Investigating the Influence of Inverse Preferential Attachment on Network Development

Obsolescence effects in second language phonological networks

Phonological networks are representations of word forms and their phonological relationships with other words in a given language lexicon. A principle underlying the growth (or evolution) of those networks is preferential attachment, or the "rich-gets-richer" mechanisms, according to which words with many phonological neighbors (or links) are the main beneficiaries of future growth opportunities. Due to their limited number of words, language lexica constitute node-constrained networks where growth cannot keep increasing in a linear way; hence, preferential attachment is likely mitigated by certain factors. The present study investigated obsolescence effects (i.e., a word's finite timespan of being active in terms of growth) in an evolving phonological network of English as a second language. It was found that phonological neighborhoods are constructed by one large initial lexical spurt, followed by sublinear growth spurts that eventually lead to very limited growth in later lexical spurts during network evolution. First-language-given neighborhood densities are rarely reached even by the most advanced language learners. An analysis of the strength of phonological relationships between phonological word forms revealed a tendency to incorporate phonetically more distant phonological neighbors at earlier acquisition stages. Overall, the findings suggest an obsolescence effect in growth that favors younger words. Implications for the second-language lexicon include leveraged learning mechanisms and learning bouts focused on a smaller range of phonological segments, and involve questions concerning lexical processing in aging networks.

Using Complex Networks in the Hearing Sciences

In this Point of View, we review a number of recent discoveries from the emerging, interdisciplinary field of Network Science , which uses graph theoretic techniques to understand complex systems. In the network science approach, nodes represent entities in a system, and connections are placed between nodes that are related to each other to form a web-like network . We discuss several studies that demonstrate how the micro-, meso-, and macro-level structure of a network of phonological word-forms influence spoken word recognition in listeners with normal hearing and in listeners with hearing loss. Given the discoveries made possible by this new approach and the influence of several complex network measures on spoken word recognition performance we argue that speech recognition measures-originally developed in the late 1940s and routinely used in clinical audiometry-should be revised to reflect our current understanding of spoken word recognition. We also discuss other ways in which the tools of network science can be used in Speech and Hearing Sciences and Audiology more broadly.

The Resilience of the Phonological Network May Have Implications for Developmental and Acquired Disorders

A central tenet of network science states that the structure of the network influences processing. In this study of a phonological network of English words we asked: how does damage alter the network structure (Study 1)? How does the damaged structure influence lexical processing (Study 2)? How does the structure of the intact network "protect" processing with a less efficient algorithm (Study 3)? In Study 1, connections in the network were randomly removed to increasingly damage the network. Various measures showed the network remained well-connected (i.e., it is resilient to damage) until ~90% of the connections were removed. In Study 2, computer simulations examined the retrieval of a set of words. The performance of the model was positively correlated with naming accuracy by people with aphasia (PWA) on the Philadelphia Naming Test (PNT) across four types of aphasia. In Study 3, we demonstrated another way to model developmental or acquired disorders by manipulating how efficiently activation spread through the network. We found that the structure of the network "protects" word retrieval despite decreases in processing efficiency; words that are relatively easy to retrieve with efficient transmission of priming remain relatively easy to retrieve with less efficient transmission of priming. Cognitive network science and computer simulations may provide insight to a wide range of speech, language, hearing, and cognitive disorders.

Spatio-temporal evaluation of the urban agglomeration expansion in the middle reaches of the Yangtze River and its impact on ecological lands

In developing countries, urban agglomerations have become one of the most important carriers of urbanization. In China, one of the largest national urban agglomerations is the UAMRYR (urban agglomeration in the middle reaches of the Yangtze River), which has the fastest urban development in Central China and is home to critical ecological spaces. On this basis, this study analyzed the urban expansion and loss of ecological lands in UAMRYR by establishing a spatial database, grid system, rose maps, and scenario simulation. The highlights of results are as follows: the urban expansion of UAMRYR includes four stages of development: steady growth, accelerated development, peak, and deceleration. Polarized development has occurred in the region, with the Wuhan, Changsha-Zhuzhou-Xiangtan, and Nanchang metropolitan areas having high levels of urban expansion while the other cities have varying levels of urban growth. The loss of ecological land was found to have a highly similar curve and spatial distribution with the urban growth. In terms of ecological land types, the loss of agricultural land accounted for more than half of ecological lands lost overall. Losses in forests, grasslands, wetlands, and water areas varied for different areas, depending on particular conditions and specific environmental characteristics of the city. The three metropolitan areas experienced the highest increases in urban built-up areas; they also had the highest loss of ecological land, which can have critical consequences given their proximity to the Yangtze River, Poyang Lake, Dongting Lake, and other important environmental sensitive areas. The simulation results show that the UAMRYR is projected to maintain a rapid expansion rate from 2020 to 2030. If the urban expansion, particularly in the metropolitan areas, is not managed well and controlled, serious ecological damage could be inflicted on the Yangtze River Basin.

What Can Network Science Tell Us About Phonology and Language Processing?

Contemporary psycholinguistic models place significant emphasis on the cognitive processes involved in the acquisition, recognition, and production of language but neglect many issues related to the representation of language‐related information in the mental lexicon. In contrast, a central tenet of network science is that the structure of a network influences the processes that operate in that system, making process and representation inextricably connected. Here, we consider how the structure found across phonological networks of several languages from different language families may influence language processing as we age and experience diseases that affect cognition during the typical and atypical acquisition of new words, during typical perception and production of speech in adults, and during language change over time. We conclude that the network science approach may not only provide insights into specific language processes but also provide a way to connect the work from these domains, which are becoming increasingly balkanized.