The ordered perception grounding the referential competence
Barbara Giolito, Università del Piemonte Orientale “A. Avogadro”
(barbara.giolito@infinito.it)
First premise of my proposal is the distinction, developed by Diego Marconi, between
inferential competence and referential competence: by ‘inferential competence’ we
designate the capability of connecting lexical items with each others, by ‘referential
competence’ we designate the capacity of connecting lexical items to the external
world. An important consequence of this distinction is that we can assume that a
theory could be a good explanation for just one of these two competences without
claiming that such a theory is enough for the whole meaning of words. The separation
between inferential and referential capacity can be applied to the same word: most
words have indeed both an inferential and a referential part in their meaning.
Nevertheless, for different words one of these two aspects can be more important than
the other, and for some words just one of them can be relevant (abstract or functional
words can be lacking the referential aspect). The hypothesis of such a distinction, and
the connected possibility to study inferential and referential capacity separately, can
be relevant against some criticisms concerning the difficulty in explaining the
meaning of words by the sensorial perception of world. Indeed, these criticisms are in
part founded on the existence of some completely abstract words and on the existence
of some abstract aspects in most words. On the contrary, we could refer to perception
just as an explanation source for the referential aspects of some words, while we
could need another sort of explanation for inferential aspects, where we could look for
the explanation of most abstract aspects of words.
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In particular, we can try to explain some aspects of the meaning of words by our
capacity of perceiving the world in a conceptually ordered way. Indeed, if our
perception is already ordered in different categories by our sensory system work, such
categories could ground the meaning of some of our lexical items or at least part of it.
This attempt can be based on some experiments on neural networks, supporting my
second premise, that is the possibility of an influence between the sensory system
work and the conceptual system work. A connectionist model, supporting the
existence of Categorical perception, is developed by Harnad, Hanson and Lubin. They
consider the ability of back-propagation neural networks to learn to discriminate 8
lines of different lengths in two categories (‘short’ and ‘long’ lines). The lines are
presented in different ways: they can have a ‘place-code’, so that a line of length 4
would be represented as 0 0 0 1 0 0 0 0, or a ‘thermometer-code’, so that the same line
would be represented as 1 1 1 1 0 0 0 0. The place-code is assumed to be more
arbitrary and the thermometer-code is assumed to be more analog, because the fist one
preserves some multi-unit constraints not preserved by the place-code. To produce the
pre-categorization discrimination function, an auto-association method is used. For
each network, the interstimulus-distances for all pairs of the 8 lines are calculated as
the Euclidean distance between the vectors of hidden unit activations. After the autoassociation task, the trained connection weights between hidden units and outputs
units are reloaded for a new double task: the auto-association task and a
categorization task. The categorisation task consists in naming lines 1 - 4 by the
arbitrary name ‘short’ and lines 5 – 8 by the arbitrary name ‘long’. The interstimulusdistances of the networks that have been trained on the categorization task are
compared to the pre-categorization values for the same networks: a decrease in
within-category interstimulus-distances and an increase in between-categories
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interstimulus-distances are considered Categorical Perception effects. These effects
emerge for all representations. The networks acquire the capacity to sort inputs in
categories imposed by supervised learning, shifting the distances between them until
there is enough within-category compression and between-category separation to
achieve reliable categorization. In other studies, the number of stimuli is increased
from 8 to 12 and the number of categories from 2 to 3 (‘short’, ‘medium’ and ‘long’
lines). The average results are basically the same, but - in the last work - the placecode shows a Categorical Perception effect consisting both in compression withincategory and in separation between-categories, while the thermometer-code shows a
Categorical Perception effect mainly consisting in separation between-categories:
representations using this kind of code are already grouped together after the autoassociation phase because of their analog nature, making compression effects less
useful and small. These studies on neural networks suggest that Categorical
Perception arises with more strength in difficult and confused cases and support the
plausibility of Categorical Perception phenomena, at least in terms of reciprocal
influences between sensory system work and conceptual system work.
Such a fact is important in the study of language meaning. The possibility that
concepts influence sense data suggests a plausible link between concepts and sensory
perception, and this could support the hypothesis that some aspects of the meaning of
concepts depend on our perception of the world: our conceptual structure could
depend on our natural capability to interpret sense data in a meaningful way.
Consequently, we could consider the conceptual structure supporting the meaning of
language in part as a by-product of Categorical Perception: some lexical terms could
be grounded in the capability to discriminate and categorize the real word objects,
events and states of affairs to which they can be interpreted as referring. Our
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referential competence could be grounded in our natural capacity to perceive the
world in an ordered way.
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