Proceedings of the Annual Meeting of the Cognitive Science Society

Cognition is complex. This complexity is related tomultiple, distributed neurocognitive processes dynamicallyoperating across parallel scales, resulting in cognitiveprocessing. A major challenge in studying this complexity,relates to the abstractness of theoretical cognitive constructs,such as language, memory, or thinking in general. Suchabstractness is operationalized, indirectly, via behavioral,measures or in neural activity. In the past two decades, anincreasing number of studies have been applying networkscience methodologies across diverse scientific fields tostudy complex systems.Network science is based on mathematical graph theory,providing quantitative methods to investigate complexsystems as networks (Baronchelli, Ferrer-i-Cancho, Pastor-Satorras, Chater, & Christiansen, 2013; Siew, Wulff,Beckage, & Kenett, 2018). A network is comprised fromnodes, that represent the basic unit of the system (e.g.,concepts in semantic memory) and links, or edges, thatsignify the relations between them (e.g. semantic similarity).While the application of network science methodologies hasbecome an extremely popular approach to study brainstructure and function, it has been used to study cognitivephenomena to a much lesser extent. This, despite classiccognitive theory in language and memory being highlyrelated to a network perspective (Collins & Loftus, 1975;Siew et al., 2018). Already, network science in cognitivescience has enabled the direct examination of the theory thathigh creative individuals have a more flexible semanticmemory structure, identified mechanisms of languagedevelopment through preferential attachment, shed novellight on statistical learning, shown how specific semanticmemory network parameters influence memory retrieval,and provided new insight on the structure of semanticnetwork of second language in bilinguals (Siew et al.,2018).The aim of the current symposia is to demonstrate thepotential and strength of applying network sciencemethodologies to study cognition. This will be achieved bybringing together leading researchers that apply suchmethods to study various aspects of cognition, includinglanguage, learning, aging, and creativity. The presentationswill describe state-of-the-art progress and perspectives thatare achieved in applying these methods to study cognition.Importantly, these talks aim at stimulating discussion of thefruitfulness of such an approach and how such an approachcan powerfully and quantitatively study the complexity ofcognitive phenomena. Finally, this symposium aims todemonstrate how network science in cognitive science canbe used to quantitatively bridge across different levels ofanalysis, spanning the computational, behavioral, neural,and social.