Phonological similarity judgments of word pairs reflect sensitivity to large-scale structure of the phonological lexicon

Network analyses of the phonological mental lexicon show that words are clustered into communities and phonologically dissimilar words can be connected to each other through distant paths. Here we investigate whether behavioral traces of the large-scale structure of the phonological lexicon can be obtained. Participants listened to pairs of spoken words and made phonological similarity judgments for word pairs with varying path lengths and community membership. Path length in the phonological network represented the number of steps needed to traverse from one word to another word in the phonological network. Word pairs were either from the same phonological community or from different communities. Results indicated that participants were sensitive to large-scale structure of the phonological lexicon. Word pairs residing in the same community were more likely rated as similar sounding than word pairs from different communities. Word pairs with longer path lengths were less likely rated as similar sounding than word pairs with shorter path lengths. Computational simulations suggested that the behavioral findings could be accounted for via a spreading activation mechanism implemented on the phonological network. Taken together, our results provide converging evidence that people are sensitive to the large-scale structure of the phonological language network and have implications for our understanding of the nature of phonological similarity representations in the mental lexicon. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Generalized network psychometrics of eating-disorder psychopathology

OBJECTIVE

As network models of eating disorder (ED) psychopathology become increasingly popular in modeling symptom interconnectedness and identifying potential treatment targets, it is necessary to contextualize their performance against other methods of modeling ED psychopathology and to evaluate potential ways to optimize and capitalize on their use. To accomplish these goals, we used generalized network psychometrics to estimate and compare latent variable models and network models, as well as hybrid models.

METHOD

We tested the structure of the Eating Pathology Symptoms Inventory (EPSI) and Eating Disorder Examination-Questionnaire (EDE-Q) in Recovery Record, Inc. mobile phone application users (N = 6856).

RESULTS

Although all models fit well, results favored a hybrid latent variable and network framework, which showed that ED symptoms fit best when modeled as higher-order constructs, rather than direct symptom-to-symptom connections, and when the relationships between those constructs are described as a network. Hybrid models in which latent factors were modeled as nodes within a network showed that EPSI Purging, Binge Eating, Cognitive Restraint, Body Dissatisfaction, and Excessive Exercise had high importance in the network. EDE-Q Eating Concern and Shape Concern were also important nodes. Results showed that the EPSI network was highly stable and replicable, whereas the EDE-Q network was not.

DISCUSSION

Integrating latent variable and network model frameworks enables tests of centrality to identify important latent variables, such as purging, that may promote the spread of ED psychopathology throughout a network, allowing for the identification of future treatment targets.

The network nature of language endangerment hotspots

Language endangerment is one of the most urgent issues of the twenty-first century. Languages are disappearing at unprecedented rates, with dire consequences that affect speaker communities, scientific community and humanity. There is impetus for understanding the nature of language endangerment, and we investigate where language endangerment occurs by performing network analysis on 3423 languages at various levels of risk. Macro-level analysis shows evidence of positive assortative mixing of endangerment statuses—critically endangered languages are surrounded by similarly endangered languages, indicating the prevalence of linguistic hotspots throughout the world. Meso-level analysis using community detection returned 13 communities experiencing different levels of threat. Micro-level analysis of closeness centrality shows that more geographically isolated languages tend to be more critically endangered. Even after accounting for the statistical contributions of linguistic diversity, the structural properties of the spatial network were still significantly associated with endangerment outcomes. Findings support that the notion of hotspots is useful when accounting for language endangerment but go beyond that to establish that quantifying spatial structure is crucial. Language preservation in these hotspots and understanding why endangered languages pattern the way they do in their environments becomes more vital than ever.

Nymph piss and gravy orgies: Local and global contrast effects in relational humor

How does the relation between two words create humor? In this article, we investigated the effect of global and local contrast on the humor of word pairs. We capitalized on the existence of psycholinguistic lexical norms by examining violations of expectations set up by typical patterns of English usage (global contrast) and within the local context of the words within the word pairs (local contrast). Global contrast was operationalized as lexical-semantic norms for single-words and local contrast was operationalized as the orthographic, phonological, and semantic distance between the two words in the pair. Through crowd-sourced (Study 1) and best-worst (Study 2) ratings of the humor of a large set of word pairs (i.e., compounds), we find evidence of both global and local contrast on compound-word humor. Specifically, we find that humor arises when there is a violation of expectations at the local level, between the individual words that make up the word pair, even after accounting for violations at the global level relative to the entire language. Semantic variables (arousal, dominance, and concreteness) were stronger predictors of word pair humor whereas form-related variables (number of letters, phonemes, and letter frequency) were stronger predictors of single-word humor. Moreover, we also find that semantic dissimilarity increases humor, by defusing the impact of low-valence words-making them seem more amusing-and by enhancing the incongruence of highly imageable pairs of concrete words. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Global and Local Feature Distinctiveness Effects in Language Acquisition

Various aspects of semantic features drive early vocabulary development, but less is known about how the global and local structure of the overall semantic feature space influences language acquisition. A feature network of English words was constructed from a large database of adult feature production norms such that edges in the network represented feature distances between words (i.e., Manhattan distances of probability distributions of features elicited for each pair of words). A word's global feature distinctiveness is measured with respect to all other words in the network and a word's local feature distinctiveness is measured relative to words in sub-networks derived from clustering analyses. This paper investigates how feature distinctiveness of individual words at local and global scales of the network influences language acquisition. Regression analyses indicate that global feature distinctiveness was associated with earlier age of acquisition ratings, and was a stronger predictor of age of acquisition than local feature distinctiveness. These results suggest that the global structure of the semantic feature network could play an important role in language acquisition, whereby globally distinctive concepts help to structure vocabulary development over the lifespan.

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.

An investigation of network growth principles in the phonological language network

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

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.

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.

Interpersonal Functioning in Borderline Personality Disorder Traits: A Social Media Perspective

This is the first study to demonstrate interpersonal difficulties associated with borderline personality disorder (BPD) features in the domain of social media. Using crowdsourcing, we presented participants with a battery of questions about their recent social media use, and then assessed their BPD features using the short form of the Five-Factor Borderline Inventory. The results revealed that individuals with higher BPD trait scores reported posting more often on social media, as well as a higher incidence of experiencing regret after posting on social media, and of deleting or editing their posts. They also report a higher degree of importance of social media in their social behavior and daily routines. These results highlight the pervasiveness of interpersonal difficulties associated with BPD features even in the non-clinical population, and demonstrate that these difficulties are also observable in social media behavior. Our findings may provide a starting point for research using data from social media to illuminate the cognitive and emotional processes underpinning the interpersonal difficulties associated with BPD features, and to inform and assess therapeutic interventions.

Application of network analysis to investigate sex differences in interactive systems of eating-disorder psychopathology

OBJECTIVE

Although men comprise 25% of persons with eating disorders (EDs), most research has focused on understanding EDs in women. The theoretical framework underlying common ED treatment has not been rigorously tested in men. The purpose of this study was to compare the interconnectivity among ED symptoms in men versus women.

METHOD

Participants (N = 1,348; 50% men) were individuals with anorexia nervosa, bulimia nervosa, binge-eating disorder, or other specified feeding or eating disorder who were users of Recovery Record, a smartphone app for monitoring ED symptoms. Participants were matched on age and duration of illness. Network analysis was used to create networks of symptoms for both sexes. Strength centrality, network stability, and bootstrapped centrality differences were tested. The network comparison test (NCT) was used to identify sex differences between networks. Key players analysis was used to compare fragmentation of each network.

RESULTS

For both sexes, items related to binge eating and restricting emerged as highest in strength centrality. The NCT identified significant differences global strength (p = .03) but not network invariance (p = .06) suggesting that although the structure of the networks was not statistically different, the strength of the connections within the network was greater for women. Key players analysis indicated that both networks were similarly disrupted when important nodes within the network were removed.

DISCUSSION

Findings suggested that there are more similarities than differences in networks of EDs in men and women. Results have important clinical implications by supporting theoretical underpinnings of cognitive-behavioral models of EDs in both men and women.

Forma mentis networks quantify crucial differences in STEM perception between students and experts

In order to investigate how high school students and researchers perceive science-related (STEM) subjects, we introduce forma mentis networks. This framework models how people conceptually structure their stance, mindset or forma mentis toward a given topic. In this study, we build forma mentis networks revolving around STEM and based on psycholinguistic data, namely free associations of STEM concepts (i.e., which words are elicited first and associated by students/researchers reading "science"?) and their valence ratings concepts (i.e., is "science" perceived as positive, negative or neutral by students/researchers?). We construct separate networks for (Ns = 159) Italian high school students and (Nr = 59) interdisciplinary professionals and researchers in order to investigate how these groups differ in their conceptual knowledge and emotional perception of STEM. Our analysis of forma mentis networks at various scales indicate that, like researchers, students perceived "science" as a strongly positive entity. However, differently from researchers, students identified STEM subjects like "physics" and "mathematics" as negative and associated them with other negative STEM-related concepts. We call this surrounding of negative associations a negative emotional aura. Cross-validation with external datasets indicated that the negative emotional auras of physics, maths and statistics in the students' forma mentis network related to science anxiety. Furthermore, considering the semantic associates of "mathematics" and "physics" revealed that negative auras may originate from a bleak, dry perception of the technical methodology and mnemonic tools taught in these subjects (e.g., calculus rules). Overall, our results underline the crucial importance of emphasizing nontechnical and applied aspects of STEM disciplines, beyond purely methodological teaching. The quantitative insights achieved through forma mentis networks highlight the necessity of establishing novel pedagogic and interdisciplinary links between science, its real-world complexity, and creativity in science learning in order to enhance the impact of STEM education, learning and outreach activities.

spreadr: An R package to simulate spreading activation in a network

The notion of spreading activation is a central theme in the cognitive sciences; however, the tools for implementing spreading activation computationally are not as readily available. This article introduces the spreadr R package, which can implement spreading activation within a specified network structure. The algorithmic method implemented in the spreadr subroutines follows the approach described in Vitevitch, Ercal, and Adagarla (Frontiers in Psychology, 2, 369, 2011), who viewed activation as a fixed cognitive resource that could "spread" among connected nodes in a network. Three sets of simulations were conducted using the package. The first set of simulations successfully reproduced the results reported in Vitevitch et al. (Frontiers in Psychology, 2, 369, 2011), who showed that a simple mechanism of spreading activation could account for the clustering coefficient effect in spoken word recognition. The second set of simulations showed that the same mechanism could be extended to account for higher false alarm rates for low clustering coefficient words in a false memory task. The final set of simulations demonstrated how spreading activation could be applied to a semantic network to account for semantic priming effects. It is hoped that this package will encourage cognitive and language scientists to explicitly consider how the structures of cognitive systems such as the mental lexicon and semantic memory interact with the process of spreading activation.

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.

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.

Application of network analysis to identify interactive systems of eating disorder psychopathology

BACKGROUND

Traditional approaches for the classification of eating disorders (EDs) attribute symptoms to an underlying, latent disease entity. The network approach is an alternative model in which mental disorders are represented as networks of interacting, self-reinforcing symptoms. This project was the first to use network analysis to identify interconnected systems of ED symptoms.

METHOD

Adult participants (n = 143; 77.6% women) with a Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) ED were recruited from the community to take part in a larger ongoing longitudinal study. The Structured Clinical Interview for DSM Disorders (SCID-I) was used to establish diagnoses. An undirected network of ED symptoms was created using items from the Eating Pathology Symptoms Inventory (EPSI) and the R package qgraph.

RESULTS

Body checking emerged as the strongest and most important single symptom in the entire network by having the shortest average distance to other symptoms in the network, and by being the most frequent symptom on the path between any two other symptoms. Feeling the need to exercise every day and two symptoms assessing dietary restraint/restricting emerged as 'key players', such that their removal from the network resulted in maximal fracturing of the network into smaller components.

CONCLUSIONS

Although cognitive-behavioral therapy for EDs focuses on reducing body checking to promote recovery, our data indicate that amplified efforts to address body checking may produce stronger (and more enduring) effects. Finally, results of the 'key players analysis' suggested that targeting interventions at these key nodes might prevent or slow the cascade of symptoms through the 'network' of ED psychopathology.

Estimating group size from human speech: Three's a conversation, but four's a crowd

Much previous research has examined various aspects of auditory processing, including the localization of sounds, and the influence of lexical and indexical information on language processing. In the present set of experiments we explored the ability of listeners to estimate the number of speakers in a group solely from the information in an auditory signal. The bound on accurately estimating the number of simultaneous speakers is 3. We suggest that subitization-the ability to estimate numerosity of visual and auditory elements without explicitly counting these elements-rather than the capacity of short-term memory, may underlie this limitation. The cognitive constraint on estimating the number of simultaneous speakers may have implications for a wide variety of seemingly unrelated psychological phenomena.

Speech error and tip of the tongue diary for mobile devices

Collections of various types of speech errors have increased our understanding of the acquisition, production, and perception of language. Although such collections of naturally occurring language errors are invaluable for a number of reasons, the process of collecting various types of speech errors presents many challenges to the researcher interested in building such a collection, among them a significant investment of time and effort to obtain a sufficient number of examples to enable statistical analysis. Here we describe a freely accessible website http://spedi.ku.edu that helps users document slips of the tongue, slips of the ear, and tip of the tongue states that they experience firsthand or observe in others. The documented errors are amassed, and made available for other users to analyze, thereby distributing the time and effort involved in collecting errors across a large number of individuals instead of saddling the lone researcher, and facilitating distribution of the collection to other researchers. This approach also addresses some issues related to data curation that hampered previous error collections, and enables the collection to continue to grow over a longer period of time than previous collections. Finally, this web-based tool creates an opportunity for language scientists to engage in outreach efforts to increase the understanding of language disorders and research in the general public.

Spoken word recognition and serial recall of words from components in the phonological network

Network science uses mathematical techniques to study complex systems such as the phonological lexicon (Vitevitch, 2008). The phonological network consists of a giant component (the largest connected component of the network) and lexical islands (smaller groups of words that are connected to each other, but not to the giant component). To determine if the component that a word resided in influenced lexical processing, language-related tasks (naming, lexical decision, and serial recall) were used to compare the processing of words from the giant component and from lexical islands. Results showed that words from lexical islands were recognized more quickly and recalled more accurately than words from the giant component. These findings can be accounted for via the diffusion of activation across a network. Implications for models of spoken word recognition and network science are also discussed.

Community structure in the phonological network

Community structure, which refers to the presence of densely connected groups within a larger network, is a common feature of several real-world networks from a variety of domains such as the human brain, social networks of hunter-gatherers and business organizations, and the World Wide Web (Porter et al., 2009). Using a community detection technique known as the Louvain optimization method, 17 communities were extracted from the giant component of the phonological network described in Vitevitch (2008). Additional analyses comparing the lexical and phonological characteristics of words in these communities against words in randomly generated communities revealed several novel discoveries. Larger communities tend to consist of short, frequent words of high degree and low age of acquisition ratings, and smaller communities tend to consist of longer, less frequent words of low degree and high age of acquisition ratings. Real communities also contained fewer different phonological segments compared to random communities, although the number of occurrences of phonological segments found in real communities was much higher than that of the same phonological segments in random communities. Interestingly, the observation that relatively few biphones occur very frequently and a large number of biphones occur rarely within communities mirrors the pattern of the overall frequency of words in a language (Zipf, 1935). The present findings have important implications for understanding the dynamics of activation spread among words in the phonological network that are relevant to lexical processing, as well as understanding the mechanisms that underlie language acquisition and the evolution of language.