Abstract
Verification of cognitive models is one of the most important stages in their construction, since reliability of results of subsequent modeling largely depends on the successful implementation of verification. The paper considers the problem of verifying cause-and-effect relationships in cognitive models based on the use of fuzzy cognitive maps. It is noted that increasing the effectiveness of cognitive model verification is possible by activating analyst's cognitive potential. The most natural way of such activation is to increase cognitive clarity of the model through the use of visualization capabilities. For this purpose, a number of metaphors for visualizing fuzzy cognitive maps have been proposed, aimed at increasing their cognitive clarity during verification. Each of the metaphors is focused on the visualization of a certain type of fragments of a fuzzy cognitive map potentially containing errors, redundancy or incompleteness and therefore of interest from the point of view of verification. The first considered visualization metaphor is intended to display the cycles that are part of a cognitive graph. The second metaphor focuses on the mapping of transitive paths between concepts. Finally, the third metaphor is aimed at eliminating cognitive model incompleteness, which consists in the lack of relationships between some concepts. Examples are given of applying the proposed visualization metaphors to increase cognitive clarity of the visual image of the verified fuzzy cognitive map.
Highlights
This paper continues a series of publications of the authors’ research materials in the field of visualization of cognitive models based on fuzzy cognitive maps (FCM)
The research is based on the hypothesis of an increase in the efficiency of FCM verification by increasing cognitive clarity of its visual image
An important feature of the proposed visualization metaphor is the possibility of adjusting its spatial component in order to increase cognitive clarity of the visual image of the FCM
Summary
With regard to an FCM, such a metaphor is based on graph visualization algorithms and formalized criteria of cognitive clarity [3]. These criteria describe requirements for the FCM visual image quality. Observing these requirements simplifies visual perception of the cognitive model by the analyst. This leads to a general increase in the speed of working with the model, and helps to reduce the number of errors made at various stages of modeling. The research is based on the hypothesis of an increase in the efficiency of FCM verification by increasing cognitive clarity of its visual image
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