Improving the cognitive clarity of graph models of knowledge  representation and decision-making using visualization

Isaev, Ruslan; Podvesovskiy, Aleksandr

doi:10.30987/2658-4026-2021-1-27-35

Cited by 8 publications

(4 citation statements)

References 5 publications

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“…In [11][12][13][14], the authors investigated aspects of application of visualization metaphors in the problems of visualization of fuzzy cognitive maps and graph models in general. Summarizing the results obtained, the process of visualizing a graph model using a visualization metaphor can be represented as a diagram in Fig.…”

Section: Visualization Metaphors As a Means Of Increasing Cognitive Clarity Of Graph Modelsmentioning

confidence: 99%

“…In [14] using an example of visualization of fuzzy cognitive maps, the authors showed that use of visualization metaphors allows structuring and partially formalizing the task of increasing cognitive clarity of a visual representation. They also hypothesized that a similar effect can be achieved through the use of visualization metaphors for graph models of other types.…”

Section: Fig 1 Graph Model Visualization Using a Metaphormentioning

confidence: 99%

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Visualization of Graph Models: An Approach to Construction of Representation Metaphors

Isaev¹,

Podvesovskii²

2021

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The paper presents development of the authors' approach to visualization of graph models of various types based on the use of visualization metaphors and aimed at increasing cognitive clarity of these models. One of the key problems of this approach is investigatednamely, formalization of the process of constructing representation metaphors for graph models. Features of graph models that allow formalizing the process of their visualization are considered, the necessary terminology is introduced. A number of principles have been formulated that must be considered when forming metaphors for representing graph models. On the basis of the introduced principles, a general approach to the construction of representation metaphors for visualization of arbitrary graph models is proposed. The main ideas for applying the proposed approach are demonstrated by examples of constructing representation metaphors for two types of graph models: fuzzy cognitive maps and Bayesian networks. In order to discuss the results, a contradiction between the volume of a representation metaphor and its cognitive clarity is formulated, and a hypothesis is proposed about the relationship of this contradiction with Hick's law. The feasibility of experimental study of this relationship and the refinement of its parameters, including with the aim of developing recommendations for the construction of efficient representation metaphors of graph models, is noted. In the future, the presented approach can become an important component of an integrated approach to building a visualization mechanism for an arbitrary graph model, which provides support for efficient visual analysis throughout all stages of modeling.

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Section: Visualization Metaphors As a Means Of Increasing Cognitive Clarity Of Graph Modelsmentioning

confidence: 99%

Section: Fig 1 Graph Model Visualization Using a Metaphormentioning

confidence: 99%

Visualization of Graph Models: An Approach to Construction of Representation Metaphors

Isaev¹,

Podvesovskii²

2021

View full text Add to dashboard Cite

show abstract

Section: Visualization Metaphors As a Means Of Increasing Cognitive Clarity Of Graph Modelsmentioning

confidence: 99%

“…In [14], the authors, using an example of visualization of fuzzy cognitive maps, showed that use of visualization metaphors allows structuring and partially formalizing the task of increasing cognitive clarity of a visual representation. They also hypothesized that a similar effect can be achieved through the use of visualization metaphors for graph models of other types.…”

Section: Visualization Metaphors As a Means Of Increasing Cognitive Clarity Of Graph Modelsmentioning

confidence: 99%

Development of an Approach to Building Representation Metaphors for Visualizing Graph Models (by the Example of Cognitive Maps)

Isaev

Podvesovskii

2021

Proceedings of the 31th International Conference on Computer Graphics and Vision. Volume 2

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The paper presents development of the authors’ approach to visualization of graph models of various types based on the use of visualization metaphors and aimed at increasing cognitive clarity of these models. One of the key problems of this approach is investigated – namely, formalization of the process of constructing representation metaphors for graph models. Features of graph models that allow formalizing the process of their visualization are considered, the necessary terminology is introduced. A number of principles have been formulated that must be considered when forming metaphors for representing graph models. On the basis of the introduced principles, a general approach to the construction of representation metaphors for visualization of arbitrary graph models is proposed. The main ideas for applying the proposed approach are demonstrated by the example of a fuzzy cognitive map when constructing a metaphor for representing the results of its structure and target analysis. The directions of advanced research have been determined, within the framework of which it is planned to further formalize the approach in order to ensure the possibility of its software implementation. In the future, the presented approach can become an important component of an integrated approach to building a visualization mechanism for an arbitrary graph model, which provides support for efficient visual analysis throughout all stages of modeling.

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An Experimental Approach to Evaluating the Influence of Factors on the Cognitive Clarity of Graph Models

Isaev

Podvesovskii

2022

Proceedings of the 32nd International Conference on Computer Graphics and Vision

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The paper describes the author's new approach to understanding the idea of cognitive clarity of graph models. A conceptual scheme of structuring concepts related to cognitive clarity is given. According to this scheme, the following are distinguished: factors of cognitive clarity formation, cognitive clarity itself as a set of internal characteristics of the visual image of the model, as well as the effects of its presence manifested in the visual analysis of the model. The properties of the various components of this scheme are discussed, and it is concluded that it is the factors of cognitive clarity formation that are of the greatest interest, due to their constructiveness. Based on this, we propose an approach to the organization of experimental research aimed at studying and evaluating the influence of various factors on the cognitive clarity of graph models, by measuring various indicators that characterize the degree of manifestation of the effects of the presence of cognitive clarity. The idea of this approach is to form a hypothesis about the influence of some factors, followed by an experiment on the analyst's solution of a certain visual analysis problem involving these factors. As a result of recording the given indicators the achieved effect of changes in the level of cognitive clarity is evaluated and the nature of dependence or its absence is revealed, which allows accepting, rejecting or refining the initial hypothesis. A generalized algorithm for preparing and conducting an experiment in the framework of the described approach is proposed, the peculiarities of its individual stages are considered. An example of staging an experiment, the purpose of which is to study the dependence of indicators of efficiency of visual analysis of graph model on the volume of its visual image, is given. The conclusion discusses promising directions for research in this area.

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Improving the cognitive clarity of graph models of knowledge representation and decision-making using visualization

Cited by 8 publications

References 5 publications

Visualization of Graph Models: An Approach to Construction of Representation Metaphors

Visualization of Graph Models: An Approach to Construction of Representation Metaphors

Development of an Approach to Building Representation Metaphors for Visualizing Graph Models (by the Example of Cognitive Maps)

An Experimental Approach to Evaluating the Influence of Factors on the Cognitive Clarity of Graph Models

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