Towards hypergraph cognitive networks

Traditional network models for human memory organization represent conceptual associations using pairwise links. However, conceptual assocations can naturally be depicted as hyperlinks involving more than two concepts. Using word associations data, we introduced two studies where pairwise networks and hypergraphs were compared. In the first study, we quantitatively investigate whether there is any benefit in using the hypergraph model over a pairwise network in predicting (i) test-based age of acquisition norms in children up to age 9 years and (ii) normative learning in toddlers up to age 30 months. In the second study, we build hypergraphs from word associations and use evaluation methods from machine learning features to predict concept concreteness. Our studies reveal that hypergraphs contain richer information and better predict word concreteness in adults, and age of acquisition trends in toddlers. The shift from pairwise networks to hypergraphs represents a significant advancement in memory modeling, offering deeper insights into cognitive phenomena and developmental processes.