Spoken word recognition and serial recall of words from the giant component and words from lexical islands in the phonological network

Network science is a field that applies mathematical techniques to study complex systems, and the tools of network science have been used to analyze the phonological network of language (Vitevitch, 2008). The phonological network consists of a giant component, lexical islands, and several hermits. The giant component represents the largest connected component of the network, whereas lexical islands constitute smaller groups of words that are connected to each other but not to the giant component. To determine if the size of the network component that a word resided in influenced lexical processing, three psycholinguistic tasks (word shadowing, lexical decision, and serial recall) were used to compare the processing of words from the giant component and word from lexical islands. Results showed that words from lexical islands were more quickly recognized and more accurately recalled than words from the giant component. These findings can be accounted for via a spreading activation framework. Implications for models of spoken word recognition and network science are also discussed

Community structure in the phonological network

A grant from the One-University Open Access Fund at the University of Kansas was used to defray the author’s publication fees in this Open Access journal. The Open Access Fund, administered by librarians from the KU, KU Law, and KUMC libraries, is made possible by contributions from the offices of KU Provost, KU Vice Chancellor for Research & Graduate Studies, and KUMC Vice Chancellor for Research. For more information about the Open Access Fund, please see http://library.kumc.edu/authors-fund.xml.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

Investigating the Influence of Inverse Preferential Attachment on Network Development

This work is licensed under a Creative Commons Attribution 4.0 International License.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.University of Kansa

THE PHONOGRAPHIC NETWORK OF LANGUAGE: 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 behavioral experimental paradigms, 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 within a phonographic multiplex. In this paper, I analyze a section of the phonographic multiplex known as the phonographic network of language, where links are placed between words that are both phonologically and orthographically similar to each other, i.e., a link would be placed between words such as ‘pant’ (/p@nt/) and ‘punt’ (/p^nt/). 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 two 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. The present findings have implications for theoretical models of spoken and visual word recognition, and for increasing our understanding of language learning and language disorders

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

How does the relation between two words create humor? In this paper, 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 crowdsourced (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, concreteness) were stronger predictors of word pair humor whereas form-related variables (number of letters, phonemes, 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

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

The influence of 2-hop network density on spoken word recognition

The final publication is available at Springer via http://dx.doi.org/10.3758/s13423-016-1103-9The influence of 2-hop density on spoken word recognition was investigated. 2-hop density measures the density of connections among the phonological neighbors (i.e., 1-hop neighbors) and phonological neighbors of those neighbors (i.e., 2-hop neighbors) of a target word. In both naming and lexical decision tasks, words with low 2-hop density were recognized more quickly than words with high 2-hop density. Because stimuli were selected such that the number of 1-hop and 2-hop neighbors were matched across both sets of words, the results suggest that spoken word recognition is influenced by the amount of connectivity among distant neighbors of the target word—a result that is not easily accommodated by current models of spoken word recognition. A diffusion of activation framework is proposed to account for the present finding

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