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Conceptual spaces as a framework for knowledge representation
Gärdenfors, Peter
Published in:
Behavioral and Brain Sciences
DOI:
10.1017/S0140525X04280098
2004
Link to publication
Citation for published version (APA):
Gärdenfors, P. (2004). Conceptual spaces as a framework for knowledge representation.
Behavioral and Brain
Sciences
,
27
(3), 403. https://doi.org/10.1017/S0140525X04280098
Total number of authors:
1
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closely tied his account to the physiological literature, this short-
coming might have been evaded.
Emulators as sources of hidden cognitive
variables
Peter Gärdenfors
Department of Cognitive Science, Lund University, Kungshuset, S-222 22
Lund, Sweden. Peter.Gardenfors@lucs.lu.se
http://www.lucs.lu.se/People/Peter.Gardenfors/
Abstract: I focus on the distinction between sensation and perception.
Perceptions contain additional information that is useful for interpreting
sensations. Following Grush, I propose that emulators can be seen as con-
taining (or creating) hidden variables that generate perceptions from sen-
sations. Such hidden variables could be used to explain further cognitive
phenomena, for example, causal reasoning.
I have a great deal of sympathy for Grush’s emulator model. Al-
beit still rather programmatic, it promises a powerful methodol-
ogy that can generate a multitude of applications in the cognitive
sciences.
Grush presents some evidence concerning the neural sub-
strates of the emulators. However, this evidence is based on dif-
ferent kinds of neuroimaging. In my opinion, one should rather be
looking for functional units in the brain, described in neurocom-
putational terms that can be interpreted as some kind of Kalman
filter. At a low level, the example from Duhamel et al. (1992) con-
cerning saccade anticipation seems to be such a system. However,
the functional units should be searched for at higher levels of cog-
nition as well. What ought to be developed is a way of combining
the modeling techniques of artificial neuron nets with the control
theoretical principles of Kalman filters (see the volume by Haykin
[2001] for some first steps). What is needed, in particular, is an ac-
count of how a Kalman filter can adapt to the successes or failures
of the controlled process.
As used in traditional control theory, Kalman filters operate
with a limited number of control variables. In his presentation in
section 2.3, Grush presumes that the emulator has the same set of
variables as the process to be controlled. Although he notes that
this is a special case and mentions that the variables of the emula-
tor may be different from those of the process itself, he never pre-
sents alternative versions of the filters.
Now, from the perspective of the evolution of cognition, the dis-
tinction between sensation and perception that Grush makes in
section 5.1 is of fundamental importance (Gärdenfors 2003;
Humphrey 1993). Organisms that have perceptions are, in gen-
eral, better prepared for what is going to happen in their environ-
ment. My proposal is that perceptions are generated by emulators
and they function as forward models.
One important property of an emulator is that it does not need
to rely exclusively on the signals coming from sense organs; it can
also add on new types of information that can be useful in emu-
lating. As a matter of fact, Grush (1998) has written about this pos-
sibility himself:
The emulator is free to “posit” new variables and supply their values as
part of the output. A good adaptive system would posit those variables
which helped the controller [. . .] They are variables which are not part
of the input the emulator gets from the target system. They may be the
actual parameters of the target system, they may not. But what is im-
portant is that the emulator’s output may be much richer than the sen-
sory input it receives from the target system. (emphasis in original)
It does not matter much if the added information has no direct
counterpart in the surrounding world as long as the emulation pro-
duces the right result, that is, leads to appropriate control signals.
The information provided by these variables is what generates
the difference between sensations and perceptions. For example,
when the system observes a moving object, its sensations consist
only of the positions of the object, whereas the forces that influ-
ence the movement of the object are not sensed. However, if the
system has been able to extract “force” as a hidden variable and
relates this to the sensations via something like Newton’s Second
Law, then the system would be able to make more efficient and
general, if not more accurate, predictions.
In section 2.2, Grush makes the point that emulators must have
a certain degree of plasticity. This is not sufficient: A general the-
ory must also account for how an emulator can learn to control a
system. Supposedly, it slowly adjusts its filter settings (and set of
variables) on the basis of some form of reward or punishment
feedback from the process to be controlled. This would be analo-
gous to how artificial neuron networks learn. Such a form of learn-
ing may pick up higher-order correlations between input and out-
put. These correlations may be expressed by the hidden variables
of the emulator.
The hidden variables of the multimodal emulators that Grush
discusses in section 6.1, may provide the system (the brain) with
cognitive abilities such as object permanence. More generally, one
would expect the multimodal emulator to represent the world in
an object-centered framework, rather than in a viewer-centered
one (Marr 1982). As Grush (1998) writes: “[S]pace is a theoretical
posit of the nervous system, made in order to render intelligible
the multitude of interdependencies between the many motor
pathways going out, and the many forms of sensory information
coming in. Space is not spoon-fed to the cognizer, but is an
achievement.” Another speculation is that phenomena related to
categorical perception are created by the hidden variables of the
emulator.
More generally, different kinds of emulators may produce the
variables that are used in causal reasoning. An interesting finding
is that there is a substantial difference between humans and other
animal species. As has been shown by Povinelli (2000) and others,
monkeys and apes are surprisingly bad at reasoning about physi-
cal causes of phenomena. Tomasello (1999, p. 19) gives the fol-
lowing explanation of why monkeys and apes cannot understand
causal mechanisms and intentionality in others: “It is just that they
do not view the world in terms of intermediate and often hidden
‘forces,’ the underlying causes and intentional/mental states, that
are so important in human thinking.”
On the other hand, even very small human children show strong
signs of interpreting the world with the aid of hidden forces and
other causal variables. Gopnik (1998, p. 104) claims that “other an-
imals primarily understand causality in terms of the effects of their
own actions on the world. In contrast, human beings combine that
understanding with a view that equates the causal power of their
own actions and those of objects independent of them.” Appar-
ently, humans have more advanced causal emulators than other
animals.
Finally, as Grush mentions in section 6.3.2, another relevant
area is our “theory of mind,” that is, the ability of humans to em-
ulate (yes, not simulate) the intentions and beliefs of other indi-
viduals. An important question for future research then becomes:
Why do humans have all these, apparently very successful, emu-
lators for causes and a theory of mind, and why do other species
not have them? A research methodology based on emulators and
Kalman filters may provide the right basis for tackling these ques-
tions.
Commentary/Grush: The emulation theory of representation: Motor control, imagery, and perception
BEHAVIORAL AND BRAIN SCIENCES (2004) 27:3 403