Backward spatial perception can be augmented through a novel visual-to-auditory sensory substitution algorithm

Can humans extend and augment their natural perceptions during adulthood? Here, we address this fascinating question by investigating the extent to which it is possible to successfully augment visual spatial perception to include the backward spatial field (a region where humans are naturally blind) via other sensory modalities (i.e., audition). We thus developed a sensory-substitution algorithm, the “Topo-Speech” which conveys identity of objects through language, and their exact locations via vocal-sound manipulations, namely two key features of visual spatial perception. Using two different groups of blindfolded sighted participants, we tested the efficacy of this algorithm to successfully convey location of objects in the forward or backward spatial fields following ~ 10 min of training. Results showed that blindfolded sighted adults successfully used the Topo-Speech to locate objects on a 3 × 3 grid either positioned in front of them (forward condition), or behind their back (backward condition). Crucially, performances in the two conditions were entirely comparable. This suggests that novel spatial sensory information conveyed via our existing sensory systems can be successfully encoded to extend/augment human perceptions. The implications of these results are discussed in relation to spatial perception, sensory augmentation and sensory rehabilitation.

Multivariate versus Univariate Sensor Selection for Spatial Field Estimation

The paper discusses the sensor selection problem in estimating spatial fields. It is demonstrated that selecting a subset of sensors depends on modelling spatial processes. It is first proposed to exploit Gaussian process (GP) to model a univariate spatial field and multivariate GP (MGP) to jointly represent multivariate spatial phenomena. A Mat´ern cross covariance function is employed in the MGP model to guarantee its cross-covariance matrices to be positive semi-definite. We then consider two corresponding univariate and multivariate sensor selection problems in effectively monitoring multiple spatial random fields. The sensor selection approaches were implemented in the real-world experiments and their performances were compared. Difference of results obtained by the univariate and multivariate sensor selection techniques is insignificant; that is, either of the methods can be efficiently used in practice.

Multivariate versus Univariate Sensor Selection for Spatial Field Estimation

The paper discusses the sensor selection problem in estimating spatial fields. It is demonstrated that selecting a subset of sensors depends on modelling spatial processes. It is first proposed to exploit Gaussian process (GP) to model a univariate spatial field and multivariate GP (MGP) to jointly represent multivariate spatial phenomena. A Mat´ern cross covariance function is employed in the MGP model to guarantee its cross-covariance matrices to be positive semi-definite. We then consider two corresponding univariate and multivariate sensor selection problems in effectively monitoring multiple spatial random fields. The sensor selection approaches were implemented in the real-world experiments and their performances were compared. Difference of results obtained by the univariate and multivariate sensor selection techniques is insignificant; that is, either of the methods can be efficiently used in practice.

Multivariate versus Univariate Sensor Selection for Spatial Field Estimation

<pre>The paper discusses the sensor selection problem in estimating spatial fields. It is demonstrated that selecting a subset of sensors depends on modelling spatial processes. It is first proposed to exploit Gaussian process (GP) to model a univariate spatial field and multivariate GP (MGP) to jointly represent multivariate spatial phenomena. A Mat\'ern cross-covariance function is employed in the MGP model to guarantee its cross-covariance matrices to be positive semi-definite. We then consider two corresponding \textit{univariate} and \textit{multivariate sensor selection} problems in effectively monitoring multiple spatial random fields. The sensor selection approaches were implemented in the real-world experiments and their performances were compared. Difference of results obtained by the univariate and multivariate sensor selection techniques is insignificant; that is, either of the methods can be efficiently used in practice.</pre>

Multivariate versus Univariate Sensor Selection for Spatial Field Estimation

<pre>The paper discusses the sensor selection problem in estimating spatial fields. It is demonstrated that selecting a subset of sensors depends on modelling spatial processes. It is first proposed to exploit Gaussian process (GP) to model a univariate spatial field and multivariate GP (MGP) to jointly represent multivariate spatial phenomena. A Mat\'ern cross-covariance function is employed in the MGP model to guarantee its cross-covariance matrices to be positive semi-definite. We then consider two corresponding \textit{univariate} and \textit{multivariate sensor selection} problems in effectively monitoring multiple spatial random fields. The sensor selection approaches were implemented in the real-world experiments and their performances were compared. Difference of results obtained by the univariate and multivariate sensor selection techniques is insignificant; that is, either of the methods can be efficiently used in practice.</pre>

Nations as Gravitational Fields of Culture: In Defence of “Nationology”

Nations have been questioned as meaningful units for analyzing culture. Against this skepticism, we underline that culture is always a collective phenomenon, commonly understood as the prevalent values in a population that form its mentality and identity in differentiation from others. Nations are population entities that are manifest in states as their organizational frame, in countries as their territorial space, and in national identity as their psychological glue. Territorial in character, nations form spatial fields of ‘cultural gravitation.’ Above and beneath nations, other spatial fields of cultural gravitation exist, like sub-national regions (beneath) and geo-political areas (above). There are also non-spatial forces of cultural gravitation, including language, ethnicity, religion, social class, gender, and generation. To operationalize nations as gravitational fields of culture, we look at them in terms of their central tendencies and these tendencies’ densities and variance-binding powers, rather than understanding nations as monolithic and closed cultural containers. Because national culture is foundational for societal institutions and guides individuals’ behavior, it is of intrinsic interest for the social sciences to study culture at the nation-level, even in the presence of internal heterogeneity and cross-border similarity. Whenever of interest, sub- and supra-national cultural groups as well as non-spatial cultural groups should also be studied, but our theoretical framework warrants the use of nations as meaningful gravitational units for analyzing the dimensions and dynamics of culture.