
baseball, the dog is given the meaning from the baseball that “this is
something for playing with and to be chased after and bitten.”
The affordance theory asserts that the meaning of an object in
an environment is defined by the interaction between the subject
and the environment. According to affordance theory, the meaning
of an object is not subjective information arbitrarily interpreted by
the subject based on stimuli received from the environment, but the
meaning is instead information existing in the environment itself,
which is objective information described by the environment and is
simply transmitted to the subject. On the basis of this thinking, the
affordance theory intends to find a solution to the problem of subject
and object that is as objective a solution as possible.
The autopoiesis theory closely resembles the affordance theory.
It was introduced by Francisco Varela (1946–2001).
The difference between affordance and autopoiesis is that with
the former the meaning of an object is the objective information
given by the environment, whereas with the latter that objective
information constructs, or invents, the meaning of the object using
experiential knowledge already learned within the subject.
4.11 Embodied Cognitive Science and Symbol Grounding
Problem
This section introduces embodied cognitive science, which has been
attracting the attention of many robot researchers today.
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Embodied Cognitive Science and Symbol Grounding Problem 59
Embodied cognitive science is a belief that intelligence can only
be understood in combination with physical systems. This concept
captured the hearts of many researchers because it specifically
proclaims that intelligence is in need of an embodied mechanical
system. Embodied cognitive science represented a large flow of
research covering the major achievements of robot researchers in
the 1980s, which was later summarized by Rolf Pfeifer, Federal
Institute of Technology Zurich, Switzerland, and other researchers
(Pfeifer, 2001).
Pfeifer and other researchers considered that the study of
artificial intelligence and robots had been critically blocked by two
major obstacles, and he tried to achieve a breakthrough using a new
keyword: embodiment. These obstacles are the frame problem and
symbol grounding problem.
“Frame” refers to a symbolically represented knowledge data-
base, and the term was originally proposed by Marvin Minsky. He de-
clared that human intelligence could be realized on a computer using
symbolic inference within two or three years. Certainly, artificial
intelligence using symbolic inference contributed to the develop-
ment of the MYCIN medical expert system designed to examine and
diagnose infectious diseases and a robot simulator called SHRDLU
(previously mentioned), which performed intellectual tasks as
instructed by humans although in the world of building blocks.
In 1997, the IBM research group developed Deep Blue, a chess-
playing computer. Readers may remember that the machine played
chess with world champion Garry Kasparov and won a five-game
match by two wins, one loss, and two draws. Deep Blue symbolized
and processed the chessboard and the positions of the chess pieces
and anticipated the next moves of its opponent by symbol-based
reasoning.
There is no doubt that symbolic inference is one of the
basic techniques for achieving artificial intelligence, but the frame
problem prevented the results for use in the future possibilities of
artificial intelligence.
John McCarthy is said to be the first person to point out
the frame problem (1969). The essence of the frame problem
is this: It is an illusion to believe that the knowledge database
describes everything, as it is obvious that knowledge databases
cannot describe the entire mass of knowledge. A nuclear reactor
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60 Human Consciousness and the Mind
Figure 4.12. Frame problem.
maintenance robot, for example, operates on a knowledge database
that describes the structure and functions of the nuclear reactor.
Considering an extreme case, let us assume the reactor were
damaged by an earthquake or other accident; then the robot would
not have been able to use the knowledge database and therefore
would not move and do anything (Fig. 4.12). Generally, there
is another problem in that working environments are changing
continually and this is not a simple problem that can be solved by
just modifying parts of the knowledge database and updating it for
the robot to cope with changes in its environment. This is the frame
problem: the robot is required to respond to the situation using
“common sense,” but this is unknown to robots.
The other problem is the symbol grounding problem. It is said
that Stevan Harnad first mentioned the symbol grounding problem.
The essence of the symbol grounding problem is how to define the
relationships between symbols used for processing intelligence and
real things in the environment (or “mapping”). Mapping does not
constitute a problem when humans stand in between the “symbols
in the robot” and the “environmental stimuli to be mapped by such
symbols,” and interpret the relationships between them. Mapping
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