Some challenges in using multilayer networks to bridge brain and mind.

The application of techniques from network science to create single-layer networks of the brain and mind has resulted in significant advances in the neuro- (i.e., structural and functional brain networks) and cognitive sciences (i.e., cognitive network science). Recent advances in network science on multilayer networks increase the possibility that a "network of networks" might finally connect the physical brain to the intangible mind, much like physical fibre optic cables and wires connect to other layers of the internet to allow intangible social networks to form in various social media platforms. Several advances in structural brain networks, functional brain networks, cognitive networks, and multilayer networks are briefly reviewed. The possibility that these single-layer networks can be connected in a multilayer network to connect the brain to the mind is discussed, as well as some of the challenges that face such an ambitious endeavour. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The Effect of the COVID Pandemic on Clinical Psychology Research: A Bibliometric Analysis.

The present bibliometric analysis used traditional measures and network science techniques to examine how the COVID-19 pandemic influenced research in Clinical Psychology. Publication records from the Web of Science (WoS) were obtained for journal articles published prior to (2015 and 2018), during (2020), and at the end of the pandemic (2022) for the search terms "men and mental health" and "women and mental health". Network analyses of author-provided keywords showed that COVID-19 co-occurred with fear, anxiety, depression, and stress for both men and women in 2020. In 2022, COVID-19 co-occurred with topics related to world-wide lockdowns (e.g., alcohol use, substance use, intimate partner violence, loneliness, physical activity), and to more fundamental topics in Clinical Psychology (e.g., eating disorders and post-traumatic stress disorder). Although the COVID pandemic was associated with several changes in the research topics that were examined in Clinical Psychology, pre-existing disparities in the amount of mental health research on men compared to women did not appear to increase (in contrast to increases associated with COVID in pre-existing gender disparities observed in other areas of society).

Using network science to examine audio-visual speech perception with a multi-layer graph.

To examine visual speech perception (i.e., lip-reading), we created a multi-layer network (the AV-net) that contained: (1) an auditory layer with nodes representing phonological word-forms and edges connecting words that were phonologically related, and (2) a visual layer with nodes representing the viseme representations of words and edges connecting viseme representations that differed by a single viseme (and additional edges to connect related nodes in the two layers). The results of several computer simulations (in which activation diffused across the network to simulate word identification) are reported and compared to the performance of human participants who identified the same words in a condition in which audio and visual information were both presented (Simulation 1), in an audio-only presentation condition (Simulation 2), and a visual-only presentation condition (Simulation 3). Another simulation (Simulation 4) examined the influence of phonological information on visual speech perception by comparing performance in the multi-layer AV-net to a single-layer network that contained only a visual layer with nodes representing the viseme representations of words and edges connecting viseme representations that differed by a single viseme. We also report the results of several analyses of the errors made by human participants in the visual-only presentation condition. The results of our analyses have implications for future research and training of lip-reading, and for the development of automatic lip-reading devices and software for individuals with certain developmental or acquired disorders or for listeners with normal hearing in noisy conditions.

Cognitive modelling of concepts in the mental lexicon with multilayer networks: Insights, advancements, and future challenges.

The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Over decades psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and other types of conceptual associations be mapped within a coherent mathematical framework to study how the mental lexicon works? Here we review cognitive multilayer networks as a promising quantitative and interpretative framework for investigating the mental lexicon. Cognitive multilayer networks can map multiple types of information at once, thus capturing how different layers of associations might co-exist within the mental lexicon and influence cognitive processing. This review starts with a gentle introduction to the structure and formalism of multilayer networks. We then discuss quantitative mechanisms of psychological phenomena that could not be observed in single-layer networks and were only unveiled by combining multiple layers of the lexicon: (i) multiplex viability highlights language kernels and facilitative effects of knowledge processing in healthy and clinical populations; (ii) multilayer community detection enables contextual meaning reconstruction depending on psycholinguistic features; (iii) layer analysis can mediate latent interactions of mediation, suppression, and facilitation for lexical access. By outlining novel quantitative perspectives where multilayer networks can shed light on cognitive knowledge representations, including in next-generation brain/mind models, we discuss key limitations and promising directions for cutting-edge future research.

Using Complex Networks in the Hearing Sciences.

In this Point of View, we review a number of recent discoveries from the emerging, interdisciplinary field of Network Science , which uses graph theoretic techniques to understand complex systems. In the network science approach, nodes represent entities in a system, and connections are placed between nodes that are related to each other to form a web-like network . We discuss several studies that demonstrate how the micro-, meso-, and macro-level structure of a network of phonological word-forms influence spoken word recognition in listeners with normal hearing and in listeners with hearing loss. Given the discoveries made possible by this new approach and the influence of several complex network measures on spoken word recognition performance we argue that speech recognition measures-originally developed in the late 1940s and routinely used in clinical audiometry-should be revised to reflect our current understanding of spoken word recognition. We also discuss other ways in which the tools of network science can be used in Speech and Hearing Sciences and Audiology more broadly.

Speech, Language, and Hearing in the 21st Century: A Bibliometric Review of JSLHR From 2001 to 2021.

PURPOSE: A bibliometric analysis was performed for articles published in the Journal of Speech, Language, and Hearing Research (JSLHR) from 2001 to 2021 to examine changes to and emerging trends in the speech, language, and hearing sciences in the 21st century. METHOD: Quantitative analyses using JASP were performed on the publication rate and number of authors in articles published in JSLHR for each year from 2001 to 2021. VOSviewer was used to analyze and visualize networks of co-occurring Keyword Plus terms extracted from the articles published in JSLHR for several representative years from 2001 to 2021. RESULTS: Although the number of publications and number of authors published in JSLHR increased from 2001 to 2021, that growth was consistent with the growth found in science in general and with changes in publication policies and practices. The number and range of countries other than the United States published in JSLHR increased from 2001 to 2021. A consistent set of institutions published frequently in JSLHR across the years examined. The analysis of Keyword Plus terms showed an increase in the range of populations, disorders, and languages that were the subject of research from 2001 to 2021, as well as influences from other fields on speech, language, and hearing sciences. CONCLUSION: The science of science approach and the tools of network science are useful for assessing how changes in editorial policy affect diversity, for monitoring research topics that are growing (or declining), for identifying institutions that lead the field, and for inviting discussions among various interested parties related to the growth and development of a discipline.

Identifying the phonological backbone in the mental lexicon.

Previous studies used techniques from network science to identify individual nodes and a set of nodes that were "important" in a network of phonological word-forms from English. In the present study we used a network simplification process-known as the backbone-that removed redundant edges to extract a subnetwork of "important" words from the network of phonological word-forms. The backbone procedure removed 68.5% of the edges in the original network to extract a backbone with a giant component containing 6,211 words. We compared psycholinguistic and network measures of the words in the backbone to the words that did not survive the backbone extraction procedure. Words in the backbone occurred more frequently in the language, were shorter in length, were similar to more phonological neighbors, and were closer to other words than words that did not survive the backbone extraction procedure. Words in the backbone of the phonological network might form a "kernel lexicon"-a small but essential set of words that allows one to communicate in a wide-range of situations-and may provide guidance to clinicians and researchers on which words to focus on to facilitate typical development, or to accelerate rehabilitation efforts. The backbone extraction method may also prove useful in other applications of network science to the speech, language, hearing and cognitive sciences.

The Resilience of the Phonological Network May Have Implications for Developmental and Acquired Disorders.

A central tenet of network science states that the structure of the network influences processing. In this study of a phonological network of English words we asked: how does damage alter the network structure (Study 1)? How does the damaged structure influence lexical processing (Study 2)? How does the structure of the intact network "protect" processing with a less efficient algorithm (Study 3)? In Study 1, connections in the network were randomly removed to increasingly damage the network. Various measures showed the network remained well-connected (i.e., it is resilient to damage) until ~90% of the connections were removed. In Study 2, computer simulations examined the retrieval of a set of words. The performance of the model was positively correlated with naming accuracy by people with aphasia (PWA) on the Philadelphia Naming Test (PNT) across four types of aphasia. In Study 3, we demonstrated another way to model developmental or acquired disorders by manipulating how efficiently activation spread through the network. We found that the structure of the network "protects" word retrieval despite decreases in processing efficiency; words that are relatively easy to retrieve with efficient transmission of priming remain relatively easy to retrieve with less efficient transmission of priming. Cognitive network science and computer simulations may provide insight to a wide range of speech, language, hearing, and cognitive disorders.

Feature-rich multiplex lexical networks reveal mental strategies of early language learning.

Knowledge in the human mind exhibits a dualistic vector/network nature. Modelling words as vectors is key to natural language processing, whereas networks of word associations can map the nature of semantic memory. We reconcile these paradigms-fragmented across linguistics, psychology and computer science-by introducing FEature-Rich MUltiplex LEXical (FERMULEX) networks. This novel framework merges structural similarities in networks and vector features of words, which can be combined or explored independently. Similarities model heterogenous word associations across semantic/syntactic/phonological aspects of knowledge. Words are enriched with multi-dimensional feature embeddings including frequency, age of acquisition, length and polysemy. These aspects enable unprecedented explorations of cognitive knowledge. Through CHILDES data, we use FERMULEX networks to model normative language acquisition by 1000 toddlers between 18 and 30 months. Similarities and embeddings capture word homophily via conformity, which measures assortative mixing via distance and features. Conformity unearths a language kernel of frequent/polysemous/short nouns and verbs key for basic sentence production, supporting recent evidence of children's syntactic constructs emerging at 30 months. This kernel is invisible to network core-detection and feature-only clustering: It emerges from the dual vector/network nature of words. Our quantitative analysis reveals two key strategies in early word learning. Modelling word acquisition as random walks on FERMULEX topology, we highlight non-uniform filling of communicative developmental inventories (CDIs). Biased random walkers lead to accurate (75%), precise (55%) and partially well-recalled (34%) predictions of early word learning in CDIs, providing quantitative support to previous empirical findings and developmental theories.

Using Network Science and Psycholinguistic Megastudies to Examine the Dimensions of Phonological Similarity.

Network science was used to examine different dimensions of phonological similarity in English. Data from a phonological associate task and an identification of words in noise task were used to create a phonological association network and a misperception network. These networks were compared to a network formed by a computational metric widely used to assess phonological similarity (i.e., one-phoneme metric). The phonological association network and the misperception network were topographically more similar to each other than either were to the one-phoneme metric network, but there were several network features in common between the one-phoneme metric network and the phonological association network. To assess the influence of network structure on processing, we compared the influence of degree (i.e., neighborhood density) from each of the networks on visual and auditory lexical decision reaction times obtained from two psycholinguistic megastudies. The effect of degree differed across network types and tasks. We discuss the use of each approach to determine phonological similarity and a possible direction forward for language research through the use of multiplex networks.

Do age and language impairment affect speed of recognition for words with high and low closeness centrality within the phonological network?

Purpose: Speed and accuracy of lexical access change with healthy ageing and neurodegeneration. While a word's immediate phonological neighbourhood density (i.e. words differing by a single phoneme) influences access, connectivity to all words in the phonological network (i.e. closeness centrality) may influence processing. This study aimed to investigate the effect of closeness centrality on speed and accuracy of lexical processing pre- and post- a single word-training session in healthy younger and older adults, and adults with logopenic primary progressive aphasia (lvPPA), which affects phonological processing.Method: Participants included 29 young and 17 older healthy controls, and 10 adults with lvPPA. Participants received one session of word-training on words with high or low closeness centrality, using a picture-word verification task. Changes in lexical decision reaction times (RT) and accuracy were measured.Result: Baseline RT was unaffected by age and accuracy was at ceiling for controls. Post-training, only young adults' RT were significantly faster. Adults with lvPPA were slower and less accurate than controls at baseline, with no training effect. Closeness centrality did not influence performance.Conclusion: Absence of training effect for older adults suggests higher threshold to induce priming, possibly associated with insufficient dosage or fatigue. Implications for word-finding interventions with older adults are discussed.

A web-based interface to calculate phonological neighborhood density for words and nonwords in Modern Standard Arabic.

The availability of online databases (e.g., Balota et al., 2007) and calculators (e.g., Storkel & Hoover, 2010) has contributed to an increase in psycholinguistic-related research, to the development of evidence-based treatments in clinical settings, and to scientifically supported training programs in the language classroom. The benefit of online language resources is limited by the fact that the majority of such resources provide information only for the English language (Vitevitch, Chan & Goldstein, 2014). To address the lack of diversity in these resources for languages that differ phonologically and morphologically from English, the present article describes an online database to compute phonological neighborhood density (i.e., the number of words that sound similar to a given word) for words and nonwords in Modern Standard Arabic (MSA). A full description of how the calculator can be used is provided. It can be freely accessed at https://calculator.ku.edu/density/about .

The influence of memory on the speech-to-song illusion.

In the speech-to-song illusion a spoken phrase is presented repeatedly and begins to sound as if it is being sung. Anecdotal reports suggest that subsequent presentations of a previously heard phrase enhance the illusion, even if several hours or days have elapsed between presentations. In Experiment 1, we examined in a controlled laboratory setting whether memory traces for a previously heard phrase would influence song-like ratings to a subsequent presentation of that phrase. The results showed that word lists that were played several times throughout the experimental session were rated as being more song-like at the end of the experiment than word lists that were played only once in the experimental session. In Experiment 2, we examined if the memory traces that influenced the speech-to-song illusion were abstract in nature or exemplar-based by playing some word lists several times during the experiment in the same voice and playing other word lists several times during the experiment but in different voices. The results showed that word lists played in the same voice were rated as more song-like at the end of the experiment than word lists played in different voices. Many previous studies have examined how various aspects of the stimulus itself influences the perception of the speech-to-song illusion. The results of the present experiments demonstrate that memory traces of the stimulus also influence the speech-to-song illusion.

What Can Network Science Tell Us About Phonology and Language Processing?

Contemporary psycholinguistic models place significant emphasis on the cognitive processes involved in the acquisition, recognition, and production of language but neglect many issues related to the representation of language-related information in the mental lexicon. In contrast, a central tenet of network science is that the structure of a network influences the processes that operate in that system, making process and representation inextricably connected. Here, we consider how the structure found across phonological networks of several languages from different language families may influence language processing as we age and experience diseases that affect cognition during the typical and atypical acquisition of new words, during typical perception and production of speech in adults, and during language change over time. We conclude that the network science approach may not only provide insights into specific language processes but also provide a way to connect the work from these domains, which are becoming increasingly balkanized.

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

Cognitive network science is an emerging approach that uses the mathematical tools of network science to map the relationships among representations stored in memory to examine how that structure might influence processing. In the present study, we used computer simulations to compare the ability of a well-known model of spoken word recognition, TRACE, to the ability of a cognitive network model with a spreading activation-like process to account for the findings from several previously published behavioral studies of language processing. In all four simulations, the TRACE model failed to retrieve a sufficient number of words to assess if it could replicate the behavioral findings. The cognitive network model successfully replicated the behavioral findings in Simulations 1 and 2. However, in Simulation 3a, the cognitive network did not replicate the behavioral findings, perhaps because an additional mechanism was not implemented in the model. However, in Simulation 3b, when the decay parameter in spreadr was manipulated to model this mechanism the cognitive network model successfully replicated the behavioral findings. The results suggest that models of cognition need to take into account the multi-scale structure that exists among representations in memory, and how that structure can influence processing.

Unveiling the nature of interaction between semantics and phonology in lexical access based on multilayer networks.

An essential aspect of human communication is the ability to access and retrieve information from ones' 'mental lexicon'. This lexical access activates phonological and semantic components of concepts, yet the question whether and how these two components relate to each other remains widely debated. We harness tools from network science to construct a large-scale linguistic multilayer network comprising of phonological and semantic layers. We find that the links in the two layers are highly similar to each other and that adding information from one layer to the other increases efficiency by decreasing the network overall distances, but specifically affecting shorter distances. Finally, we show how a multilayer architecture demonstrates the highest efficiency, and how this efficiency relates to weak semantic relations between cue words in the network. Thus, investigating the interaction between the layers and the unique benefit of a linguistic multilayer architecture allows us to quantify theoretical cognitive models of lexical access.

Does age affect perception of the Speech-to-Song Illusion?

The Speech-to-Song Illusion is an auditory illusion that occurs when a spoken phrase is repeatedly presented. After several presentations, listeners report that the phrase seems to be sung rather than spoken. Previous work [1] indicates that the mechanisms-priming, activation, and satiation-found in the language processing model, Node Structure Theory (NST), may account for the Speech-to-Song Illusion. NST also accounts for other language-related phenomena, including increased experiences in older adults of the tip-of-the-tongue state (where you know a word, but can't retrieve it). Based on the mechanism in NST used to account for the age-related increase in the tip-of-the-tongue phenomenon, we predicted that older adults may be less likely to experience the Speech-to-Song Illusion than younger adults. Adults of a wide range of ages heard a stimulus known to evoke the Speech-to-Song Illusion. Then, they were asked to indicate if they experienced the illusion or not (Study 1), to respond using a 5-point song-likeness rating scale (Study 2), or to indicate when the percept changed from speech to song (Study 3). The results of these studies suggest that the illusion is experienced with similar frequency and strength, and after the same number of repetitions by adult listeners regardless of age.

Phonological but not semantic influences on the speech-to-song illusion.

In the speech to song illusion, a spoken phrase begins to sound as if it is being sung after several repetitions. Castro et al. (2018) used Node Structure Theory (NST; MacKay, 1987), a model of speech perception and production, to explain how the illusion occurs. Two experiments further test the mechanisms found in NST-priming, activation, and satiation-as an account of the speech to song illusion. In Experiment 1, words varying in the phonological clustering coefficient influenced how quickly a lexical node could recover from satiation, thereby influencing the song-like ratings to lists of words that were high versus low in phonological clustering coefficient. In Experiment 2, we used equivalence testing (i.e., the TOST procedure) to demonstrate that once lexical nodes are satiated the higher level semantic information associated with the word cannot differentially influence song-like ratings to lists of words varying in emotional arousal. The results of these two experiments further support the NST account of the speech to song illusion.

Investigating the Influence of Inverse Preferential Attachment on Network Development.

Recent work investigating the development of the phonological lexicon, where edges between words represent phonological similarity, have suggested that phonological network growth may be partly driven by a process that favors the acquisition of new words that are phonologically similar to several existing words in the lexicon. To explore this growth mechanism, we conducted a simulation study to examine the properties of networks grown by inverse preferential attachment, where new nodes added to the network tend to connect to existing nodes with fewer edges. Specifically, we analyzed the network structure and degree distributions of artificial networks generated via either preferential attachment, an inverse variant of preferential attachment, or combinations of both network growth mechanisms. The simulations showed that network growth initially driven by preferential attachment followed by inverse preferential attachment led to densely-connected network structures (i.e., smaller diameters and average shortest path lengths), as well as degree distributions that could be characterized by non-power law distributions, analogous to the features of real-world phonological networks. These results provide converging evidence that inverse preferential attachment may play a role in the development of the phonological lexicon and reflect processing costs associated with a mature lexicon structure.

An investigation of network growth principles in the phonological language network.

This article investigated how network growth algorithms-preferential attachment, preferential acquisition, and lure of the associates-relate to the acquisition of words in the phonological language network, where edges are placed between words that are phonologically similar to each other. Through an archival analysis of age-of-acquisition norms from English and Dutch and word learning experiments, we examined how new words were added to the phonological network. Across both approaches, we found converging evidence that an inverse variant of preferential attachment-where new nodes were instead more likely to attach to existing nodes with few connections-influenced the growth of the phonological network. We suggest that the inverse preferential attachment principle reflects the constraints of adding new phonological representations to an existing language network with already many phonologically similar representations, possibly reflecting the pressures associated with the processing costs of retrieving lexical representations that have many phonologically similar competitors. These results contribute toward our understanding of how the phonological language network grows over time and could have implications for the learning outcomes of individuals with language disorders. (PsycInfo Database Record (c) 2020 APA, all rights reserved).