Toward a taxonomic model of attention in effortful listening

In recent years, there has been increasing interest in studying listening effort. Research on listening effort intersects with the development of active theories of speech perception and contributes to the broader endeavor of understanding speech perception within the context of neuroscientific theories of perception, attention, and effort. Due to the multidisciplinary nature of the problem, researchers vary widely in their precise conceptualization of the catch-all term listening effort. Very recent consensus work stresses the relationship between listening effort and the allocation of cognitive resources, providing a conceptual link to current cognitive neuropsychological theories associating effort with the allocation of selective attention. By linking listening effort to attentional effort, we enable the application of a taxonomy of external and internal attention to the characterization of effortful listening. More specifically, we use a vectorial model to decompose the demand causing listening effort into its mutually orthogonal external and internal components and map the relationship between demanded and exerted effort by means of a resource-limiting term that can represent the influence of motivation as well as vigilance and arousal. Due to its quantitative nature and easy graphical interpretation, this model can be applied to a broad range of problems dealing with listening effort. As such, we conclude that the model provides a good starting point for further research on effortful listening within a more differentiated neuropsychological framework

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 dimension