Search

Longstanding cross-linguistic work on event representations in spoken languages have argued for a robust mapping between an event's underlying representation and its syntactic encoding, such that-for example-the agent of an event is most frequently mapped to subject position. In the same vein, sign languages have long been claimed to construct signs that visually represent their meaning, i.e., signs that are iconic. Experimental research on linguistic parameters such as plurality and aspect has recently shown some of them to be visually universal in sign, i.e. recognized by non-signers as well as signers, and have identified specific visual cues that achieve this mapping. However, little is known about what makes action representations in sign language iconic, or whether and how the mapping of underlying event representations to syntactic encoding is visually apparent in the form of a verb sign. To this end, we asked what visual cues non-signers may use in evaluating transitivity (i.e., the number of entities involved in an action). To do this, we correlated non-signer judgments about transitivity of verb signs from American Sign Language (ASL) with phonological characteristics of these signs. We found that non-signers did not accurately guess the transitivity of the signs, but that non-signer transitivity judgments can nevertheless be predicted from the signs' visual characteristics. Further, non-signers cue in on just those features that code event representations across sign languages, despite interpreting them differently. This suggests the existence of visual biases that underlie detection of linguistic categories, such as transitivity, which may uncouple from underlying conceptual representations over time in mature sign languages due to lexicalization processes., Competing Interests: The authors have declared that no competing interests exist.

The surge of post-truth political argumentation suggests that we are living in a special historical period when it comes to the balance between emotion and reasoning. To explore if this is indeed the case, we analyze language in millions of books covering the period from 1850 to 2019 represented in Google nGram data. We show that the use of words associated with rationality, such as "determine" and "conclusion," rose systematically after 1850, while words related to human experience such as "feel" and "believe" declined. This pattern reversed over the past decades, paralleled by a shift from a collectivistic to an individualistic focus as reflected, among other things, by the ratio of singular to plural pronouns such as "I"/"we" and "he"/"they." Interpreting this synchronous sea change in book language remains challenging. However, as we show, the nature of this reversal occurs in fiction as well as nonfiction. Moreover, the pattern of change in the ratio between sentiment and rationality flag words since 1850 also occurs in New York Times articles, suggesting that it is not an artifact of the book corpora we analyzed. Finally, we show that word trends in books parallel trends in corresponding Google search terms, supporting the idea that changes in book language do in part reflect changes in interest. All in all, our results suggest that over the past decades, there has been a marked shift in public interest from the collective to the individual, and from rationality toward emotion., Competing Interests: The authors declare no competing interest., (Copyright © 2021 the Author(s). Published by PNAS.)

Measuring the semantic similarity between words is important for natural language processing tasks. The traditional models of semantic similarity perform well in most cases, but when dealing with words that involve geographical context, spatial semantics of implied spatial information are rarely preserved. Geographic information retrieval (GIR) methods have focused on this issue; however, they sometimes fail to solve the problem because the spatial and textual similarities of words are considered and calculated separately. In this paper, from the perspective of spatial context, we consider the two parts as a whole-spatial context semantics, and we propose a method that measures spatial semantic similarity using a sliding geospatial context window for geo-tagged words. The proposed method was first validated with a set of simulated data and then applied to a real-world dataset from Flickr. As a result, a spatial semantic similarity model at different scales is presented. We believe this model is a necessary supplement for traditional textual-language semantic analyses of words obtained by word-embedding technologies. This study has the potential to improve the quality of recommendation systems by considering relevant spatial context semantics, and benefits linguistic semantic research by emphasising the spatial cognition among words., Competing Interests: The authors have declared that no competing interests exist.

Stretched words like 'heellllp' or 'heyyyyy' are a regular feature of spoken language, often used to emphasize or exaggerate the underlying meaning of the root word. While stretched words are rarely found in formal written language and dictionaries, they are prevalent within social media. In this paper, we examine the frequency distributions of 'stretchable words' found in roughly 100 billion tweets authored over an 8 year period. We introduce two central parameters, 'balance' and 'stretch', that capture their main characteristics, and explore their dynamics by creating visual tools we call 'balance plots' and 'spelling trees'. We discuss how the tools and methods we develop here could be used to study the statistical patterns of mistypings and misspellings and be used as a basis for other linguistic research involving stretchable words, along with the potential applications in augmenting dictionaries, improving language processing, and in any area where sequence construction matters, such as genetics., Competing Interests: We have the following interests: TJG, CMD, and PSD were supported by a gift from MassMutual. There are no patents, products in development, or marketed products to declare. This does not alter our adherence to all of the PLOS ONE policies on sharing data and materials.

The novel finding of Balakrishnan, Miller & Shankar (2008) that investors, overwhelmed by the plethora of stock investment offerings, limit their analysis and daily choices to only a small subset of stocks (i.e., herding behavior) now seems to be common wisdom (Iosebashvili, 2019). We investigate whether the introduction of an innovation in financial products designed to allow investors to trade the entire product bundle of S&P 500 stocks, namely S&P 500 index funds, altered "herding behavior" by creating a new class of index investors. We model the distribution of daily trading concentration as a power law function and examine changes over the last six decades. Intriguingly, we discover a unique pattern in the trading concentration distribution that exhibits two distinct trends. For the period 1960-75, the trading concentration of the S&P 500 stocks tracks the increasing trend for the entire market, i.e., the unevenness in trading has steadily increased. However, after the introduction of S&P 500 index funds in 1975, concentration of trading in the S&P 500 stocks has steadily decreased, i.e., trading distribution has become more even across all 500 stocks, contrary to the current belief of equity analysts. This is also in sharp contrast to the case of U.S. stocks that are not in the S&P 500 index where trading concentration has steadily increased. We further corroborate the uniqueness of the inverted V-shape by a counterfactual investigation of the trading concentration patterns for other sets of 500 stock portfolios. This uniquely distinctive trading concentration pattern for S&P 500 stocks appears to be driven by the increasing dominance of bundle trading by index investors., Competing Interests: The authors have declared that no competing interests exist.

Recently, prominent theoretical linguists have argued for an explicit scenario for the evolution of the human language capacity on the basis of its computational properties. Concretely, the simplicity of a minimalist formulation of the operation Merge, which allows humans to recursively compute hierarchical relations in language, has been used to promote a sudden-emergence, single-mutation scenario. In support of this view, Merge is said to be either fully present or fully absent: one cannot have half-Merge. On this basis, it is inferred that the emergence of our fully fledged language capacity had to be sudden. Thus, proponents of this view draw a parallelism between the formal complexity of the operation at the computational level and the number of evolutionary steps it must imply. Here, we examine this argument in detail and show that the jump from the atomicity of Merge to a single-mutation scenario is not valid and therefore cannot be used as justification for a theory of language evolution along those lines., Competing Interests: The authors have declared that no competing interests exist.

Hearing in noisy environments is a complicated task that engages attention, memory, linguistic knowledge, and precise auditory-neurophysiological processing of sound. Accumulating evidence in school-aged children and adults suggests these mechanisms vary with the task's demands. For instance, co-located speech and noise demands a large cognitive load and recruits working memory, while spatially separating speech and noise diminishes this load and draws on alternative skills. Past research has focused on one or two mechanisms underlying speech-in-noise perception in isolation; few studies have considered multiple factors in tandem, or how they interact during critical developmental years. This project sought to test complementary hypotheses involving neurophysiological, cognitive, and linguistic processes supporting speech-in-noise perception in young children under different masking conditions (co-located, spatially separated). Structural equation modeling was used to identify latent constructs and examine their contributions as predictors. Results reveal cognitive and language skills operate as a single factor supporting speech-in-noise perception under different masking conditions. While neural coding of the F0 supports perception in both co-located and spatially separated conditions, neural timing predicts perception of spatially separated listening exclusively. Together, these results suggest co-located and spatially separated speech-in-noise perception draw on similar cognitive/linguistic skills, but distinct neural factors, in early childhood., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Associating human genetic makeup with the faculty of language has long been a goal for biolinguistics. This stimulated the idea that language is attributed to genes and language disabilities are caused by genetic mutations. However, application of genetic knowledge on language intervention is still a gap in the existing literature. In an effort to bridge this gap, this article presents an account of genetic and neural associations of language and synthesizes the genetic, neural, epigenetic and environmental facets involved in language. In addition to describing the association of genes with language, the neural and epigenetic aspects of language are also explored. Further, the environmental aspects of language such as language input, emotion and cognition are also traced back to gene expressions. Therefore, effective language intervention for language learning difficulties must offer genetics-informed solutions, both linguistic and medical.

It is tempting to treat frequency trends from the Google Books data sets as indicators of the "true" popularity of various words and phrases. Doing so allows us to draw quantitatively strong conclusions about the evolution of cultural perception of a given topic, such as time or gender. However, the Google Books corpus suffers from a number of limitations which make it an obscure mask of cultural popularity. A primary issue is that the corpus is in effect a library, containing one of each book. A single, prolific author is thereby able to noticeably insert new phrases into the Google Books lexicon, whether the author is widely read or not. With this understood, the Google Books corpus remains an important data set to be considered more lexicon-like than text-like. Here, we show that a distinct problematic feature arises from the inclusion of scientific texts, which have become an increasingly substantive portion of the corpus throughout the 1900 s. The result is a surge of phrases typical to academic articles but less common in general, such as references to time in the form of citations. We use information theoretic methods to highlight these dynamics by examining and comparing major contributions via a divergence measure of English data sets between decades in the period 1800-2000. We find that only the English Fiction data set from the second version of the corpus is not heavily affected by professional texts. Overall, our findings call into question the vast majority of existing claims drawn from the Google Books corpus, and point to the need to fully characterize the dynamics of the corpus before using these data sets to draw broad conclusions about cultural and linguistic evolution.

Our understanding of the cognitive and neural underpinnings of language has traditionally been firmly based on spoken Indo-European languages and on language studied as speech or text. However, in face-to-face communication, language is multimodal: speech signals are invariably accompanied by visual information on the face and in manual gestures, and sign languages deploy multiple channels (hands, face and body) in utterance construction. Moreover, the narrow focus on spoken Indo-European languages has entrenched the assumption that language is comprised wholly by an arbitrary system of symbols and rules. However, iconicity (i.e. resemblance between aspects of communicative form and meaning) is also present: speakers use iconic gestures when they speak; many non-Indo-European spoken languages exhibit a substantial amount of iconicity in word forms and, finally, iconicity is the norm, rather than the exception in sign languages. This introduction provides the motivation for taking a multimodal approach to the study of language learning, processing and evolution, and discusses the broad implications of shifting our current dominant approaches and assumptions to encompass multimodal expression in both signed and spoken languages., (© 2014 The Author(s) Published by the Royal Society. All rights reserved.)

Global patterns of ethnolinguistic diversity vary tremendously. Some regions show very little variation even across vast expanses, whereas others exhibit dense mosaics of different languages spoken alongside one another. Compared with the rest of Native North America, prehistoric California exemplified the latter. Decades of linguistic, genetic, and archaeological research have produced detailed accounts of the migrations that aggregated to build California's diverse ethnolinguistic mosaic, but there have been few have attempts to explain the process underpinning these migrations and why such a mosaic did not develop elsewhere. Here we show that environmental productivity predicts both the order of migration events and the population density recorded at contact. The earliest colonizers occupied the most suitable habitats along the coast, whereas subsequent Mid-Late Holocene migrants settled in more marginal habitats. Other Late Holocene patterns diverge from this trend, reflecting altered dynamics linked to food storage and increased sedentism. Through repeated migration events, incoming populations replaced resident populations occurring at lower densities in lower-productivity habitats, thereby resulting in the fragmentation of earlier groups and the development of one of the most diverse ethnolinguistic patterns in the Americas. Such a process may account for the distribution of ethnolinguistic diversity worldwide.

We explored variation in the linguistic environments of hearing children of Deaf parents and how it was associated with their early bilingual language development. For that purpose we followed up the children's productive vocabulary (measured with the MCDI; MacArthur Communicative Development Inventory) and syntactic complexity (measured with the MLU10; mean length of the 10 longest utterances the child produced during videorecorded play sessions) in both Finnish Sign Language and spoken Finnish between the ages of 12 and 30 months. Additionally, we developed new methodology for describing the linguistic environments of the children (N = 10). Large variation was uncovered in both the amount and type of language input and language acquisition among the children. Language exposure and increases in productive vocabulary and syntactic complexity were interconnected. Language acquisition was found to be more dependent on the amount of exposure in sign language than in spoken language. This was judged to be related to the status of sign language as a minority language. The results are discussed in terms of parents' language choices, family dynamics in Deaf-parented families and optimal conditions for bilingual development.

It is generally assumed that hierarchical phrase structure plays a central role in human language. However, considerations of simplicity and evolutionary continuity suggest that hierarchical structure should not be invoked too hastily. Indeed, recent neurophysiological, behavioural and computational studies show that sequential sentence structure has considerable explanatory power and that hierarchical processing is often not involved. In this paper, we review evidence from the recent literature supporting the hypothesis that sequential structure may be fundamental to the comprehension, production and acquisition of human language. Moreover, we provide a preliminary sketch outlining a non-hierarchical model of language use and discuss its implications and testable predictions. If linguistic phenomena can be explained by sequential rather than hierarchical structure, this will have considerable impact in a wide range of fields, such as linguistics, ethology, cognitive neuroscience, psychology and computer science.

As the world grows less biologically diverse, it is becoming less linguistically and culturally diverse as well. Biologists estimate annual loss of species at 1,000 times or more greater than historic rates, and linguists predict that 50-90% of the world's languages will disappear by the end of this century. Prior studies indicate similarities in the geographic arrangement of biological and linguistic diversity, although conclusions have often been constrained by use of data with limited spatial precision. Here we use greatly improved datasets to explore the co-occurrence of linguistic and biological diversity in regions containing many of the Earth's remaining species: biodiversity hotspots and high biodiversity wilderness areas. Results indicate that these regions often contain considerable linguistic diversity, accounting for 70% of all languages on Earth. Moreover, the languages involved are frequently unique (endemic) to particular regions, with many facing extinction. Likely reasons for co-occurrence of linguistic and biological diversity are complex and appear to vary among localities, although strong geographic concordance between biological and linguistic diversity in many areas argues for some form of functional connection. Languages in high biodiversity regions also often co-occur with one or more specific conservation priorities, here defined as endangered species and protected areas, marking particular localities important for maintaining both forms of diversity. The results reported in this article provide a starting point for focused research exploring the relationship between biological and linguistic-cultural diversity, and for developing integrated strategies designed to conserve species and languages in regions rich in both.

The paper is devoted to the peculiarities of medical discourse in pathological anatomy as coherent speech and as a linguistic correlate of medical practice taking into account the analysis of its strategies and tactics. The purpose of the paper is to provide a multifaceted analysis of the speech strategies and tactics of pathological anatomy discourse and ways of their implementation. The main strategies of medical discourse in pathological anatomy are an anticipating strategy, a diagnosing strategy and an explaining one. The supporting strategies are pragmatic, conversational and a rhetorical one. The pragmatic strategy is implemented through contact establishing tactics, the conversational one - with the help of control tactics, the rhetorical one - with the help of attention correction tactics. The above mentioned tactics and strategies are used in the distinguishing of major, closely interrelated strategies: "the contact strategy" (to establish contact with a patient's relatives - phatic replicas of greeting and addressing) and "the strategy of explanation" (used in the practice of a pathologist for a detailed explanation of the reasons of a patient's death). The ethic aspect of speech conduct of a doctor-pathologist is analyzed.

Language is the best example of a cultural evolutionary system, able to retain a phylogenetic signal over many thousands of years. The temporal stability (conservatism) of basic vocabulary is relatively well understood, but the stability of the structural properties of language (phonology, morphology, syntax) is still unclear. Here we report an extensive Bayesian phylogenetic investigation of the structural stability of numerous features across many language families and we introduce a novel method for analyzing the relationships between the "stability profiles" of language families. We found that there is a strong universal component across language families, suggesting the existence of universal linguistic, cognitive and genetic constraints. Against this background, however, each language family has a distinct stability profile, and these profiles cluster by geographic area and likely deep genealogical relationships. These stability profiles seem to show, for example, the ancient historical relationships between the Siberian and American language families, presumed to be separated by at least 12,000 years, and possible connections between the Eurasian families. We also found preliminary support for the punctuated evolution of structural features of language across families, types of features and geographic areas. Thus, such higher-level properties of language seen as an evolutionary system might allow the investigation of ancient connections between languages and shed light on the peopling of the world.

Human languages evolve continuously, and a puzzling problem is how to reconcile the apparent robustness of most of the deep linguistic structures we use with the evidence that they undergo possibly slow, yet ceaseless, changes. Is the state in which we observe languages today closer to what would be a dynamical attractor with statistically stationary properties or rather closer to a non-steady state slowly evolving in time? Here we address this question in the framework of the emergence of shared linguistic categories in a population of individuals interacting through language games. The observed emerging asymptotic categorization, which has been previously tested--with success--against experimental data from human languages, corresponds to a metastable state where global shifts are always possible but progressively more unlikely and the response properties depend on the age of the system. This aging mechanism exhibits striking quantitative analogies to what is observed in the statistical mechanics of glassy systems. We argue that this can be a general scenario in language dynamics where shared linguistic conventions would not emerge as attractors, but rather as metastable states.

Natural language processing is the study of computer programs that can understand and produce human language. An important goal in the research to produce such technology is identifying the right meaning of words and phrases. In this paper, we give an overview of current research in three areas: (i) inducing word meaning; (ii) distinguishing different meanings of words used in context; and (iii) determining when the meaning of a phrase cannot straightforwardly be obtained from its parts. Manual construction of resources is labour intensive and costly and furthermore may not reflect the meanings that are useful for the task or data at hand. For this reason, we focus particularly on systems that use samples of language data to learn about meanings, rather than examples annotated by humans.

We constructed a connectionist model with multilayer perceptron networks for the simulation of word production errors in the Finnish language. Such errors occur as slips of the tongue in healthy subjects and even more often in aphasic patients suffering from brain damage. Word production errors are of theoretical interest as they open an avenue to inner workings of the language production system. Here we present our coding schemes for semantic and phoneme information of words, investigate the general properties of the model, and compare the results of the model against empirical naming error data from ten aphasic patients. The model was reasonable in simulating word production errors in this heterogeneous group of aphasic patients.

The world's language system is undergoing rapid change because of demographic trends, new technology, and international communication. These changes will affect both written and spoken communication. English may not be the dominant language of the future, and the need to be multilingual will be enhanced. Although many languages are going extinct, new ones are emerging in cities and extended social groups.

The field of natural language processing (NLP) has seen a dramatic shift in both research direction and methodology in the past several years. In the past, most work in computational linguistics tended to focus on purely symbolic methods. Recently, more and more work is shifting toward hybrid methods that combine new empirical corpus-based methods, including the use of probabilistic and information-theoretic techniques, with traditional symbolic methods. This work is made possible by the recent availability of linguistic databases that add rich linguistic annotation to corpora of natural language text. Already, these methods have led to a dramatic improvement in the performance of a variety of NLP systems with similar improvement likely in the coming years. This paper focuses on these trends, surveying in particular three areas of recent progress: part-of-speech tagging, stochastic parsing, and lexical semantics.