The multiplex structure of the mental lexicon influences picture naming in people with aphasia

Leveraging relatedness-based measures in people with language disorders: A scoping review

Understanding lexico-semantic processing is crucial for dissecting the complexities of language and its disorders. Relatedness-based measures, or those which investigate the degree of relatedness in meaning between either task items or items produced by participants, offer the opportunity to harness novel computational and analytical techniques from cognitive network science. Recognizing the need to deepen our understanding of lexico-semantic deficits through diverse experimental and analytical approaches, this review explores the use of such measures in research into language disorders. A comprehensive search of four electronic databases covering publications from the last 11 years (October 2013-September 2024) identified 38 original experimental studies employing relatedness-based measures in populations with language disorders or other neurological conditions. Articles were examined for the types of tasks used, populations studied, item selection methods and analytical approaches. The predominant use of category fluency tasks emerged across studies, with a notable absence of relatedness judgement tasks or comparable paradigms. Commonly discussed populations included individuals with post-stroke aphasia, mild cognitive impairment and schizophrenia. Analytical methods varied significantly, ranging from more traditional approaches of clustering and switching to more sophisticated computational techniques. Despite the evident utility of category fluency tasks in research and clinical settings, the review underscores a critical need to diversify experimental paradigms and probe lexico-semantic processing in a more multifaceted manner. A broadened approach in future language disorder research should incorporate innovative analytical techniques, investigations of neural correlates and a wider array of tasks employing relatedness-based measures already present in healthy populations.

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.

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.

Using citation networks to evaluate the impact of text length on keyword extraction

The identification of key concepts within unstructured data is of paramount importance in practical applications. Despite the abundance of proposed methods for extracting primary topics, only a few works investigated the influence of text length on the performance of keyword extraction (KE) methods. Specifically, many studies lean on abstracts and titles for content extraction from papers, leaving it uncertain whether leveraging the complete content of papers can yield consistent results. Hence, in this study, we employ a network-based approach to evaluate the concordance between keywords extracted from abstracts and those from the entire papers. Community detection methods are utilized to identify interconnected papers in citation networks. Subsequently, paper clusters are formed to identify salient terms within each cluster, employing a methodology akin to the term frequency-inverse document frequency (tf-idf) approach. Once each cluster has been endowed with its distinctive set of key terms, these selected terms are employed to serve as representative keywords at the paper level. The top-ranked words at the cluster level, which also appear in the abstract, are chosen as keywords for the paper. Our findings indicate that although various community detection methods used in KE yield similar levels of accuracy. Notably, text clustering approaches outperform all citation-based methods, while all approaches yield relatively low accuracy values. We also identified a lack of concordance between keywords extracted from the abstracts and those extracted from the corresponding full-text source. Considering that citations and text clustering yield distinct outcomes, combining them in hybrid approaches could offer improved performance.

Verb Frequency and Density Drive Naming Performance in Primary Progressive Aphasia

Background

Recent work has highlighted the utility of the Boston Naming Test and Hopkins Action Naming Assessment (HANA) for distinguishing between semantic (svPPA), logopenic (lvPPA) and non-fluent agrammatic (nfavPPA) variants of primary progressive aphasia (PPA).

Aims

To determine whether item level differences between variants on when naming verbs on the HANA were able to be accounted for using common variables of lexical interest: word frequency, semantic density, concreteness, or valency. We also examined three specific hypotheses: (1) svPPA and lvPPA may result in increased difficulty with decreased semantic density compared to nfavPPA; (2) svPPA may result in increased difficulty with decreased concreteness; and (3) nfavPPA may result in increased difficulty with high syntactic valency.

Methods & Procedures

268 patients with PPA were evaluated using the HANA. A hierarchical Bayesian regression approach was adopted to account for effects of repeated measurement within participants and items.

Outcomes & Results

The main effects of variant and verb trait were significant in all models, as was the interaction for frequency, semantic density, and valency. Increasing frequency, semantic density, and concreteness led to better performance, while increasing valency led to poorer performance. Low semantic density contributed to greater difficulty in svPPA and lvPPA, but low concreteness did not uniquely impact verb naming in svPPA. Those with nfavPPA had no particular difficulty as a result of valency.

Conclusions

Prior studies have identified the independent effects of frequency and semantic density on verb naming in PPA, which were confirmed by our analyses, and the best predictions of the data were achieved by combining these dimensions. This investigation complements our previous work highlighting the value of the HANA for efficiently demonstrating verb performance in PPA.

Stimulus- and Person-Level Variables Influence Word Production and Response to Anomia Treatment for Individuals With Chronic Poststroke Aphasia

PURPOSE

The impact of stimulus-level psycholinguistic variables and person-level semantic and phonological processing skills on treatment outcomes in individuals with aphasia requires further examination to inform clinical decision making in treatment prescription and stimuli selection. This study investigated the influence of stimulus-level psycholinguistic properties and person-level semantic and phonological processing skills on word production accuracy and treatment response.

METHOD

This retrospective analysis included 35 individuals with chronic, poststroke aphasia, 30 of whom completed typicality-based semantic feature treatment. Mixed-effects logistic regression models were used to predict binary naming accuracy (a) at baseline and (b) over the course of treatment using stimulus-level psycholinguistic word properties and person-level semantic and phonological processing skills as predictors.

RESULTS

In baseline naming, words with less complex lexical-semantic and phonological properties showed greater predicted accuracy. There was also an interaction at baseline between stimulus-level lexical-semantic properties and person-level semantic processing skills in predicting baseline naming accuracy. With treatment, words that were more complex from a lexical-semantic standpoint (vs. less complex) and less complex from a phonological standpoint (vs. more complex) improved more. Individuals with greater baseline semantic and phonological processing skills showed a greater treatment response.

CONCLUSIONS

This study suggests that future clinical research and clinical work should consider semantic and phonological properties of words in selecting stimuli for semantically based treatment. Furthermore, future clinical research should continue to evaluate baseline individual semantic and phonological profiles as predictors of response to semantically based treatment.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.21256341.

Semantic flow and its relation to controlled semantic retrieval deficits in the narrative production of people with aphasia

Aphasia has had a profound influence on our understanding of how language is instantiated within the human brain. Historically, aphasia has yielded an in vivo model for elucidating the effects of impaired lexical-semantic access on language comprehension and production. Aphasiology has focused intensively on single word dissociations. Yet, less is known about the integrity of combinatorial semantic processes required to construct well-formed narratives. Here we addressed the question of how controlled lexical-semantic retrieval deficits (a hallmark of aphasia) might compound over the course of longer narratives. We specifically examined word-by-word flow of taxonomic vs. thematic semantic distance in the storytelling narratives of individuals with chronic post-stroke aphasia (n = 259) relative to age-matched controls (n = 203). We first parsed raw transcribed narratives into content words and computed inter-word semantic distances for every running pair of words in each narrative (N = 232,490 word transitions). The narratives of people with aphasia showed significant reductions in taxonomic and thematic semantic distance relative to controls. Both distance metrics were strongly predictive of offline measures of semantic impairment and aphasia severity. Since individuals with aphasia often exhibit perseverative language output (i.e., repetitions), we performed additional analyses with repetitions excluded. When repetitions were excluded, group differences in semantic distances persisted and thematic distance was still predictive of semantic impairment, although some findings changed. These results demonstrate the cumulative impact of deficits in controlled word retrieval over the course of narrative production. We discuss the nature of semantic flow between words as a novel metric of characterizing discourse and elucidating the nature of lexical-semantic access impairment in aphasia at multiword levels.

Word Distance Affects Subjective Temporal Distance

The kappa effect is a well-reported phenomenon in which spatial distance between discrete stimuli affects the perception of temporal distance demarcated by the corresponding stimuli. Here, we report a new phenomenon that we propose to designate as the lexical kappa effect in which word distance, a non-magnitude relationship of discrete stimuli that exists in the lexical space of the mental lexicon, affects the perception of temporal distance. A temporal bisection task was used to assess the subjective perception of the time interval demarcated by two successively presented words. Word distance was manipulated by varying the semantic (Experiment 1) or phonological (Experiment 2) similarity between the two words. Results showed that the temporal distance between the two words was perceived to be shorter when the corresponding two words were lexically closer. We explain this effect within the internal clock framework by assuming faster detection of the word that terminated timing when it is preceded by a semantically or phonologically similar word.

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.

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.

Methodological Considerations for Incorporating Clinical Data Into a Network Model of Retrieval Failures

Difficulty retrieving information (e.g., words) from memory is prevalent in neurogenic communication disorders (e.g., aphasia and dementia). Theoretical modeling of retrieval failures often relies on clinical data, despite methodological limitations (e.g., locus of retrieval failure, heterogeneity of individuals, and progression of disorder/disease). Techniques from network science are naturally capable of handling these limitations. This paper reviews recent work using a multiplex lexical network to account for word retrieval failures and highlights how network science can address the limitations of clinical data. Critically, any model we employ could impact clinical practice and patient lives, harkening the need for theoretically well-informed network models.

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.

Text-mining forma mentis networks reconstruct public perception of the STEM gender gap in social media

Mindset reconstruction maps how individuals structure and perceive knowledge, a map unfolded here by investigating language and its cognitive reflection in the human mind, i.e., the mental lexicon. Textual forma mentis networks (TFMN) are glass boxes introduced for extracting and understanding mindsets' structure (in Latin forma mentis) from textual data. Combining network science, psycholinguistics and Big Data, TFMNs successfully identified relevant concepts in benchmark texts, without supervision. Once validated, TFMNs were applied to the case study of distorted mindsets about the gender gap in science. Focusing on social media, this work analysed 10,000 tweets mostly representing individuals' opinions at the beginning of posts. "Gender" and "gap" elicited a mostly positive, trustful and joyous perception, with semantic associates that: celebrated successful female scientists, related gender gap to wage differences, and hoped for a future resolution. The perception of "woman" highlighted jargon of sexual harassment and stereotype threat (a form of implicit cognitive bias) about women in science "sacrificing personal skills for success". The semantic frame of "man" highlighted awareness of the myth of male superiority in science. No anger was detected around "person", suggesting that tweets got less tense around genderless terms. No stereotypical perception of "scientist" was identified online, differently from real-world surveys. This analysis thus identified that Twitter discourse mostly starting conversations promoted a majorly stereotype-free, positive/trustful perception of gender disparity, aimed at closing the gap. Hence, future monitoring against discriminating language should focus on other parts of conversations like users' replies. TFMNs enable new ways for monitoring collective online mindsets, offering data-informed ground for policy making.

Quantifying the Interplay of Semantics and Phonology During Failures of Word Retrieval by People With Aphasia Using a Multiplex Lexical Network

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.

#lockdown: Network-Enhanced Emotional Profiling in the Time of COVID-19

The COVID-19 pandemic forced countries all over the world to take unprecedented measures, like nationwide lockdowns. To adequately understand the emotional and social repercussions, a large-scale reconstruction of how people perceived these unexpected events is necessary but currently missing. We address this gap through social media by introducing MERCURIAL (Multi-layer Co-occurrence Networks for Emotional Profiling), a framework which exploits linguistic networks of words and hashtags to reconstruct social discourse describing real-world events. We use MERCURIAL to analyse 101,767 tweets from Italy, the first country to react to the COVID-19 threat with a nationwide lockdown. The data were collected between the 11th and 17th March, immediately after the announcement of the Italian lockdown and the WHO declaring COVID-19 a pandemic. Our analysis provides unique insights into the psychological burden of this crisis, focussing on—(i) the Italian official campaign for self-quarantine (#iorestoacasa), (ii) national lockdown (#italylockdown), and (iii) social denounce (#sciacalli). Our exploration unveils the emergence of complex emotional profiles, where anger and fear (towards political debates and socio-economic repercussions) coexisted with trust, solidarity, and hope (related to the institutions and local communities). We discuss our findings in relation to mental well-being issues and coping mechanisms, like instigation to violence, grieving, and solidarity. We argue that our framework represents an innovative thermometer of emotional status, a powerful tool for policy makers to quickly gauge feelings in massive audiences and devise appropriate responses based on cognitive data.

Contributions of modern network science to the cognitive sciences: revisiting research spirals of representation and process

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.

Applications of Network Science to Education Research: Quantifying Knowledge and the Development of Expertise through Network Analysis

A fundamental goal of education is to inspire and instill deep, meaningful, and long-lasting conceptual change within the knowledge landscapes of students. This commentary posits that the tools of network science could be useful in helping educators achieve this goal in two ways. First, methods from cognitive psychology and network science could be helpful in quantifying and analyzing the structure of students’ knowledge of a given discipline as a knowledge network of interconnected concepts. Second, network science methods could be relevant for investigating the developmental trajectories of knowledge structures by quantifying structural change in knowledge networks, and potentially inform instructional design in order to optimize the acquisition of meaningful knowledge as the student progresses from being a novice to an expert in the subject. This commentary provides a brief introduction to common network science measures and suggests how they might be relevant for shedding light on the cognitive processes that underlie learning and retrieval, and discusses ways in which generative network growth models could inform pedagogical strategies to enable meaningful long-term conceptual change and knowledge development among students.

Forma mentis networks map how nursing and engineering students enhance their mindsets about innovation and health during professional growth

Reconstructing a "forma mentis", a mindset, and its changes, means capturing how individuals perceive topics, trends and experiences over time. To this aim we use forma mentis networks (FMNs), which enable direct, microscopic access to how individuals conceptually perceive knowledge and sentiment around a topic, providing richer contextual information than machine learning. FMNs build cognitive representations of stances through psycholinguistic tools like conceptual associations from semantic memory (free associations, i.e., one concept eliciting another) and affect norms (valence, i.e., how attractive a concept is). We test FMNs by investigating how Norwegian nursing and engineering students perceived innovation and health before and after a 2-month research project in e-health. We built and analysed FMNs by six individuals, based on 75 cues about innovation and health, and leading to 1,000 associations between 730 concepts. We repeated this procedure before and after the project. When investigating changes over time, individual FMNs highlighted drastic improvements in all students' stances towards "teamwork", "collaboration", "engineering" and "future", indicating the acquisition and strengthening of a positive belief about innovation. Nursing students improved their perception of 'robots" and "technology" and related them to the future of nursing. A group-level analysis related these changes to the emergence, during the project, of conceptual associations about openness towards multidisciplinary collaboration, and a positive, leadership-oriented group dynamics. The whole group identified "mathematics" and "coding" as highly relevant concepts after the project. When investigating persistent associations, characterising the core of students' mindsets, network distance entropy and closeness identified as pivotal in the students' mindsets concepts related to "personal well-being", "professional growth" and "teamwork". This result aligns with and extends previous studies reporting the relevance of teamwork and personal well-being for Norwegian healthcare professionals, also within the novel e-health sector. Our analysis indicates that forma mentis networks are powerful proxies for detecting individual- and group-level mindset changes due to professional growth. FMNs open new scenarios for data-informed, multidisciplinary interventions aimed at professional training in innovation.

Feature distinctiveness effects in language acquisition and lexical processing: Insights from megastudies

Semantic features are central to many influential theories of word meaning and semantic memory, but new methods of quantifying the information embedded in feature production norms are needed to advance our understanding of semantic processing and language acquisition. This paper capitalized on databases of semantic feature production norms and age-of-acquisition ratings, and megastudies including the English Lexicon Project and the Calgary Semantic Decision Project, to examine the influence of feature distinctiveness on language acquisition, visual lexical decision, and semantic decision. A feature network of English words was constructed such that edges in the network represented feature distance, or dissimilarity, between words (i.e., Jaccard and Manhattan distances of probability distributions of features elicited for each pair of words), enabling us to quantify the relative feature distinctiveness of individual words relative to other words in the network. Words with greater feature distinctiveness tended to be acquired earlier. Regression analyses of megastudy data revealed that Manhattan feature distinctiveness inhibited performance on the visual lexical decision task, facilitated semantic decision performance for concrete concepts, and inhibited semantic decision performance for abstract concepts. These results demonstrate the importance of considering the structural properties of words embedded in a semantic feature space in order to increase our understanding of semantic processing and language acquisition.

Viability in Multiplex Lexical Networks and Machine Learning Characterizes Human Creativity

Previous studies have shown how individual differences in creativity relate to differences in the structure of semantic memory. However, the latter is only one aspect of the whole mental lexicon, a repository of conceptual knowledge that is considered to simultaneously include multiple types of conceptual similarities. In the current study, we apply a multiplex network approach to compute a representation of the mental lexicon combining semantics and phonology and examine how it relates to individual differences in creativity. This multiplex combination of 150,000 phonological and semantic associations identifies a core of words in the mental lexicon known as viable cluster, a kernel containing simpler to parse, more general, concrete words acquired early during language learning. We focus on low (N = 47) and high (N = 47) creative individuals’ performance in generating animal names during a semantic fluency task. We model this performance as the outcome of a mental navigation on the multiplex lexical network, going within, outside, and in-between the viable cluster. We find that low and high creative individuals differ substantially in their access to the viable cluster during the semantic fluency task. Higher creative individuals tend to access the viable cluster less frequently, with a lower uncertainty/entropy, reaching out to more peripheral words and covering longer multiplex network distances between concepts in comparison to lower creative individuals. We use these differences for constructing a machine learning classifier of creativity levels, which leads to an accuracy of 65 . 0 ± 0 . 9 % and an area under the curve of 68 . 0 ± 0 . 8 % , which are both higher than the random expectation of 50%. These results highlight the potential relevance of combining psycholinguistic measures with multiplex network models of the mental lexicon for modelling mental navigation and, consequently, classifying people automatically according to their creativity levels.

Distance Entropy Cartography Characterises Centrality in Complex Networks

We introduce distance entropy as a measure of homogeneity in the distribution of path lengths between a given node and its neighbours in a complex network. Distance entropy defines a new centrality measure whose properties are investigated for a variety of synthetic network models. By coupling distance entropy information with closeness centrality, we introduce a network cartography which allows one to reduce the degeneracy of ranking based on closeness alone. We apply this methodology to the empirical multiplex lexical network encoding the linguistic relationships known to English speaking toddlers. We show that the distance entropy cartography better predicts how children learn words compared to closeness centrality. Our results highlight the importance of distance entropy for gaining insights from distance patterns in complex networks.