Detecting Errors in Cognitive Models Using Visualization Metaphors of Fuzzy Cognitive Maps

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.