Massimo Stella

Massimo Stella, Salvatore Citraro, Giulio Rossetti et al.

The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Decades of 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? We here 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, also in next-generation brain/mind models, we discuss key limitations and promising directions for cutting-edge future research.

Giulio Rossetti, Massimo Stella, Rémy Cazabet et al.

In this paper we introduce Y, a new-generation digital twin designed to replicate an online social media platform. Digital twins are virtual replicas of physical systems that allow for advanced analyses and experimentation. In the case of social media, a digital twin such as Y provides a powerful tool for researchers to simulate and understand complex online interactions. {\tt Y} leverages state-of-the-art Large Language Models (LLMs) to replicate sophisticated agent behaviors, enabling accurate simulations of user interactions, content dissemination, and network dynamics. By integrating these aspects, Y offers valuable insights into user engagement, information spread, and the impact of platform policies. Moreover, the integration of LLMs allows Y to generate nuanced textual content and predict user responses, facilitating the study of emergent phenomena in online environments. To better characterize the proposed digital twin, in this paper we describe the rationale behind its implementation, provide examples of the analyses that can be performed on the data it enables to be generated, and discuss its relevance for multidisciplinary research.

Salvatore Citraro, Simon De Deyne, Massimo Stella et al.

Semantic networks provide a useful tool to understand how related concepts are retrieved from memory. However, most current network approaches use pairwise links to represent memory recall patterns. Pairwise connections neglect higher-order associations, i.e. relationships between more than two concepts at a time. These higher-order interactions might covariate with (and thus contain information about) how similar concepts are along psycholinguistic dimensions like arousal, valence, familiarity, gender and others. We overcome these limits by introducing feature-rich cognitive hypergraphs as quantitative models of human memory where: (i) concepts recalled together can all engage in hyperlinks involving also more than two concepts at once (cognitive hypergraph aspect), and (ii) each concept is endowed with a vector of psycholinguistic features (feature-rich aspect). We build hypergraphs from word association data and use evaluation methods from machine learning features to predict concept concreteness. Since concepts with similar concreteness tend to cluster together in human memory, we expect to be able to leverage this structure. Using word association data from the Small World of Words dataset, we compared a pairwise network and a hypergraph with N=3586 concepts/nodes. Interpretable artificial intelligence models trained on (1) psycholinguistic features only, (2) pairwise-based feature aggregations, and on (3) hypergraph-based aggregations show significant differences between pairwise and hypergraph links. Specifically, our results show that higher-order and feature-rich hypergraph models contain richer information than pairwise networks leading to improved prediction of word concreteness. The relation with previous studies about conceptual clustering and compartmentalisation in associative knowledge and human memory are discussed.

Katherine Abramski, Riccardo Improta, Giulio Rossetti et al.

Free associations have been extensively used in cognitive psychology and linguistics for studying how conceptual knowledge is organized. Recently, the potential of applying a similar approach for investigating the knowledge encoded in LLMs has emerged, specifically as a method for investigating LLM biases. However, the absence of large-scale LLM-generated free association norms that are comparable with human-generated norms is an obstacle to this new research direction. To address this limitation, we create a new dataset of LLM-generated free association norms modeled after the "Small World of Words" (SWOW) human-generated norms consisting of approximately 12,000 cue words. We prompt three LLMs, namely Mistral, Llama3, and Haiku, with the same cues as those in the SWOW norms to generate three novel comparable datasets, the "LLM World of Words" (LWOW). Using both SWOW and LWOW norms, we construct cognitive network models of semantic memory that represent the conceptual knowledge possessed by humans and LLMs. We demonstrate how these datasets can be used for investigating implicit biases in humans and LLMs, such as the harmful gender stereotypes that are prevalent both in society and LLM outputs.

Edith Haim, Natalie Fischer, Salvatore Citraro et al.

Creativity is a fundamental skill of human cognition. We use textual forma mentis networks (TFMN) to extract network (semantic/syntactic associations) and emotional features from approximately one thousand human- and GPT3.5-generated stories. Using Explainable Artificial Intelligence (XAI), we test whether features relative to Mednick's associative theory of creativity can explain creativity ratings assigned by humans and GPT-3.5. Using XGBoost, we examine three scenarios: (i) human ratings of human stories, (ii) GPT-3.5 ratings of human stories, and (iii) GPT-3.5 ratings of GPT-generated stories. Our findings reveal that GPT-3.5 ratings differ significantly from human ratings not only in terms of correlations but also because of feature patterns identified with XAI methods. GPT-3.5 favours 'its own' stories and rates human stories differently from humans. Feature importance analysis with SHAP scores shows that: (i) network features are more predictive for human creativity ratings but also for GPT-3.5's ratings of human stories; (ii) emotional features played a greater role than semantic/syntactic network structure in GPT-3.5 rating its own stories. These quantitative results underscore key limitations in GPT-3.5's ability to align with human assessments of creativity. We emphasise the need for caution when using GPT-3.5 to assess and generate creative content, as it does not yet capture the nuanced complexity that characterises human creativity.

Salvatore Citraro, Edith Haim, Alessandra Carini et al.

We introduce SpreadPy as a Python library for simulating spreading activation in cognitive single-layer and multiplex networks. Our tool is designed to perform numerical simulations testing structure-function relationships in cognitive processes. By comparing simulation results with grounded theories in knowledge modelling, SpreadPy enables systematic investigations of how activation dynamics reflect cognitive, psychological and clinical phenomena. We demonstrate the library's utility through three case studies: (1) Spreading activation on associative knowledge networks distinguishes students with high versus low math anxiety, revealing anxiety-related structural differences in conceptual organization; (2) Simulations of a creativity task show that activation trajectories vary with task difficulty, exposing how cognitive load modulates lexical access; (3) In individuals with aphasia, simulated activation patterns on lexical networks correlate with empirical error types (semantic vs. phonological) during picture-naming tasks, linking network structure to clinical impairments. SpreadPy's flexible framework allows researchers to model these processes using empirically derived or theoretical networks, providing mechanistic insights into individual differences and cognitive impairments. The library is openly available, supporting reproducible research in psychology, neuroscience, and education research.

Edith Haim, Lars van den Bergh, Cynthia S. Q. Siew et al.

Understanding attitudes towards STEM means quantifying the cognitive and emotional ways in which individuals, and potentially large language models too, conceptualise such subjects. This study uses behavioural forma mentis networks (BFMNs) to investigate the STEM-focused mindset, i.e. ways of associating and perceiving ideas, of 177 human participants and 177 artificial humans simulated by GPT-3.5. Participants were split in 3 groups - trainees, experts and academics - to compare the influence of expertise level on their mindset. The results revealed that human forma mentis networks exhibited significantly higher clustering coefficients compared to GPT-3.5, indicating that human mindsets displayed a tendency to form and close triads of conceptual associations while recollecting STEM ideas. Human experts, in particular, demonstrated robust clustering coefficients, reflecting better integration of STEM concepts into their cognitive networks. In contrast, GPT-3.5 produced sparser mindsets. Furthermore, both human and GPT mindsets framed mathematics in neutral or positive terms, differently from STEM high schoolers, researchers and other large language models sampled in other works. This research contributes to understanding how mindset structure can provide cognitive insights about memory structure and machine limitations.

Edoardo Sebastiano De Duro, Enrique Taietta, Riccardo Improta et al.

Machine psychology aims to reconstruct the mindset of Large Language Models (LLMs), i.e. how these artificial intelligences perceive and associate ideas. This work introduces PhDGPT, a prompting framework and synthetic dataset that encapsulates the machine psychology of PhD researchers and professors as perceived by OpenAI's GPT-3.5. The dataset consists of 756,000 datapoints, counting 300 iterations repeated across 15 academic events, 2 biological genders, 2 career levels and 42 unique item responses of the Depression, Anxiety, and Stress Scale (DASS-42). PhDGPT integrates these psychometric scores with their explanations in plain language. This synergy of scores and texts offers a dual, comprehensive perspective on the emotional well-being of simulated academics, e.g. male/female PhD students or professors. By combining network psychometrics and psycholinguistic dimensions, this study identifies several similarities and distinctions between human and LLM data. The psychometric networks of simulated male professors do not differ between physical and emotional anxiety subscales, unlike humans. Other LLMs' personification can reconstruct human DASS factors with a purity up to 80%. Furthemore, LLM-generated personifications across different scenarios are found to elicit explanations lower in concreteness and imageability in items coding for anxiety, in agreement with past studies about human psychology. Our findings indicate an advanced yet incomplete ability for LLMs to reproduce the complexity of human psychometric data, unveiling convenient advantages and limitations in using LLMs to replace human participants. PhDGPT also intriguingly capture the ability for LLMs to adapt and change language patterns according to prompted mental distress contextual features, opening new quantitative opportunities for assessing the machine psychology of these artificial intelligences.

Katherine Abramski, Salvatore Citraro, Luigi Lombardi et al.

Large language models are becoming increasingly integrated into our lives. Hence, it is important to understand the biases present in their outputs in order to avoid perpetuating harmful stereotypes, which originate in our own flawed ways of thinking. This challenge requires developing new benchmarks and methods for quantifying affective and semantic bias, keeping in mind that LLMs act as psycho-social mirrors that reflect the views and tendencies that are prevalent in society. One such tendency that has harmful negative effects is the global phenomenon of anxiety toward math and STEM subjects. Here, we investigate perceptions of math and STEM fields provided by cutting-edge language models, namely GPT-3, Chat-GPT, and GPT-4, by applying an approach from network science and cognitive psychology. Specifically, we use behavioral forma mentis networks (BFMNs) to understand how these LLMs frame math and STEM disciplines in relation to other concepts. We use data obtained by probing the three LLMs in a language generation task that has previously been applied to humans. Our findings indicate that LLMs have an overall negative perception of math and STEM fields, with math being perceived most negatively. We observe significant differences across the three LLMs. We observe that newer versions (i.e. GPT-4) produce richer, more complex perceptions as well as less negative perceptions compared to older versions and N=159 high-school students. These findings suggest that advances in the architecture of LLMs may lead to increasingly less biased models that could even perhaps someday aid in reducing harmful stereotypes in society rather than perpetuating them.

Alfonso Semeraro, Salvatore Vilella, Giancarlo Ruffo et al.

Since their announcement in November 2020, COVID-19 vaccines were largely debated by the press and social media. With most studies focusing on COVID-19 disinformation in social media, little attention has been paid to how mainstream news outlets framed COVID-19 narratives compared to alternative sources. To fill this gap, we use cognitive network science and natural language processing to reconstruct time-evolving semantic and emotional frames of 5745 Italian news, that were massively re-shared on Facebook and Twitter, about COVID-19 vaccines. We found consistently high levels of trust/anticipation and less disgust in the way mainstream sources framed the general idea of "vaccine/vaccino". These emotions were crucially missing in the ways alternative sources framed COVID-19 vaccines. More differences were found within specific instances of vaccines. Alternative news included titles framing the AstraZeneca vaccine with strong levels of sadness, absent in mainstream titles. Mainstream news initially framed "Pfizer" along more negative associations with side effects than "AstraZeneca". With the temporary suspension of the latter, on March 15th 2021, we identified a semantic/emotional shift: Even mainstream article titles framed "AstraZeneca" as semantically richer in negative associations with side effects, while "Pfizer" underwent a positive shift in valence, mostly related to its higher efficacy. "Thrombosis" entered the frame of vaccines together with fearful conceptual associations, while "death" underwent an emotional shift, steering towards fear in alternative titles and losing its hopeful connotation in mainstream titles. Our findings expose crucial aspects of the emotional narratives around COVID-19 vaccines adopted by the press, highlighting the need to understand how alternative and mainstream media report vaccination news.

Salvatore Citraro, Michael S. Vitevitch, Massimo Stella et al.

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). Conformity-based 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.

Simmi Marina Joseph, Salvatore Citraro, Virginia Morini et al.

Writing messages is key to expressing feelings. This study adopts cognitive network science to reconstruct how individuals report their feelings in clinical narratives like suicide notes or mental health posts. We achieve this by reconstructing syntactic/semantic associations between conceptsin texts as co-occurrences enriched with affective data. We transform 142 suicide notes and 77,000 Reddit posts from the r/anxiety, r/depression, r/schizophrenia, and r/do-it-your-own (r/DIY) forums into 5 cognitive networks, each one expressing meanings and emotions as reported by authors. These networks reconstruct the semantic frames surrounding 'feel', enabling a quantification of prominent associations and emotions focused around feelings. We find strong feelings of sadness across all clinical Reddit boards, added to fear r/depression, and replaced by joy/anticipation in r/DIY. Semantic communities and topic modelling both highlight key narrative topics of 'regret', 'unhealthy lifestyle' and 'low mental well-being'. Importantly, negative associations and emotions co-existed with trustful/positive language, focused on 'getting better'. This emotional polarisation provides quantitative evidence that online clinical boards possess a complex structure, where users mix both positive and negative outlooks. This dichotomy is absent in the r/DIY reference board and in suicide notes, where negative emotional associations about regret and pain persist but are overwhelmed by positive jargon addressing loved ones. Our quantitative comparisons provide strong evidence that suicide notes encapsulate different ways of expressing feelings compared to online Reddit boards, the latter acting more like personal diaries and relief valve. Our findings provide an interpretable, quantitative aid for supporting psychological inquiries of human feelings in digital and clinical settings.

Asra Fatima, Li Ying, Thomas Hills et al.

Most current affect scales and sentiment analysis on written text focus on quantifying valence (sentiment) -- the most primary dimension of emotion. However, emotions are broader and more complex than valence. Distinguishing negative emotions of similar valence could be important in contexts such as mental health. This project proposes a semi-supervised machine learning model (DASentimental) to extract depression, anxiety and stress from written text. First, we trained the model to spot how sequences of recalled emotion words by $N=200$ individuals correlated with their responses to the Depression Anxiety Stress Scale (DASS-21). Within the framework of cognitive network science, we model every list of recalled emotions as a walk over a networked mental representation of semantic memory, with emotions connected according to free associations in people's memory. Among several tested machine learning approaches, we find that a multilayer perceptron neural network trained on word sequences and semantic network distances can achieve state-of-art, cross-validated predictions for depression ($R = 0.7$), anxiety ($R = 0.44$) and stress ($R = 0.52$). Though limited by sample size, this first-of-its-kind approach enables quantitative explorations of key semantic dimensions behind DAS levels. We find that semantic distances between recalled emotions and the dyad "sad-happy" are crucial features for estimating depression levels but are less important for anxiety and stress. We also find that semantic distance of recalls from "fear" can boost the prediction of anxiety but it becomes redundant when the "sad-happy" dyad is considered. Adopting DASentimental as a semi-supervised learning tool to estimate DAS in text, we apply it to a dataset of 142 suicide notes. We conclude by discussing key directions for future research enabled by artificial intelligence detecting stress, anxiety and depression.

Massimo Stella

Math anxiety is a clinical pathology impairing cognitive processing in math-related contexts. Originally thought to affect only inexperienced, low-achieving students, recent investigations show how math anxiety is vastly diffused even among high-performing learners. This review of data-informed studies outlines math anxiety as a complex system that: (i) cripples well-being, self-confidence and information processing on both conscious and subconscious levels, (ii) can be transmitted by social interactions, like a pathogen, and worsened by distorted perceptions, (iii) affects roughly 20% of students in 63 out of 64 worldwide educational systems but correlates weakly with academic performance, and (iv) poses a concrete threat to students' well-being, computational literacy and career prospects in science. These patterns underline the crucial need to go beyond performance for estimating math anxiety. Recent advances with network psychometrics and cognitive network science provide ideal frameworks for detecting, interpreting and intervening upon such clinical condition. Merging education research, psychology and data science, the approaches reviewed here reconstruct psychological constructs as complex systems, represented either as multivariate correlation models (e.g. graph exploratory analysis) or as cognitive networks of semantic/emotional associations (e.g. free association networks or forma mentis networks). Not only can these interconnected networks detect otherwise hidden levels of math anxiety but - more crucially - they can unveil the specific layout of interacting factors, e.g. key sources and targets, behind math anxiety in a given cohort. As discussed here, these network approaches open concrete ways for unveiling students' perceptions, emotions and mental well-being, and can enable future powerful data-informed interventions untangling math anxiety.

Massimo Stella, Michael S. Vitevitch, Federico Botta

Monitoring social discourse about COVID-19 vaccines is key to understanding how large populations perceive vaccination campaigns. We focus on 4765 unique popular tweets in English or Italian about COVID-19 vaccines between 12/2020 and 03/2021. One popular English tweet was liked up to 495,000 times, stressing how popular tweets affected cognitively massive populations. We investigate both text and multimedia in tweets, building a knowledge graph of syntactic/semantic associations in messages including visual features and indicating how online users framed social discourse mostly around the logistics of vaccine distribution. The English semantic frame of "vaccine" was highly polarised between trust/anticipation (towards the vaccine as a scientific asset saving lives) and anger/sadness (mentioning critical issues with dose administering). Semantic associations with "vaccine," "hoax" and conspiratorial jargon indicated the persistence of conspiracy theories and vaccines in massively read English posts (absent in Italian messages). The image analysis found that popular tweets with images of people wearing face masks used language lacking the trust and joy found in tweets showing people with no masks, indicating a negative affect attributed to face covering in social discourse. A behavioural analysis revealed a tendency for users to share content eliciting joy, sadness and disgust and to like less sad messages, highlighting an interplay between emotions and content diffusion beyond sentiment. With the AstraZeneca vaccine being suspended in mid March 2021, "Astrazeneca" was associated with trustful language driven by experts, but popular Italian tweets framed "vaccine" by crucially replacing earlier levels of trust with deep sadness. Our results stress how cognitive networks and innovative multimedia processing open new ways for reconstructing online perceptions about vaccines and trust.

Massimo Stella

Social media are digitalising massive amounts of users' cognitions in terms of timelines and emotional content. Such Big Data opens unprecedented opportunities for investigating cognitive phenomena like perception, personality and information diffusion but requires suitable interpretable frameworks. Since social media data come from users' minds, worthy candidates for this challenge are cognitive networks, models of cognition giving structure to mental conceptual associations. This work outlines how cognitive network science can open new, quantitative ways for understanding cognition through online media, like: (i) reconstructing how users semantically and emotionally frame events with contextual knowledge unavailable to machine learning, (ii) investigating conceptual salience/prominence through knowledge structure in social discourse; (iii) studying users' personality traits like openness-to-experience, curiosity, and creativity through language in posts; (iv) bridging cognitive/emotional content and social dynamics via multilayer networks comparing the mindsets of influencers and followers. These advancements combine cognitive-, network- and computer science to understand cognitive mechanisms in both digital and real-world settings but come with limitations concerning representativeness, individual variability and data integration. Such aspects are discussed along the ethical implications of manipulating socio-cognitive data. In the future, reading cognitions through networks and social media can expose cognitive biases amplified by online platforms and relevantly inform policy making, education and markets about massive, complex cognitive trends.

Andreia Sofia Teixeira, Szymon Talaga, Trevor James Swanson et al.

Understanding the cognitive and emotional perceptions of people who commit suicide is one of the most sensitive scientific challenges. There are circumstances where people feel the need to leave something written, an artifact where they express themselves, registering their last words and feelings. These suicide notes are of utmost importance for better understanding the psychology of suicidal ideation. This work gives structure to the linguistic content of suicide notes, revealing interconnections between cognitive and emotional states of people who committed suicide. We build upon cognitive network science, psycholinguistics and semantic frame theory to introduce a network representation of the mindset expressed in suicide notes. Our cognitive network representation enables the quantitative analysis of the language in suicide notes through structural balance theory, semantic prominence and emotional profiling. Our results indicate that the emotional syntax connecting positively- and negatively-valenced terms gives rise to a degree of structural balance that is significantly higher than null models where the affective structure was randomized. We show that suicide notes are affectively compartmentalized such that positive concepts tend to cluster together and dominate the overall network structure. A key positive concept is "love", which integrates information relating the self to others in ways that are semantically prominent across suicide notes. The emotions populating the semantic frame of "love" combine joy and trust with anticipation and sadness, which connects with psychological theories about meaning-making and narrative psychology. Our results open new ways for understanding the structure of genuine suicide notes informing future research for suicide prevention.

Massimo Stella, Manlio De Domenico

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.

Massimo Stella, Emilio Ferrara, Manlio De Domenico

Societies are complex systems which tend to polarize into sub-groups of individuals with dramatically opposite perspectives. This phenomenon is reflected -- and often amplified -- in online social networks where, however, humans are no more the only players, and co-exist alongside with social bots, i.e., software-controlled accounts. Analyzing large-scale social data collected during the Catalan referendum for independence on October 1, 2017, consisting of nearly 4 millions Twitter posts generated by almost 1 million users, we identify the two polarized groups of Independentists and Constitutionalists and quantify the structural and emotional roles played by social bots. We show that bots act from peripheral areas of the social system to target influential humans of both groups, bombarding Independentists with violent contents, increasing their exposure to negative and inflammatory narratives and exacerbating social conflict online. Our findings stress the importance of developing countermeasures to unmask these forms of automated social manipulation.

Massimo Stella, Nicole M. Beckage, Markus Brede et al.

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.

Massimo Stella, Nicole M. Beckage, Markus Brede

Network models of language have provided a way of linking cognitive processes to the structure and connectivity of language. However, one shortcoming of current approaches is focusing on only one type of linguistic relationship at a time, missing the complex multi-relational nature of language. In this work, we overcome this limitation by modelling the mental lexicon of English-speaking toddlers as a multiplex lexical network, i.e. a multi-layered network where N=529 words/nodes are connected according to four types of relationships: (i) free associations, (ii) feature sharing, (iii) co-occurrence, and (iv) phonological similarity. We provide analysis of the topology of the resulting multiplex and then proceed to evaluate single layers as well as the full multiplex structure on their ability to predict empirically observed age of acquisition data of English speaking toddlers. We find that the emerging multiplex network topology is an important proxy of the cognitive processes of acquisition, capable of capturing emergent lexicon structure. In fact, we show that the multiplex topology is fundamentally more powerful than individual layers in predicting the ordering with which words are acquired. Furthermore, multiplex analysis allows for a quantification of distinct phases of lexical acquisition in early learners: while initially all the multiplex layers contribute to word learning, after about month 23 free associations take the lead in driving word acquisition.

Massimo Stella, Markus Brede

In this paper we provide a quantitative framework for the study of phonological networks (PNs) for the English language by carrying out principled comparisons to null models, either based on site percolation, randomization techniques, or network growth models. In contrast to previous work, we mainly focus on null models that reproduce lower order characteristics of the empirical data. We find that artificial networks matching connectivity properties of the English PN are exceedingly rare: this leads to the hypothesis that the word repertoire might have been assembled over time by preferentially introducing new words which are small modifications of old words. Our null models are able to explain the "power-law-like" part of the degree distributions and generally retrieve qualitative features of the PN such as high clustering, high assortativity coefficient, and small-world characteristics. However, the detailed comparison to expectations from null models also points out significant differences, suggesting the presence of additional constraints in word assembly. Key constraints we identify are the avoidance of large degrees, the avoidance of triadic closure, and the avoidance of large non-percolating clusters.