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.
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Acknowledgement
This article is a revised version of the paper presented at the Second European Conference Computing and Philosophy (E-CAP2004), held at the University of Pavia, Italy (June 3–5, 2004) and chaired by Lorenzo Magnani.
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Pessa, E., Terenzi, G. Semiosis in cognitive systems: a neural approach to the problem of meaning. Mind & Society 6, 189–209 (2007). https://doi.org/10.1007/s11299-007-0033-7
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DOI: https://doi.org/10.1007/s11299-007-0033-7