Exploring How Phonotactic Knowledge Can Be Represented in Cognitive Networks

Abstract

In Linguistics and Psycholinguistics, phonotactics refers to the constraints on individual sounds in a given language that restrict how those sounds can be ordered to form words in that language. Previous empirical work in Psycholinguistics demonstrated that phonotactic knowledge influenced how quickly and accurately listeners retrieved words from that part of memory known as the mental lexicon. In the present study, we used three computer simulations to explore how three different cognitive network architectures could account for the previously observed effects of phonotactics on processing. The results of Simulation 1 showed that some—but not all—effects of phonotactics could be accounted for in a network where nodes represent words and edges connect words that are phonologically related to each other. In Simulation 2, a different network architecture was used to again account for some—but not all—effects of phonotactics and phonological neighborhood density. A bipartite network was used in Simulation 3 to account for many of the previously observed effects of phonotactic knowledge on spoken word recognition. The value of using computer simulations to explore different network architectures is discussed.

What Do Cognitive Networks Do? Simulations of Spoken Word Recognition Using the Cognitive Network Science Approach

“…Further, as described above, we believe that the cognitive network approach has much potential to account for an increasing number of phenomena in speech perception and spoken word recognition, as well as other areas of Cognition. Furthermore, the cognitive network approach may not only account for the same phenomena in speech perception and spoken word recognition that the TRACE models can [ 56 ], but may also account for phenomena that TRACE and other contemporary models of spoken word recognition cannot account for, such as the influence that the structure of the lexicon at various scales has on processing.…”

Investigating Cognitive Network Models of Learners’ Knowledge Representations

“…To date, this body of research on language networks demonstrates that the structural properties of words in the language network (as indexed by various network centrality measures) have behavioural effects on the speed and efficiency of word recognition (Chan & Vitevitch, 2009;Goldstein & Vitevitch, 2017;Luce & Pisoni, 1998). Recent computational work further illustrates how these behavioural effects can be accounted for by implementing a simple process of spreading activation in the phonological network (Siew, 2019;Vitevitch et al, 2021). This is a significant idea because it emphasizes the importance of considering the structure of the cognitive network in tandem with the processes that operate within the network (Strogatz, 2001).…”

A methodology framework for bipartite network modeling

“…Apart from that, the bipartite network approach has been widely applied in the studies of social sciences or social networks. This includes the studies of disease transmission networks (Büttner and Krieter 2020 ; Hernándex and Risau-Gusman 2013 ; Rafo et al 2021 ), biological system networks (Baumgartner 2020 ), food-web networks (Michalko et al 2021 ), ecological network (Elliott et al 2021 ), cognitive network (Vitevitch et al 2021 ), and governance-leadership relationship in a development policy network (Rudnick et al 2019 ).…”