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Computational Models of Cognitive Processes: Proceedings of by Julien Mayor, Pablo Gomez

By Julien Mayor, Pablo Gomez

Computational versions of Cognitive approaches collects refereed models of papers provided on the thirteenth Neural Computation and Psychology Workshop (NCPW13) that happened July 2012, in San Sebastian (Spain). This workshop sequence is a well-established and targeted discussion board that brings jointly researchers from such various disciplines as synthetic intelligence, cognitive technology, desktop technology, neurobiology, philosophy and psychology to debate their most modern paintings on types of cognitive approaches.

Readership: scholars and researchers in biocybernetics, neuroscience, cognitive technology, psychology and synthetic intelligence and people attracted to neural types of mental phenomena.

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G. 1–4 ). These attempts differ on how the sentence constituents (parts) are represented—either locally 1,3 , or in a distributed fashion 5,6 —and on how these constituents are bound together—through either temporal synchrony 7 , active circuits 3 , or algebraic operations 8 . The local representation of each sentence constituent (either a word, a phrase, or even a proposition) results in an exponential growth in the number of units needed for structure representation 1 ; this challenge was addressed in 3 by designing dynamic circuits between word assemblies, yet with a highly complex and meticulously (unrealistic) organized connections.

Rodriguez-Fornells, Journal of Cognitive Neuroscience 23, 3105 (2011). J. Bahlmann, R. I. Schubotz and A. D. Friederici, Neuroimage 42, 525 (2008). J. Lany and J. Saffran, Psychological Science 21, 284 (2010). D. Amit, Modeling Brain Function: The World of Attractor Neural Networks (Cambridge University Press, 1992). J. Hopfield, Proceedings of the National Academy of Sciences 79, 2554 (1982). I. Kanter, Physical Review A 37, 2739 (1988). A. Treves, Cognitive Neuropsychology 22, 276 (2005). C. Fulvi Mari and A.

We have also made a distinction between the encoding of function words and content words, as suggested by several studies 30,31 . While we keep the overall activity for these two categories the same over the network, semantic units are less active for the function words than for the content words, while syntactic units are more active for the function words than for the content words. 0 Syn Fig. 2. The sentences produced by the interaction of these two sub-networks are written on the top. 1. October 17, 2013 18:10 8747-Computational Models - Proceeding-9 x 6in pirmoradian 41 By distributing a word on a network, we stayed away from extreme localized approaches in which the sentence constructs are represented on distinct set of units 1,3 ; on the other hand, by having a sparse representation of the words, which are implemented as a set of features localized on Potts units, we did not follow extreme distributed approaches 5 .

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