Community structure in the phonological network

Are different countries equally green with envy? A comparison of the everyday concept of envy in the United States, Spain, and Germany

Using a prototype approach to emotion concepts, we mapped the internal structure and content of the everyday concept of envy (as used in the United States) and its translation equivalents of envidia in Spanish and Neid in German. In Study 1 (total N = 415), the features of the concept of envy, envidia, and Neid were generated via an open-ended questionnaire. In Study 2 (total N = 404), participants rated the degree of typicality of the constitutive features on a forced-choice questionnaire. The prototype analysis of envy, supplemented with network analyses, revealed that the largest connected set of features of envy, envidia, and Neid shared a group of central features, including features related to success or to people with a better appearance. Still, envy, envidia, and Neid did differ with respect to their constituent peripheral features as well as the density of their networks, their structure, and the betweenness centrality of the nodes. These results suggest that a prototype approach combined with network analysis is a convenient approach for studying the internal structure of everyday emotion concepts and the degree of overlap with respect to the translation equivalents in different countries.

Obsolescence effects in second language phonological networks

Phonological networks are representations of word forms and their phonological relationships with other words in a given language lexicon. A principle underlying the growth (or evolution) of those networks is preferential attachment, or the "rich-gets-richer" mechanisms, according to which words with many phonological neighbors (or links) are the main beneficiaries of future growth opportunities. Due to their limited number of words, language lexica constitute node-constrained networks where growth cannot keep increasing in a linear way; hence, preferential attachment is likely mitigated by certain factors. The present study investigated obsolescence effects (i.e., a word's finite timespan of being active in terms of growth) in an evolving phonological network of English as a second language. It was found that phonological neighborhoods are constructed by one large initial lexical spurt, followed by sublinear growth spurts that eventually lead to very limited growth in later lexical spurts during network evolution. First-language-given neighborhood densities are rarely reached even by the most advanced language learners. An analysis of the strength of phonological relationships between phonological word forms revealed a tendency to incorporate phonetically more distant phonological neighbors at earlier acquisition stages. Overall, the findings suggest an obsolescence effect in growth that favors younger words. Implications for the second-language lexicon include leveraged learning mechanisms and learning bouts focused on a smaller range of phonological segments, and involve questions concerning lexical processing in aging networks.

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.

Multiple sclerosis is associated with differences in semantic memory structure

OBJECTIVE

Although language is often considered to be largely intact in multiple sclerosis (MS), word-finding difficulties are a common complaint. Recent work suggests that declines in language are not solely the result of motoric and cognitive slowing that is most strongly associated with MS. Network science approaches have been effectively used to examine network structure as it relates to clinical conditions, aging, and language. The present study utilizes a network science approach to investigate whether individuals with MS exhibit less interconnected and resilient semantic networks compared to age-matched neurotypical peers.

METHOD

We used semantic fluency data from 89 participants with MS and 88 neurotypical participants to estimate and analyze the semantic network structure for each participant group. Additionally, we conducted a percolation analysis to examine the resilience of each network.

RESULTS

Network measures showed that individuals with MS had lower local and global clustering coefficients, longer average shortest path lengths, and higher modularity values compared to neurotypical peers. Small-worldness, network portrait divergence measures, and community detection analyses were consistent with these results and indicated that macroscopic properties of the two networks differed and that the semantic network for individuals with MS was more fractured than the neurotypical peer network. Moreover, a spreading activation simulation and percolation analysis suggested that the semantic networks of individuals with MS are less flexible and activation degrades faster than those of age-matched neurotypical participants.

CONCLUSIONS

These differing semantic network structures suggest that language retrieval difficulties in MS partially result from decline in language-specific factors. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Aesthetic emotions are affected by context: a psychometric network analysis

Aesthetic emotions are defined as emotions arising when a person evaluates a stimulus for its aesthetic appeal. Whether these emotions are unique to aesthetic activities is debated. We address this debate by examining if recollections of different types of engaging activities entail different emotional profiles. A large sample of participants were asked to recall engaging aesthetic (N = 167), non-aesthetic (N = 160), or consumer (N = 172) activities. They rated the extent to which 75 candidate aesthetic emotions were evoked by these activities. We applied a computational psychometric network approach to represent and compare the space of these emotions across the three conditions. At the behavioral level, recalled aesthetic activities were rated as the least vivid but most intense compared to the two other conditions. At the network level, we found several quantitative differences across the three conditions, related to the typology, community (clusters) and core nodes (emotions) of these networks. Our results suggest that aesthetic and non-aesthetic activities evoke emotional spaces differently. Thus, we propose that aesthetic emotions are distributed differently in a multidimensional aesthetic space than for other engaging activities. Our results highlight the context-specificity of aesthetic emotions.

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.

Representing melodic relationships using network science

Music is a complex system consisting of many dimensions and hierarchically organized information-the organization of which, to date, we do not fully understand. Network science provides a powerful approach to representing such complex systems, from the social networks of people to modelling the underlying network structures of different cognitive mechanisms. In the present research, we explored whether network science methodology can be extended to model the melodic patterns underlying expert improvised music. Using a large corpus of transcribed improvisations, we constructed a network model in which 5-pitch sequences were linked depending on consecutive occurrences, constituting 116,403 nodes (sequences) and 157,429 edges connecting them. We then investigated whether mathematical graph modelling relates to musical characteristics in real-world listening situations via a behavioral experiment paralleling those used to examine language. We found that as melodic distance within the network increased, participants judged melodic sequences as less related. Moreover, the relationship between distance and reaction time (RT) judgements was quadratic: participants slowed in RT up to distance four, then accelerated; a parallel finding to research in language networks. This study offers insights into the hidden network structure of improvised tonal music and suggests that humans are sensitive to the property of melodic distance in this network. More generally, our work demonstrates the similarity between music and language as complex systems, and how network science methods can be used to quantify different aspects of its complexity.

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.

Semantic Memory and Creativity: The Costs and Benefits of Semantic Memory Structure in Generating Original Ideas

Despite its theoretical importance, little is known about how semantic memory structure facilitates and constrains creative idea production. We examine whether the semantic richness of a concept has both benefits and costs to creative idea production. Specifically, we tested whether cue set-size-an index of semantic richness reflecting the average number of elements associated with a given concept-impacts the quantity (fluency) and quality (originality) of responses generated during the alternate uses task (AUT). Across four studies, we show that low-association, sparse, AUT cues benefit originality at the cost of fluency compared to high-association, rich, AUT cues. Furthermore, we found an interaction with individual differences in fluid intelligence in the low-association AUT cues, suggesting that constraints of sparse semantic knowledge can be overcome with top-down intervention. The findings indicate that semantic richness differentially impacts the quality and quantity of generated ideas, and that cognitive control processes can facilitate idea production when conceptual knowledge is limited.

Database of word-level statistics for Mandarin Chinese (DoWLS-MAN)

In this article we present the Database of Word-Level Statistics for Mandarin Chinese (DoWLS-MAN). The database addresses the lack of agreement in phonological syllable segmentation specific to Mandarin by offering phonological features for each lexical item according to 16 schematic representations of the syllable (8 with tone and 8 without tone). Those lexical statistics that differ per phonological word and nonword due to changes in syllable segmentation are of the variant category and include subtitle lexical frequency, phonological neighborhood density measures, homophone density, and network science measures. The invariant characteristics consist of each items' lexical tone, phonological transcription, and syllable structure among others. The goal of DoWLS-MAN is to provide researchers both the ability to choose stimuli that are derived from a segmentation schema that supports an existing model of Mandarin speech processing, and the ability to choose stimuli that allow for the testing of hypotheses on phonological segmentation according to multiple schemas. In an exploratory analysis we illustrate how multiple schematic representations of the phonological mental lexicon can aid in hypothesis generation, specifically in terms of phonological processing when reading Chinese orthography. Users of the database can search among over 92,000 words, over 1600 out-of-vocabulary Chinese characters, and 4300 phonological nonwords according to either Chinese orthography, pinyin, or ASCII phonetic script. Users can also generate a list of phonological words and nonwords according to user-defined ranges and categories of lexical characteristics. DoWLS-MAN is available to the public for search or download at https://dowls.site .

Exploring Networks of Lexical Variation in Russian Sign Language

When describing variation at the lexical level in sign languages, researchers often distinguish between phonological and lexical variants, using the following principle: if two signs differ in only one of the major phonological components (handshape, orientation, movement, location), then they are considered phonological variants, otherwise they are considered separate lexemes. We demonstrate that this principle leads to contradictions in some simple and more complex cases of variation. We argue that it is useful to visualize the relations between variants as graphs, and we describe possible networks of variants that can arise using this visualization tool. We further demonstrate that these scenarios in fact arise in the case of variation in color terms and kinship terms in Russian Sign Language (RSL), using a newly created database of lexical variation in RSL. We show that it is possible to develop a set of formal rules that can help distinguish phonological and lexical variation also in the problematic scenarios. However, we argue that it might be a mistake to dismiss the actual patterns of variant relations in order to arrive at the binary lexical vs. phonological variant opposition.

The Value of Statistical Learning to Cognitive Network Science

To study the human mind is to consider the nature of associations-how are they learned, what are their constituent parts, and how can they be severed or adjusted? The manipulation of associations stands as a pillar of statistical learning (SL) research, which strongly suggests that processes as diverse as word segmentation, learning of grammatical patterns, and event perception can be explained by the learner's sensitivity to simple temporal dependencies (among other regularities). Used to determine the edges of a network, associations are similarly crucial to consider when quantifying the graph-theoretical properties of various cognitive systems. With this point of convergence in mind, the present work reaffirms the unique value of network science in illuminating the broad-level architectures of complex cognitive systems. However, I also describe how insights from the SL literature, coupled with insights from psycholinguistics more broadly, offer a strong theoretical backbone upon which we can develop and study networks that reflect, as closely as possible, the psychological realities of learning.

Exploring How Phonotactic Knowledge Can Be Represented in Cognitive Networks

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.

Multilayer networks: An untapped tool for understanding bilingual neurocognition

Cross-linguistic similarity is a term so broad and multi-faceted that it is not easily defined. The degree of overlap between languages is known to affect lexical competition during online processing and production, and its relevance for second language acquisition has also been established. Nevertheless, determining what makes two languages similar (or not) increases in complexity when multiple levels of the language hierarchy (e.g., phonology, syntax) are considered. How can we feasibly account for the patterns of convergence and divergence at each level of representation, as well as the interactions between them? The growing field of network science brings new methodologies to bear on this longstanding question. Below, we summarize current network science approaches to modeling language structure and discuss implications for understanding various linguistic processes. Critically, we stress the particular value of multilayer techniques, unique and powerful in their ability to simultaneously accommodate an array of node-to-node (or word-to-word) relationships.

The Growth of Children's Semantic and Phonological Networks: Insight From 10 Languages

Children tend to produce words earlier when they are connected to a variety of other words along the phonological and semantic dimensions. Though these semantic and phonological connectivity effects have been extensively documented, little is known about their underlying developmental mechanism. One possibility is that learning is driven by lexical network growth where highly connected words in the child's early lexicon enable learning of similar words. Another possibility is that learning is driven by highly connected words in the external learning environment, instead of highly connected words in the early internal lexicon. The present study tests both scenarios systematically in both the phonological and semantic domains across 10 languages. We show that phonological and semantic connectivity in the learning environment drives growth in both production- and comprehension-based vocabularies, even controlling for word frequency and length. This pattern of findings suggests a word learning process where children harness their statistical learning abilities to detect and learn highly connected words in the learning environment.

Quantifying flexibility in thought: The resiliency of semantic networks differs across the lifespan

Older adults tend to have a broader vocabulary compared to younger adults - indicating a richer storage of semantic knowledge - but their retrieval abilities decline with age. Recent advances in quantitative methods based on network science have investigated the effect of aging on semantic memory structure. However, it is yet to be determined how this aging effect on semantic memory structure relates to its overall flexibility. Percolation analysis provides a quantitative measure of the flexibility of a semantic network, by examining how a semantic memory network is resistant to "attacks" or breaking apart. In this study, we incorporated percolation analyses to examine how semantic networks of younger and older adults break apart to investigate potential age-related differences in language production. We applied the percolation analysis to 3 independent sets of data (total N = 78 younger, 78 older adults) from which we generated semantic networks based on verbal fluency performance. Across all 3 datasets, the percolation integrals of the younger adults were larger than older adults, indicating that older adults' semantic networks were less flexible and broke down faster than the younger adults'. Our findings provide quantitative evidence for diminished flexibility in older adults' semantic networks, despite the stability of semantic knowledge across the lifespan. This may be one contributing factor to age-related differences in language production.

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.

The Challenge of Achieving Greater Generalization in Phonological Treatment of Aphasia

BACKGROUND

Stimulus selection is important to anomia treatment because similarity between trained and untrained words in the mental lexicon may influence treatment generalization. We focused on phonological similarity between trained and untrained words from a clinical trial of Phonomotor Treatment (PMT) that showed gains in confrontation naming accuracy of untrained words post-treatment. One way to capture the amount of similarity between the trained and untrained words is to consider the phonological network path distance between words. We posited that the distance between trained and untrained words in a phonological network could account for the improvement in confrontation naming accuracy post-treatment.

AIM

To define the phonological network distance between trained and untrained words that influences change in confrontation naming accuracy post-treatment.

METHODS AND PROCEDURES

We retrospectively analyzed data from 28 people with aphasia who received PMT as part of a clinical trial. Participants completed confrontation naming (baseline, post-treatment, and 3-months post-treatment) of words varying in phonological distance to the treatment stimuli. We used a phonological network to calculate the average shortest path length (ASPL), defined by number of phoneme differences, between an untrained word and all trained words. We used mixed effects regression models to predict change in confrontation naming accuracy of untrained words post-treatment from ASPL. Several post-hoc analyses were also conducted.

OUTCOMES AND RESULTS

We found no effect of ASPL on change in confrontation naming accuracy of untrained words immediately post- and 3-months post-treatment. However, post-hoc analyses indicated significant subject heterogeneity and limitations in observable path distance between trained and untrained words.

CONCLUSION

Despite the clinical trial report that confrontation naming of untrained words improved after PMT, we found no overall effect of ASPL on the amount of improvement. We discuss further investigation of the entire domain of phonological sequence knowledge (the phonological sequence knowledge landscape) and its influence on treatment generalization, and the potential importance of identifying predictors of treatment response to enhance the effects of treatment generalization.

Using Network Science to Map What Montréal Bilinguals Talk about Across Languages and Communicative Contexts

Recent work within the language sciences, particularly bilingualism, has sought new methods to evaluate and characterize how people differentially use language across different communicative contexts. These differences have thus far been linked to changes in cognitive control strategy, reading behavior, and brain organization. Here, we approach this issue using a novel application of Network Science to map the conversational topics that Montréal bilinguals discuss across communicative contexts (e.g., work, home, family, school, social), in their dominant vs. non-dominant language. Our results demonstrate that all communicative contexts display a unique pattern in which conversational topics are discussed, but only a few communicative contexts (work and social) display a unique pattern of how many languages are used to discuss particular topics. We also demonstrate that the dominant language has greater network size, strength, and density than the non-dominant language, suggesting that more topics are used in a wider variety of contexts in this language. Lastly, using community detection to thematically group the topics in each language, we find evidence of greater specificity in the non-dominant language than the dominant language. We contend that Network Science is a valuable tool for representing complex information, such as individual differences in bilingual language use, in a rich and granular manner, that may be used to better understand brain and behavior.

Phonological network fluency identifies phonological restructuring through mental search

We investigated network principles underlying mental search through a novel phonological verbal fluency task. Post exclusion, 95 native-language Mandarin speakers produced as many items that differed by a single lexical tone as possible within one minute. Their verbal productions were assessed according to several novel graded fluency measures, and network science measures that accounted for the structure, cohesion and interconnectedness of lexical items. A multivariate regression analysis of our participants' language backgrounds included their mono- or multi-lingual status, English proficiency, and fluency in other Chinese languages/dialects. Higher English proficiency predicted lower error rates and greater interconnectedness, while higher fluency in other Chinese languages/dialects revealed lower successive similarity and lower network coherence. This inverse relationship between English and other Chinese languages/dialects provides evidence of the restructuring of the phonological mental lexicon.

New Perspectives on the Aging Lexicon

The field of cognitive aging has seen considerable advances in describing the linguistic and semantic changes that happen during the adult life span to uncover the structure of the mental lexicon (i.e., the mental repository of lexical and conceptual representations). Nevertheless, there is still debate concerning the sources of these changes, including the role of environmental exposure and several cognitive mechanisms associated with learning, representation, and retrieval of information. We review the current status of research in this field and outline a framework that promises to assess the contribution of both ecological and psychological aspects to the aging lexicon.

Orthographic Networks in the Developing Mental Lexicon. Insights From Graph Theory and Implications for the Study of Language Processing

In this study, we examine the development of orthographic networks in the mental lexicon using graph theory. According to this view, words are represented by nodes in a network and connected as a function of their orthographic similarity. With a sampling approach based on a language corpus for German school children, we were able to simulate lexical development for children from Grade 1–8. By sampling different lexicon sizes from the corpus, we were able to analyze the content of the orthographic lexicon at different time points and examined network characteristics using graph theory. Results show that, similar to semantic and phonological networks, orthographic networks possess small-word characteristics defined by short average path lengths between nodes and strong local clustering. Moreover, the interconnectivity of the network decreases with growth. Implications for the study of the effect of network measures on language processing are discussed.

Knowledge gaps in the early growth of semantic feature networks

Understanding language learning, and more general knowledge acquisition, requires characterization of inherently qualitative structures. Recent work has applied network science to this task by creating semantic feature networks, in which words correspond to nodes and connections to shared features, then characterizing the structure of strongly inter-related groups of words. However, the importance of sparse portions of the semantic network - knowledge gaps - remains unexplored. Using applied topology we query the prevalence of knowledge gaps, which we propose manifest as cavities within the growing semantic feature network of toddlers. We detect topological cavities of multiple dimensions and find that despite word order variation, global organization remains similar. We also show that nodal network measures correlate with filling cavities better than basic lexical properties. Finally, we discuss the importance of semantic feature network topology in language learning and speculate that the progression through knowledge gaps may be a robust feature of knowledge acquisition.

The orthographic similarity structure of English words: Insights from network science

Network science has been applied to study the structure of the mental lexicon, the part of long-term memory where all the words a person knows are stored. Here the tools of network science are used to study the organization of orthographic word-forms in the mental lexicon and how that might influence visual word recognition. An orthographic similarity network of the English language was constructed such that each node represented an English word, and undirected, unweighted edges were placed between words that differed by an edit distance of 1, a commonly used operationalization of orthographic similarity in psycholinguistics. The largest connected component of the orthographic language network had a small-world structure and a long-tailed degree distribution. Additional analyses were conducted using behavioral data obtained from a psycholinguistic database to determine if network science measures obtained from the orthographic language network could be used to predict how quickly and accurately people process written words. The present findings show that the structure of the mental lexicon influences lexical access in visual word recognition.

Multiplex model of mental lexicon reveals explosive learning in humans

Word similarities affect language acquisition and use in a multi-relational way barely accounted for in the literature. We propose a multiplex network representation of this mental lexicon of word similarities as a natural framework for investigating large-scale cognitive patterns. Our representation accounts for semantic, taxonomic, and phonological interactions and it identifies a cluster of words which are used with greater frequency, are identified, memorised, and learned more easily, and have more meanings than expected at random. This cluster emerges around age 7 through an explosive transition not reproduced by null models. We relate this explosive emergence to polysemy - redundancy in word meanings. Results indicate that the word cluster acts as a core for the lexicon, increasing both lexical navigability and robustness to linguistic degradation. Our findings provide quantitative confirmation of existing conjectures about core structure in the mental lexicon and the importance of integrating multi-relational word-word interactions in psycholinguistic frameworks.

Using the OASES-A to illustrate how network analysis can be applied to understand the experience of stuttering

PURPOSE

Network science uses mathematical and computational techniques to examine how individual entities in a system, represented by nodes, interact, as represented by connections between nodes. This approach has been used by Cramer et al. (2010) to make "symptom networks" to examine various psychological disorders. In the present analysis we examined a network created from the items in the Overall Assessment of the Speaker's Experience of Stuttering-Adult (OASES-A), a commonly used measure for evaluating adverse impact in the lives of people who stutter.

METHOD

The items of the OASES-A were represented as nodes in the network. Connections between nodes were placed if responses to those two items in the OASES-A had a correlation coefficient greater than ±0.5. Several network analyses revealed which nodes were "important" in the network.

RESULTS

Several centrally located nodes and "key players" in the network were identified. A community detection analysis found groupings of nodes that differed slightly from the subheadings of the OASES-A.

CONCLUSIONS

Centrally located nodes and "key players" in the network may help clinicians prioritize treatment. The different community structure found for people who stutter suggests that the way people who stutter view stuttering may differ from the way that scientists and clinicians view stuttering. Finally, the present analyses illustrate how the network approach might be applied to other speech, language, and hearing disorders to better understand how those disorders are experienced and to provide insights for their treatment.

The Hyper-Modular Associative Mind: A Computational Analysis of Associative Responses of Persons with Asperger Syndrome

Rigidity of thought is considered a main characteristic of persons with Asperger syndrome (AS). This rigidity may explain the poor comprehension of unusual semantic relations, frequently exhibited by persons with AS. Research indicates that such deficiency is related to altered mental lexicon organization, but has never been directly examined. The present study used computational network science tools to compare the mental lexicon structure of persons with AS and matched controls. Persons with AS and matched controls generated free associations, and network tools were used to extract and compare the mental lexicon structure of the two groups. The analysis revealed that persons with AS exhibit a hyper-modular semantic organization: their mental lexicon is more compartmentalized compared to matched controls. We argue that this hyper-modularity may be related to the rigidity of thought which characterizes persons with AS and discuss the clinical and more general cognitive implications of our findings.

Local Patterns to Global Architectures: Influences of Network Topology on Human Learning

A core question in cognitive science concerns how humans acquire and represent knowledge about their environments. To this end, quantitative theories of learning processes have been formalized in an attempt to explain and predict changes in brain and behavior. We connect here statistical learning approaches in cognitive science, which are rooted in the sensitivity of learners to local distributional regularities, and network science approaches to characterizing global patterns and their emergent properties. We focus on innovative work that describes how learning is influenced by the topological properties underlying sensory input. The confluence of these theoretical approaches and this recent empirical evidence motivate the importance of scaling-up quantitative approaches to learning at both the behavioral and neural levels.

Using network science in the language sciences and clinic

A number of variables—word frequency, word length—have long been known to influence language processing. This study briefly reviews the effects in speech perception and production of two more recently examined variables: phonotactic probability and neighbourhood density. It then describes 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 neighbours) 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 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.

The influence of clustering coefficient on word-learning: how groups of similar sounding words facilitate acquisition

Clustering coefficient, C, measures the extent to which neighbors of a word are also neighbors of each other, and has been shown to influence speech production, speech perception, and several memory-related processes. In this study we examined how C influences word-learning. Participants were trained over three sessions at 1-week intervals, and tested with a picture-naming task on nonword-nonobject pairs. We found an advantage for novel words with high C (the neighbors of this novel word are likely to be neighbors with each other), but only after the 1-week retention period with no additional exposures to the stimuli. The results are consistent with the spreading-activation network-model of the lexicon proposed by Chan and Vitevitch (2009). The influence of C on various language-related processes suggests that characteristics of the individual word are not the only things that influence processing; rather, lexical processing may also be influenced by the relationships that exist among words in the lexicon.

Keywords in the mental lexicon

Network science draws from a number of fields to examine complex systems using nodes to represent individuals and connections to represent relationships between individuals to form a network. This approach has been used in several areas of Psychology to illustrate the influence that the structure of a network has on processing in that system. In the present study the concept of keyplayers in a network (Borgatti, 2006) was examined in the domain of Psycholinguistics. Keyplayers are nodes in a network that, when removed, result in the network fracturing into several smaller components. A set of such nodes was found in a network of phonological word-forms as was another set of foil words, comparable to the "keywords" on a number of lexical and network characteristics. In three conventional psycholinguistic tasks keywords were responded to more quickly and accurately than the foils. A similar trend was observed in an analysis of the keywords and foils (and another set of foils) in the English Lexicon Project. These results open avenues for further exploration of keywords in various areas of language processing, and demonstrate the utility of the network science approach to psycholinguistics and psychology more generally.

Insights into failed lexical retrieval from network science

Previous network analyses of the phonological lexicon (Vitevitch, 2008) observed a web-like structure that exhibited assortative mixing by degree: words with dense phonological neighborhoods tend to have as neighbors words that also have dense phonological neighborhoods, and words with sparse phonological neighborhoods tend to have as neighbors words that also have sparse phonological neighborhoods. Given the role that assortative mixing by degree plays in network resilience, we examined instances of real and simulated lexical retrieval failures in computer simulations, analysis of a slips-of-the-ear corpus, and three psycholinguistic experiments for evidence of this network characteristic in human behavior. The results of the various analyses support the hypothesis that the structure of words in the mental lexicon influences lexical processing. The implications of network science for current models of spoken word recognition, language processing, and cognitive psychology more generally are discussed.