Vector-based Approach to Verbal Cognition
Human verbal thinking is an object of many multidisciplinary studies. Verbal cognition is often an integration of complex mental activities, such as neurocognitive and psychological processes. In neuro-cognitive study of language, neural architecture and neuropsychological mechanism of verbal cognition are basis of a vector – based modeling. Human mental states, as constituents of mental continuum, represent an infinite set of meanings. Number of meanings is not limited, but numbers of words and rules that are used for building complex verbal structures are limited. Verbal perception and interpretation of the multiple meanings and propositions in mental continuum can be modeled by applying tensor methods. A comparison of human mental space to a vector space is an effective way of analyzing of human semantic vocabulary, mental representations and rules of clustering and mapping. As such, Euclidean and non-Euclidean spaces can be applied for a description of human semantic vocabulary and high order. Additionally, changes in semantics and structures can be analyzed in 3D and other dimensional spaces. It is suggested that different forms of verbal representation should be analyzed in a light of vector (tensor) transformations. Vector dot and cross product, covariance and contra variance have been applied to analysis of semantic transformations and pragmatic change in high order syntax structures. These ideas are supported by empirical data from typologically different languages such as Mongolian, English and Russian. Moreover, the author argues that the vectorbased approach to cognitive linguistics offers new opportunities to develop an alternative version of quantitative semantics and, thus, to extend theory of Universal grammar in new dimensions.
