Day 3

at Virtual MathPsych/ICCM 2024

Jun 17 @ 20:00 EDT - Jun 19 @ 19:00 EDT

Public session

Has a live component

Jun 17 @ 21:00 EDT - Jun 19 @ 19:00 EDT

Public session

Has a live component

Jun 17 @ 22:00 EDT - Jun 19 @ 19:00 EDT

Public session

Has a live component

Cognitive network science is a growing field at the fringe of mathematical psychology, graph theory and computational network science. A cognitive network is a representation of associative knowledge, where nodes represent atomistic cognitive units (e.g. concepts, word forms, graphemes, etc.) linked together by one or multiple types of cognitive associations (e.g. memory recall patterns, co-occurrences in words, phonological similarities, etc.). Vastly disseminated in psychology in the 60s/70s thanks to the works of Collins, Quillian and Loftus, cognitive networks have been recently re-discovered with the advent of novel computational tools from graph theory but also thanks to the increasing availability of large-scale cognitive datasets, enabling novel methodological frameworks for investigating the interplay between the structure of associative knowledge and a variety of psychological phenomena. This symposium, proposed to MathPsych/ICCM 2024 VIRTUAL, focuses on the exploration of cognitive networks as both representational and computational models for understanding cognitive phenomena. The representational capacity of cognitive networks is highlighted through their ability to act as proxies of the complex organization of associative knowledge. In the last 20 years, several studies, including Steyvers and Tenenbaum (Cog. Sci. 2005), Vitevitch (ASLHA, 2008), Hills et al. (Psych. Sci. 2009) and Kenett et al. (PNAS, 2018) have accumulated strong scientific evidence that associative structure, as incompletely captured by cognitive networks, can explain behavioral data ranging from lexical processing to language acquisition, from word confusability to semantic memory functioning. The recent advent of multiplex lexical networks (Stella et al., Sci.Rep. 2017), encoding simultaneously several types of conceptual associations, opens the way to quantitatively exploring how the interplay between different aspects of associative knowledge (e.g. semantic and phonological representations) can affect the above cognitive phenomena. As remarked in the recent review of the field by Siew et al. (Compl. 2019), cognitive networks are also powerful computational tools. Network provide a substrate for simulating models like spreading activation or mental search via random walks and simple diffusion. Thus, cognitive networks can provide unprecedented ways to: (i) test multiple experimental scenarios by changing simulation parameters or network structure, e.g. simulating spreading activation across multiple priming conditions, (Siew, Beh. Res. Met. 2019); (ii) open the way to automatic frameworks capturing, predicting and understanding psychological phenomena relative to the presence of specific constructs or personality traits, e.g. capturing openness to experience via network exploration (Samuel et al., J. Pers. Res. 2023). These interpretable computational approaches are importantly driven by decades of psychological theories and promote not only computational psychology but also our understanding of psychological and cognitive phenomena. The CNS symposium will provide a crucial opportunity to engage with the latest applications of graph theoretical investigations of cognitive networks, elucidating how the structure of cognitive networks can reveal important characteristics of cognitive processing, such as information flow efficiency, robustness, and the role of hub nodes in cognitive architectures. By integrating empirical findings with theoretical models, the symposium aims to advance our comprehension of cognitive networks in mathematical psychology and cognitive data science. Participants will engage with the latest research methodologies and computational tools used in the analysis of cognitive networks, fostering a deeper scientific understanding of how these models can be applied to investigate cognitive phenomena. This interdisciplinary workshop is designed to facilitate rigorous academic dialogue, encouraging collaboration across fields to refine and expand the application of cognitive networks in understanding the complexities of human cognition.