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Inference and representation: philosophical and cognitive issues I. F. Mikhailov

By: Mikhailov, Igor FMaterial type: ArticleArticleContent type: Текст Media type: электронный Subject(s): логические выводы | репрезентация | когнитивная наукаGenre/Form: статьи в журналах Online resources: Click here to access online In: Вестник Томского государственного университета. Философия. Социология. Политология № 58. С. 34-46Abstract: The paper is dedicated to particular cases of interaction and mutual impact of philosophy and cognitive science. Thus, philosophical preconditions in the middle of the 20th century shaped the newly born cognitive science as mainly based on conceptual and propositional representations and syntactical inference. Further developments towards neural networks and statistical representations did not change the prejudice much: many still believe that network models must be complemented with some extra tools that would account for proper human cognitive traits. I address some real implemented connectionist models that show how ‘new associationism’ of the neural network approach may not only surpass Humean limitations, but, as well, realistically explain abstraction, inference and prediction. Then I stay on Predictive Processing theories in a little more detail to demonstrate that sophisti-cated statistical tools applied to a biologically realist ontology may not only provide solu-tions to scientific problems or integrate different cognitive paradigms but propose some philosophical insights either. To conclude, I touch on a certain parallelism of Predictive Processing and philosophical inferentialism as presented by Robert Brandom.
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Библиогр.: 45 назв.

The paper is dedicated to particular cases of interaction and mutual impact of philosophy and cognitive science. Thus, philosophical preconditions in the middle of the 20th century shaped the newly born cognitive science as mainly based on conceptual and propositional representations and syntactical inference. Further developments towards neural networks and statistical representations did not change the prejudice much: many still believe that network models must be complemented with some extra tools that would account for proper human cognitive traits. I address some real implemented connectionist models that show how ‘new associationism’ of the neural network approach may not only surpass Humean limitations, but, as well, realistically explain abstraction, inference and prediction. Then I stay on Predictive Processing theories in a little more detail to demonstrate that sophisti-cated statistical tools applied to a biologically realist ontology may not only provide solu-tions to scientific problems or integrate different cognitive paradigms but propose some philosophical insights either. To conclude, I touch on a certain parallelism of Predictive Processing and philosophical inferentialism as presented by Robert Brandom.

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