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Books > Language & Literature > Language & linguistics > Semantics (meaning)
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Neuromimetic Semantics - Coordination, quantification, and collective predicates (Hardcover, New)
Loot Price: R3,973
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Neuromimetic Semantics - Coordination, quantification, and collective predicates (Hardcover, New)
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This book attempts to marry truth-conditional semantics with
cognitive linguistics in the church of computational neuroscience.
To this end, it examines the truth-conditional meanings of
coordinators, quantifiers, and collective predicates as
neurophysiological phenomena that are amenable to a
neurocomputational analysis. Drawing inspiration from work on
visual processing, and especially the simple/complex cell
distinction in early vision (V1), we claim that a similar two-layer
architecture is sufficient to learn the truth-conditional meanings
of the logical coordinators and logical quantifiers.
As a prerequisite, much discussion is given over to what a
neurologically plausible representation of the meanings of these
items would look like. We eventually settle on a representation in
terms of correlation, so that, for instance, the semantic input to
the universal operators (e.g. and, all)is represented as maximally
correlated, while the semantic input to the universal negative
operators (e.g. nor, no)is represented as maximally anticorrelated.
On the basis this representation, the hypothesis can be offered
that the function of the logical operators is to extract an
invariant feature from natural situations, that of degree of
correlation between parts of the situation. This result sets up an
elegant formal analogy to recent models of visual processing, which
argue that the function of early vision is to reduce the redundancy
inherent in natural images.
Computational simulations are designed in which the logical
operators are learned by associating their phonological form with
some degree of correlation in the inputs, so that the overall
function of the system is as a simple kindof pattern recognition.
Several learning rules are assayed, especially those of the Hebbian
sort, which are the ones with the most neurological support.
Learning vector quantization (LVQ) is shown to be a perspicuous and
efficient means of learning the patterns that are of interest. We
draw a formal parallelism between the initial, competitive layer of
LVQ and the simple cell layer in V1, and between the final, linear
layer of LVQ and the complex cell layer in V1, in that the initial
layers are both selective, while the final layers both generalize.
It is also shown how the representations argued for can be used to
draw the traditionally-recognized inferences arising from
coordination and quantification, and why the inference of
subalternacy breaks down for collective predicates.
Finally, the analogies between early vision and the logical
operators allow us to advance the claim of cognitive linguistics
that language is not processed by proprietary algorithms, but
rather by algorithms that are general to the entire brain. Thus in
the debate between objectivist and experiential metaphysics, this
book falls squarely into the camp of the latter. Yet it does so by
means of a rigorous formal, mathematical, and neurological
exposition - in contradiction of the experiential claim that formal
analysis has no place in the understanding of cognition. To make
our own counter-claim as explicit as possible, we present a sketch
of the LVQ structure in terms of mereotopology, in which the
initial layer of the network performs topological operations, while
the final layer performs mereological operations.
The book is meant to be self-contained, in the sense that it does
not assume any priorknowledge of any of the many areas that are
touched upon. It therefore contains mini-summaries of biological
visual processing, especially the retinocortical and ventral
/what?/ parvocellular pathways; computational models of neural
signaling, and in particular the reduction of the Hodgkin-Huxley
equations to the connectionist and integrate-and-fire neurons;
Hebbian learning rules and the elaboration of learning vector
quantization; the linguistic pathway in the left hemisphere; memory
and the hippocampus; truth-conditional vs. image-schematic
semantics; objectivist vs. experiential metaphysics; and
mereotopology. All of the simulations are implemented in MATLAB,
and the code is available from the book's website.
-The discovery of several algorithmic similarities between visison
and semantics.
-The support of all of this by means of simulations, and the
packaging of all of this in a coherent theoretical framework.
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