Event-related electric microstates of the brain differ between words with visual and abstract meaning

The present study shows that different neural activity during mental imagery and abstract mentation can be assigned to well-defined steps of the brain's information-processing. During randomized visual presentation of single, imagery-type and abstract-type words, 27 channel event-related potential (ERP) field maps were obtained from 25 subjects (sequence-divided into a first and second group for statistics). The brain field map series showed a sequence of typical map configurations that were quasi-stable for brief time periods (microstates). The microstates were concatenated by rapid map changes. As different map configurations must result from different spatial patterns of neural activity, each microstate represents different active neural networks. Accordingly, microstates are assumed to correspond to discrete steps of information-processing. Comparing microstate topographies (using centroids) between imagery- and abstract-type words, significantly different microstates were found in both subject groups at 286–354 ms where imagery-type words were more right-lateralized than abstract-type words, and at 550–606 ms and 606–666 ms where anterior-posterior differences occurred. We conclude that language-processing consists of several, well-defined steps and that the brain-states incorporating those steps are altered by the stimuli's capacities to generate mental imagery or abstract mentation in a state-dependent manner.

The FORA Framework - A Fuzzy Grassroots Ontology for Online Reputation Management

Online reputation management deals with monitoring and influencing the online record of a person, an organization or a product. The Social Web offers increasingly simple ways to publish and disseminate personal or opinionated information, which can rapidly have a disastrous influence on the online reputation of some of the entities. The author focuses on the Social Web and possibilities of its integration with the Semantic Web as resource for a semi-automated tracking of online reputations using imprecise natural language terms. The inherent structure of natural language supports humans not only in communication but also in the perception of the world. Thereby fuzziness is a promising tool for transforming those human perceptions into computer artifacts. Through fuzzy grassroots ontologies, the Social Semantic Web becomes more naturally and thus can streamline online reputation management. For readers interested in the cross-over field of computer science, information systems, and social sciences, this book is an ideal source for becoming acquainted with the evolving field of fuzzy online reputation management in the Social Semantic Web area. ​

A Restriction-Centered Theory of Reasoning and Computation to materialize a Web 3.0

Researchers suggest that personalization on the Semantic Web adds up to a Web 3.0 eventually. In this Web, personalized agents process and thus generate the biggest share of information rather than humans. In the sense of emergent semantics, which supplements traditional formal semantics of the Semantic Web, this is well conceivable. An emergent Semantic Web underlying fuzzy grassroots ontology can be accomplished through inducing knowledge from users' common parlance in mutual Web 2.0 interactions [1]. These ontologies can also be matched against existing Semantic Web ontologies, to create comprehensive top-level ontologies. On the Web, if augmented with information in the form of restrictions andassociated reliability (Z-numbers) [2], this collection of fuzzy ontologies constitutes an important basis for an implementation of Zadeh's restriction-centered theory of reasoning and computation (RRC) [3]. By considering real world's fuzziness, RRC differs from traditional approaches because it can handle restrictions described in natural language. A restriction is an answer to a question of the value of a variable such as the duration of an appointment. In addition to mathematically well-defined answers, RRC can likewise deal with unprecisiated answers as "about one hour." Inspired by mental functions, it constitutes an important basis to leverage present-day Web efforts to a natural Web 3.0. Based on natural language information, RRC may be accomplished with Z-number calculation to achieve a personalized Web reasoning and computation. Finally, through Web agents' understanding of natural language, they can react to humans more intuitively and thus generate and process information.

A Concept of Semantics Extraction from Web Data by Induction of Fuzzy Ontologies

Traditionally, ontologies describe knowledge representation in a denotational, formalized, and deductive way. In addition, in this paper, we propose a semiotic, inductive, and approximate approach to ontology creation. We define a conceptual framework, a semantics extraction algorithm, and a first proof of concept applying the algorithm to a small set of Wikipedia documents. Intended as an extension to the prevailing top-down ontologies, we introduce an inductive fuzzy grassroots ontology, which organizes itself organically from existing natural language Web content. Using inductive and approximate reasoning to reflect the natural way in which knowledge is processed, the ontology’s bottom-up build process creates emergent semantics learned from the Web. By this means, the ontology acts as a hub for computing with words described in natural language. For Web users, the structural semantics are visualized as inductive fuzzy cognitive maps, allowing an initial form of intelligence amplification. Eventually, we present an implementation of our inductive fuzzy grassroots ontology Thus,this paper contributes an algorithm for the extraction of fuzzy grassroots ontologies from Web data by inductive fuzzy classification.

The FORA Framework - A Fuzzy Grassroots Ontology for Online Reputation Management

Online reputation management deals with monitoring and influencing the online record of a person, an organization or a product. The Social Web offers increasingly simple ways to publish and disseminate personal or opinionated information, which can rapidly have a disastrous influence on the online reputation of some of the entities. This dissertation can be split into three parts: In the first part, possible fuzzy clustering applications for the Social Semantic Web are investigated. The second part explores promising Social Semantic Web elements for organizational applications,while in the third part the former two parts are brought together and a fuzzy online reputation analysis framework is introduced and evaluated. Theentire PhD thesis is based on literature reviews as well as on argumentative-deductive analyses.The possible applications of Social Semantic Web elements within organizations have been researched using a scenario and an additional case study together with two ancillary case studies—based on qualitative interviews. For the conception and implementation of the online reputation analysis application, a conceptual framework was developed. Employing test installations and prototyping, the essential parts of the framework have been implemented.By following a design sciences research approach, this PhD has created two artifacts: a frameworkand a prototype as proof of concept. Bothartifactshinge on twocoreelements: a (cluster analysis-based) translation of tags used in the Social Web to a computer-understandable fuzzy grassroots ontology for the Semantic Web, and a (Topic Maps-based) knowledge representation system, which facilitates a natural interaction with the fuzzy grassroots ontology. This is beneficial to the identification of unknown but essential Web data that could not be realized through conventional online reputation analysis. Theinherent structure of natural language supports humans not only in communication but also in the perception of the world. Fuzziness is a promising tool for transforming those human perceptions intocomputer artifacts. Through fuzzy grassroots ontologies, the Social Semantic Web becomes more naturally and thus can streamline online reputation management.

Notes on a Queer Brain: Can there be a Critical Research on Sex/Gender in Neuroscience?

Anelis Kaiser is associate researcher at the Center for Cognitive Science at the University of Freiburg, Germany. Dr. Kaiser recently co-edited a special issue of the journal Neuroethics on gender and brain science. She is co-founder (with Isabelle Dussauge) of the interdisciplinary network NeuroGenderings, which brings together experts from the brain sciences, the humanities and science studies (STS) to critically study the sexed brain. She has published on sex and gender as constructed categories in science as well as on the topics of multilingualism and language processing in the brain. Co-sponsored with the Center for Lesbian and Gay Studies. - See more at: http://www.gc.cuny.edu/Page-Elements/Academics-Research-Centers-Initiatives/Centers-and-Institutes/Center-for-the-Study-of-Women-and-Society/Center-Events#sthash.bDeBg5fk.dpuf

White matter in aphasia: a historical review of the Dejerines' studies

The Objective was to describe the contributions of Joseph Jules Dejerine and his wife Augusta Dejerine-Klumpke to our understanding of cerebral association fiber tracts and language processing. The Dejerines (and not Constantin von Monakow) were the first to describe the superior longitudinal fasciculus/arcuate fasciculus (SLF/AF) as an association fiber tract uniting Broca's area, Wernicke's area, and a visual image center in the angular gyrus of a left hemispheric language zone. They were also the first to attribute language-related functions to the fasciculi occipito-frontalis (FOF) and the inferior longitudinal fasciculus (ILF) after describing aphasia patients with degeneration of the SLF/AF, ILF, uncinate fasciculus (UF), and FOF. These fasciculi belong to a functional network known as the Dejerines' language zone, which exceeds the borders of the classically defined cortical language centers. The Dejerines provided the first descriptions of the anatomical pillars of present-day language models (such as the SLF/AF). Their anatomical descriptions of fasciculi in aphasia patients provided a foundation for our modern concept of the dorsal and ventral streams in language processing.

"Sounds Funny? – Humor Effects of Phonological and Prosodic Figures of Speech"

Whereas semantic, logical, and narrative features of verbal humor are well-researched, phonological and prosodic dimensions of verbal funniness are hardly explored. In a 2 × 2 design we varied rhyme and meter in humorous couplets. Rhyme and meter enhanced funniness ratings and supported faster processing. Rhyming couplets also elicited more intense and more positive affective responses, increased subjective comprehensibility and more accurate memory. The humor effect is attributed to special rhyme and meter features distinctive of humoristic poetry in several languages. Verses that employ these formal features make an artful use of typical poetic vices of amateurish poems written for birthday parties or other occasions. Their metrical patterning sounds “mechanical” rather than genuinely “poetic”; they also disregard rules for “good” rhymes. The processing of such verses is discussed in terms of a metacognitive integration of their poetically deviant features into an overall effect of processing ease. The study highlights the importance of nonsemantic rhetorical features in language processing.

Enhanced Knowledge Management by Synchronizing Mind Maps and Fuzzy Cognitive Maps

This paper presents a conceptual approach to enhance knowledge management by synchronizing mind maps and fuzzy cognitive maps. The use of mind maps allows taking advantage of human creativity, while the application of fuzzy cognitive maps enables to store information expressed in natural language. By applying cognitive computing, it makes possible to gather and extract relevant information out of a data pool. Therefore, this approach is supposed to give a framework that enhances knowledge management. To demonstrate the potential of this framework, a use case concerning the development of a smart city app is presented.

Tratamiento de resultados en diseños cualitativos : La aplicación del Análisis Descriptivo

Este artículo presenta el Análisis Descriptivo como una estrategia del tratamiento de la información durante el proceso de investigación y su posible uso en estudios de diseño cualitativo. Muchas investigaciones en Ciencias Sociales y Humanas no contemplan la importancia de explicitar los soportes teórico-metodológicos de las inferencias explicativas o interpretación/es a la/s que se arriba, es decir, cómo es que se ha pasado del referente seleccionado (unidad de referencia), al argumento (modelo explicativo o interpretativo) con el que se lo pretende representar. De este modo, se suele ignorar el problema de la representación del referente en un dato tratable y la necesaria transformación del lenguaje natural (LN) en lenguaje descriptivo (LD). Se desarrollan dos ejemplos del campo de la Etología y de la Psicología, aplicando la estrategia metodológica del Análisis Descriptivo. En ellos se demuestra que la codificación que permite realizar este método toma en cuenta por un lado, la base de conocimientos e informaciones relativas a un dominio disciplinar particular y, por otro, permite evidenciar las inferencias seguidas en el razonamiento y las reglas de interpretación utilizadas para arribar a nuevos conocimientos

Lógica formal y argumentación

Este artículo explora críticamente la tesis según la cual la lógica formal deductiva contemporánea proporciona métodos e instrumentos para una teoría de la evaluación de argumentos formulados en un lenguaje natural. En este artículo se sostiene que la teoría de la (in)validez de la lógica formal deductiva sólo se puede aplicar a los argumentos del lenguaje natural utilizando aquello que se quiere explicar teóricamente, i.e. Las intuiciones que los/las hablantes de un lenguaje natural tienen acerca de la relaciones de implicación lógica entre las expresiones de esa lengua. Se exploran también algunas consecuencias pedagógicas de esta crítica.

Tratamiento de resultados en diseños cualitativos : La aplicación del Análisis Descriptivo

Este artículo presenta el Análisis Descriptivo como una estrategia del tratamiento de la información durante el proceso de investigación y su posible uso en estudios de diseño cualitativo. Muchas investigaciones en Ciencias Sociales y Humanas no contemplan la importancia de explicitar los soportes teórico-metodológicos de las inferencias explicativas o interpretación/es a la/s que se arriba, es decir, cómo es que se ha pasado del referente seleccionado (unidad de referencia), al argumento (modelo explicativo o interpretativo) con el que se lo pretende representar. De este modo, se suele ignorar el problema de la representación del referente en un dato tratable y la necesaria transformación del lenguaje natural (LN) en lenguaje descriptivo (LD). Se desarrollan dos ejemplos del campo de la Etología y de la Psicología, aplicando la estrategia metodológica del Análisis Descriptivo. En ellos se demuestra que la codificación que permite realizar este método toma en cuenta por un lado, la base de conocimientos e informaciones relativas a un dominio disciplinar particular y, por otro, permite evidenciar las inferencias seguidas en el razonamiento y las reglas de interpretación utilizadas para arribar a nuevos conocimientos