A number of variables—word frequency, word length—have long been known to influence language processing. We briefly review the effects in speech perception and production of two more recently examined variables: phonotactic probability and neighborhood density. We then describe a new approach to study language, network science, which is an interdisciplinary field drawing from mathematics, computer science, physics, and other disciplines. In this approach, nodes represent individual entities in a system (i.e., phonological word-forms in the lexicon), links between nodes represent relationships between nodes (i.e., phonological neighbors), and various measures enable researchers to assess the micro-level (i.e., the individual word), the macro-level (i.e., characteristics about the whole system), and the meso-level (i.e., how an individual fits into smaller sub-groups in the larger system). Although research on individual lexical characteristics such as word-frequency has increased our understanding of language processing, these measures only assess the “micro-level.” Using network science, researchers can examine words at various levels in the system, and how each word relates to the many other words stored in the lexicon. Several new findings using the network science approach are summarized to illustrate how this approach can be used to advance basic research as well as clinical practice.

An emerging area of research in cognitive science is the utilization of networks to model the structure and processes of the mental lexicon in healthy and clinical populations, like aphasia. Previous research has focused on only one type of word similarity at a time (e.g., semantic relationships), even though words are multi-faceted. Here, we investigate lexical retrieval in a picture naming task from people with Broca's and Wernicke's aphasia and healthy controls by utilizing a multiplex network structure that accounts for the interplay between multiple semantic and phonological relationships among words in the mental lexicon. Extending upon previous work, we focused on the global network measure of closeness centrality which is known to capture spreading activation, an important process supporting lexical retrieval. We conducted a series of logistic regression models predicting the probability of correct picture naming. We tested whether multiplex closeness centrality was a better predictor of picture naming performance than single-layer closeness centralities, other network measures assessing local and meso-scale structure, psycholinguistic variables, and group differences. We also examined production gaps, or the difference between the likelihood of producing a word with the lowest and highest closeness centralities. Our results indicated that multiplex closeness centrality was a significant predictor of picture naming performance, where words with high closeness centrality were more likely to be produced than words with low closeness centrality. Additionally, multiplex closeness centrality outperformed single-layer closeness centralities and other multiplex network measures, and remained a significant predictor after controlling for psycholinguistic variables and group differences. Furthermore, we found that the facilitative effect of closeness centrality was similar for both types of aphasia. Our results underline the importance of integrating multiple measures of word similarities in cognitive language networks for better understanding lexical retrieval in aphasia, with an eye toward future clinical applications.

In the Speech-to-Song Illusion, repetition of a spoken phrase results in it being perceived as if it were sung. Although a number of previous studies have examined which characteristics of the stimulus will produce the illusion, there is, until now, no description of the cognitive mechanism that underlies the illusion. We suggest that the processes found in Node Structure Theory that are used to explain normal language processing as well as other auditory illusions might also account for the Speech-to-Song Illusion. In six experiments we tested whether the satiation of lexical nodes, but continued priming of syllable nodes may lead to the Speech-to-Song Illusion. The results of these experiments provide evidence for the role of priming, activation, and satiation as described in Node Structure Theory as an explanation of the Speech-to-Song Illusion.

Modelling the structure of cognitive systems is a central goal of the cognitive sciences—a goal that has greatly benefitted from the application of network science approaches. This paper provides an overview of how network science has been applied to the cognitive sciences, with a specific focus on the two research ‘spirals’ of cognitive sciences related to the representation and processes of the human mind. For each spiral, we first review classic papers in the psychological sciences that have drawn on graph-theoretic ideas or frameworks before the advent of modern network science approaches. We then discuss how current research in these areas has been shaped by modern network science, which provides the mathematical framework and methodological tools for psychologists to (i) represent cognitive network structure and (ii) investigate and model the psychological processes that occur in these cognitive networks. Finally, we briefly comment on the future of, and the challenges facing, cognitive network science.

Investigating instances where lexical selection fails can lead to deeper insights into the cognitive machinery and architecture supporting successful word retrieval and speech production. In this paper, we used a multiplex lexical network approach that combines semantic and phonological similarities among words to model the structure of the mental lexicon. Network measures at different levels of analysis (degree, network distance, and closeness centrality) were used to investigate the influence of network structure on picture naming accuracy and errors by people with Anomic, Broca's, Conduction, and Wernicke's aphasia. Our results reveal that word retrieval is influenced by the multiplex lexical network structure in at least two ways—(a) the accuracy of production and error type on incorrect productions were influenced by the degree and closeness centrality of the target word, and (b) error type also varied in terms of network distance between the target word and produced error word. Taken together, the analyses demonstrate that network science techniques, particularly the use of the multiplex lexical network to simultaneously represent semantic and phonological relationships among words, reveal how the structure of the mental lexicon influences language processes beyond traditionally examined psycholinguistic variables. We propose a framework for how the multiplex lexical network approach allows for understanding the influence of mental lexicon structure on word retrieval processes, with an eye toward a better understanding of the nature of clinical impairments, like aphasia.