106 research outputs found
Knowledge Modelling and Learning through Cognitive Networks
One of the most promising developments in modelling knowledge is cognitive network science, which aims to investigate cognitive phenomena driven by the networked, associative organization of knowledge. For example, investigating the structure of semantic memory via semantic networks has illuminated how memory recall patterns influence phenomena such as creativity, memory search, learning, and more generally, knowledge acquisition, exploration, and exploitation. In parallel, neural network models for artificial intelligence (AI) are also becoming more widespread as inferential models for understanding which features drive language-related phenomena such as meaning reconstruction, stance detection, and emotional profiling. Whereas cognitive networks map explicitly which entities engage in associative relationships, neural networks perform an implicit mapping of correlations in cognitive data as weights, obtained after training over labelled data and whose interpretation is not immediately evident to the experimenter. This book aims to bring together quantitative, innovative research that focuses on modelling knowledge through cognitive and neural networks to gain insight into mechanisms driving cognitive processes related to knowledge structuring, exploration, and learning. The book comprises a variety of publication types, including reviews and theoretical papers, empirical research, computational modelling, and big data analysis. All papers here share a commonality: they demonstrate how the application of network science and AI can extend and broaden cognitive science in ways that traditional approaches cannot
Complexity Heliophysics: A lived and living history of systems and complexity science in Heliophysics
In this piece we study complexity science in the context of Heliophysics,
describing it not as a discipline, but as a paradigm. In the context of
Heliophysics, complexity science is the study of a star, interplanetary
environment, magnetosphere, upper and terrestrial atmospheres, and planetary
surface as interacting subsystems. Complexity science studies entities in a
system (e.g., electrons in an atom, planets in a solar system, individuals in a
society) and their interactions, and is the nature of what emerges from these
interactions. It is a paradigm that employs systems approaches and is
inherently multi- and cross-scale. Heliophysics processes span at least 15
orders of magnitude in space and another 15 in time, and its reaches go well
beyond our own solar system and Earth's space environment to touch planetary,
exoplanetary, and astrophysical domains. It is an uncommon domain within which
to explore complexity science.
After first outlining the dimensions of complexity science, the review
proceeds in three epochal parts: 1) A pivotal year in the Complexity
Heliophysics paradigm: 1996; 2) The transitional years that established
foundations of the paradigm (1996-2010); and 3) The emergent literature largely
beyond 2010.
This review article excavates the lived and living history of complexity
science in Heliophysics. The intention is to provide inspiration, help
researchers think more coherently about ideas of complexity science in
Heliophysics, and guide future research. It will be instructive to Heliophysics
researchers, but also to any reader interested in or hoping to advance the
frontier of systems and complexity science
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