A Study of Cognitive Orbits based on Man-Machine Interactions

Cheng, Ran; Changyong, Liu; Shimin, Meng

doi:10.2174/1874110x01509012694

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…The analysis of sense-making in search activities develops in directions that highlight what might be called "networks of cognitive relevance" of identified cliques. This is explicitly the case with the attractive idea of "cognitive orbits" studied from man-machine interactions in a low-dimensional cognitive manifold and leading to "cognitive topological structures" [42]. A neighbor approach lies in the idea of learning interaction kernels for agent systems on Riemannian manifolds [43]; networks have something to say and might make sense in orienting alternatives in cognitive processes.…”

Section: Meta Learning Experience In the Analysis Of Documentary Routesmentioning

confidence: 99%

Retrieving Adversarial Cliques in Cognitive Communities: A New Conceptual Framework for Scientific Knowledge Graphs

et al. 2022

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The variety and diversity of published content are currently expanding in all fields of scholarly communication. Yet, scientific knowledge graphs (SKG) provide only poor images of the varied directions of alternative scientific choices, and in particular scientific controversies, which are not currently identified and interpreted. We propose to use the rich variety of knowledge present in search histories to represent cliques modeling the main interpretable practices of information retrieval issued from the same “cognitive community”, identified by their use of keywords and by the search experience of the users sharing the same research question. Modeling typical cliques belonging to the same cognitive community is achieved through a new conceptual framework, based on user profiles, namely a bipartite geometric scientific knowledge graph, SKG GRAPHYP. Further studies of interpretation will test differences of documentary profiles and their meaning in various possible contexts which studies on “disagreements in scientific literature” have outlined. This final adjusted version of GRAPHYP optimizes the modeling of “Manifold Subnetworks of Cliques in Cognitive Communities” (MSCCC), captured from previous user experience in the same search domain. Cliques are built from graph grids of three parameters outlining the manifold of search experiences: mass of users; intensity of uses of items; and attention, identified as a ratio of “feature augmentation” by literature on information retrieval, its mean value allows calculation of an observed “steady” value of the user/item ratio or, conversely, a documentary behavior “deviating” from this mean value. An illustration of our approach is supplied in a positive first test, which stimulates further work on modeling subnetworks of users in search experience, that could help identify the varied alternative documentary sources of information retrieval, and in particular the scientific controversies and scholarly disputes.

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Section: Meta Learning Experience In the Analysis Of Documentary Routesmentioning

confidence: 99%

Retrieving Adversarial Cliques in Cognitive Communities: A New Conceptual Framework for Scientific Knowledge Graphs

et al. 2022

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Topology Analysis of Learning Cognitive Flow for Human-Computer Interaction

Shi

Yang

Liu

et al. 2018

2018 9th International Conference on Information Technology in Medicine and Education (ITME)

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An HCI-Based Cognitive Architecture for Learning Process Observation

Shi¹,

Yang²,

Chang-yong³

et al. 2020

International Journal of Distance Education Technologies

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In this article, a cognitive framework for observing learning activities based on human-computer coupling is proposed. The observation is based on the vectorization of a learning situation along with human-computer interaction factors. An evolutionary high-dimensional topology of learning cognitive flow is introduced for human-computer interaction. In addition, the authors have selected a tree topology as the topological structure of a low-dimensional learning space to process the observations for online learning. Furthermore, the mechanism for the BSM (brain cognitive body-situation of coupling-manifold of information) the coupling morphism is presented. The principle for the coupled observation of objects in a cognitive or learning manifold is proposed. Finally, a special system for teaching and learning is programmed to observe and evaluate learning and mental arithmetic training processes. This system not only provides students with a new ergonomic learning model but also records the students' learning processes. Thus, the teachers can summarize the knowledge points automatically rather than manually.

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A Study of Cognitive Orbits based on Man-Machine Interactions

Cited by 3 publications

References 3 publications

Retrieving Adversarial Cliques in Cognitive Communities: A New Conceptual Framework for Scientific Knowledge Graphs

Retrieving Adversarial Cliques in Cognitive Communities: A New Conceptual Framework for Scientific Knowledge Graphs

Topology Analysis of Learning Cognitive Flow for Human-Computer Interaction

An HCI-Based Cognitive Architecture for Learning Process Observation

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