In the search of creating a representation, such as a cognitive trait model, of cognitive traits, such as working memory capacity or inductive reasoning ability, of a learner, it is hard to find a consensus model of the cognitive trait among different perspectives of cognitive science. Dichotomic node network (DNN) is developed to provide a viable solution to this problem. DNN is a network representation of an entity of which the constituents are nodes that is consisted of a pair of dichotomic attributes. Through the contradiction detection mechanism and inclusion resolution mechanism, DNN is able to (1) represents of an entity contains multiple portrayals/perspectives, (2) select appropriate portrayals for any particular entity is very difficult or impossible, (3) handle nonlinear aggregation of portrayals in which combinations does not render result linearly, and therefore very suitable for cognitive trait model, and is potential for other applications.
