Semiosis in cognitive systems: a neural approach to the problem of meaning [Book Review]

Mind and Society 6 (2):189-209 (2007)
Abstract
This paper deals with the problem of understanding semiosis and meaning in cognitive systems. To this aim we argue for a unified two-factor account according to which both external and internal information are non-independent aspects of meaning, thus contributing as a whole in determining its nature. To overcome the difficulties stemming from this approach we put forward a theoretical scheme based on the definition of a suitable representation space endowed with a set of transformations, and we show how it can be implemented, in the case of a single agent, by a neural network architecture. Numerical experiments conducted on different instances of the latter show that similar representations are developed as a consequence of the fact that these instances are facing a similar semantic task. This allows to model social and environmental influences through a system of interacting agents, each described by a specific implementation of this model architecture.
Keywords Semiosis  Linguistic symbols  Meaning  Two-factor accounts  Neural networks  Symbol categorization  Semantic processing  Cross-system relations
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DOI 10.1007/s11299-007-0033-7
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Perceptual Symbol Systems.Lawrence W. Barsalou - 1999 - Behavioral and Brain Sciences 22 (4):577-660.
Advertisement for a Semantics for Psychology.Ned Block - 1986 - Midwest Studies in Philosophy 10 (1):615-78.

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Semiosis in Cognitive Systems.Graziano Terenzi - 2008 - Semiotica 2008 (171):131-162.

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