A comparison of word humor ratings across speakers of North American, British, and Singapore English.

Large-scale collection of lexical-semantic norms for words in a given language has been instrumental in the progress of psycholinguistic research. However, such norms tend to be collected from speakers of the dominant variant or dialect. This research aims to determine if there may be differences across speakers of various dialects of English in the humor of individual words. Engelthaler and Hills (2018, Behavior Research Methods, 50[3], 1116-1124) observed that their humor ratings were most strongly correlated with inverse word frequency: Less frequent words tended to be rated as more humorous. We hypothesized that words that are less frequently occurring in a given English dialect should be perceived as more humorous by speakers of the same dialect. We selected words of relatively higher and lower frequencies across various corpora of North American, British, or Singapore English, and presented these words to participants who were native English speakers of North American, British, or Singapore English. Study 1 compared humor ratings of North Americans and Singaporeans; Study 2 compared humor ratings of North Americans and the British. Analyses of participants' random slope coefficients of frequency extracted from cumulative link mixed-effects models indicated that humor ratings were more strongly (and inversely) associated with the word's frequency in the corpora that aligned with the rater's English dialect. These results provide evidence that people are sensitive to the statistics of their specific language environment, and importantly suggest that creators of lexical-semantic norm databases should consider how the cultural, historical, or sociopolitical context of raters might influence the nature of their ratings.

How cognitive selection affects language change.

Like biological species, words in language must compete to survive. Previously, it has been shown that language changes in response to cognitive constraints and over time becomes more learnable. Here, we use two complementary research paradigms to demonstrate how the survival of existing word forms can be predicted by psycholinguistic properties that impact language production. In the first study, we analyzed the survival of words in the context of interpersonal communication. We analyzed data from a large-scale serial-reproduction experiment in which stories were passed down along a transmission chain over multiple participants. The results show that words that are acquired earlier in life, more concrete, more arousing, and more emotional are more likely to survive retellings. We reason that the same trend might scale up to language evolution over multiple generations of natural language users. If that is the case, the same set of psycholinguistic properties should also account for the change of word frequency in natural language corpora over historical time. That is what we found in two large historical-language corpora (Study 2): Early acquisition, concreteness, and high arousal all predict increasing word frequency over the past 200 y. However, the two studies diverge with respect to the impact of word valence and word length, which we take up in the discussion. By bridging micro-level behavioral preferences and macro-level language patterns, our investigation sheds light on the cognitive mechanisms underlying word competition.

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).

Investigating the network structure of domain-specific knowledge using the semantic fluency task.

Cognitive scientists have a long-standing interest in quantifying the structure of semantic memory. Here, we investigate whether a commonly used paradigm to study the structure of semantic memory, the semantic fluency task, as well as computational methods from network science could be leveraged to explore the underlying knowledge structures of academic disciplines such as psychology or biology. To compare the knowledge representations of individuals with relatively different levels of expertise in academic subjects, undergraduate students (i.e., experts) and preuniversity high school students (i.e., novices) completed a semantic fluency task with cue words corresponding to general semantic categories (i.e., animals, fruits) and specific academic domains (e.g., psychology, biology). Network analyses of their fluency networks found that both domain-general and domain-specific semantic networks of undergraduates were more efficiently connected and less modular than the semantic networks of high school students. Our results provide an initial proof-of-concept that the semantic fluency task could be used by educators and cognitive scientists to study the representation of more specific domains of knowledge, potentially providing new ways of quantifying the nature of expert cognitive representations.

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.

Diachronic semantic change in language is constrained by how people use and learn language.

While it has long been understood that the human mind evolved to learn language, recent studies have begun to ask the inverted question: How has language evolved under the cognitive constraints of its users and become more learnable over time? In this paper, we explored how the semantic change of English words is shaped by the way humans acquire and process language. In Study 1, we quantified the extent of semantic change over the past 200 years and found that meaning change is more likely for words that are acquired later in life and are more difficult to process. We argue that it is human cognition that constrains the semantic evolution of words, rather than the other way around, because historical meanings of words were not easily accessible to people living today, and therefore could not have directly influenced how they learn and process language. In Study 2, we went further to show that semantic change, while bringing the benefit of meeting communicative needs, is cognitively costly for those who were born early enough to experience the change: Semantic change between 1970 and 2000 hindered processing speeds among middle-aged adults (ages 45-55) but not in younger adults (ages <25) in a semantic decision task. This hampering effect may have, in turn, curbed the rate of semantic change so that language does not change too fast for the human mind to catch up. Taken together, our research demonstrates that semantic change is shaped by processing and acquisition patterns across generations of language users.

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.

Investigating the Influence of Inverse Preferential Attachment on Network Development.

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

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.

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.

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).

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.

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.

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 phonographic language network: Using network science to investigate the phonological and orthographic similarity structure of language.

Orthographic effects in spoken word recognition and phonological effects in visual word recognition have been observed in a variety of experimental tasks, strongly suggesting that a close interrelationship exists between phonology and orthography. However, the metrics used to investigate these effects, such as consistency and neighborhood size, fail to generalize to words of various lengths or syllable structures, and do not take into account the more global similarity structure that exists between phonological and orthographic representations in the language. To address these limitations, the tools of Network Science were used to simultaneously characterize the phonological as well as orthographic similarity structure of words in English. In the phonographic network of language, links are placed between words that are both phonologically and orthographically similar to each other (e.g., words such as pant (/pænt/) and punt (/pʌnt/)). Conventional psycholinguistic experiments (auditory naming and auditory lexical decision) and an archival analysis of the English Lexicon Project (visual naming and visual lexical decision) were conducted to investigate the influence of 2 network science metrics derived from the phonographic network-phonographic degree and phonographic clustering coefficient-on spoken and visual word recognition. Results indicated a facilitatory effect of phonographic degree on visual word recognition, and a facilitatory effect of phonographic clustering coefficient on spoken word recognition. Implications of the present findings for theoretical models of spoken and visual word recognition are discussed. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

The Macroscope: A tool for examining the historical structure of language.

The recent rise in digitized historical text has made it possible to quantitatively study our psychological past. This involves understanding changes in what words meant, how words were used, and how these changes may have responded to changes in the environment, such as in healthcare, wealth disparity, and war. Here we make available a tool, the Macroscope, for studying historical changes in language over the last two centuries. The Macroscope uses over 155 billion words of historical text, which will grow as we include new historical corpora, and derives word properties from frequency-of-usage and co-occurrence patterns over time. Using co-occurrence patterns, the Macroscope can track changes in semantics, allowing researchers to identify semantically stable and unstable words in historical text and providing quantitative information about changes in a word's valence, arousal, and concreteness, as well as information about new properties, such as semantic drift. The Macroscope provides information about both the local and global properties of words, as well as information about how these properties change over time, allowing researchers to visualize and download data in order to make inferences about historical psychology. Although quantitative historical psychology represents a largely new field of study, we see this work as complementing a wealth of other historical investigations, offering new insights and new approaches to understanding existing theory. The Macroscope is available online at http://www.macroscope.tech .