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This study investigated the strategies of expression of referents in Kiitharaka in information structure are found in expression of referents. The data for this research was gathered from narratives collected through story telling sessions organized during the field study. The study finds that although accessibility status of the referent is the main factor determining the choice of referring expressions, there are other factors that come into play, further influencing the amount of linguistic material a speaker uses. These factors include referential properties of the referent, the predicate type, presence of competing referents and the saliency of the referent, among others.

It is widely accepted that infants begin learning their native language not by learning words, but by discovering features of the speech signal: consonants, vowels, and combinations of these sounds. Learning to understand words, as opposed to just perceiving their sounds, is said to come later, between 9 and 15 mo of age, when infants develop a capacity for interpreting others’ goals and intentions. Here, we demonstrate that this consensus about the developmental sequence of human language learning is flawed: in fact, infants already know the meanings of several common words from the age of 6 mo onward. We presented 6- to 9-mo-old infants with sets of pictures to view while their parent named a picture in each set. Over this entire age range, infants directed their gaze to the named pictures, indicating their understanding of spoken words. Because the words were not trained in the laboratory, the results show that even young infants learn ordinary words through daily experience with language. This surprising accomplishment indicates that, contrary to prevailing beliefs, either infants can already grasp the referential intentions of adults at 6 mo or infants can learn words before this ability emerges. The precocious discovery of word meanings suggests a perspective in which learning vocabulary and learning the sound structure of spoken language go hand in hand as language acquisition begins.

Humans have a fundamental need to experience a shared reality with others. We present a new conceptualization of shared reality based on four conditions. We posit (a) that shared reality involves a (subjectively perceived) commonality of individuals ' inner states (not just observable behaviors); (b) that shared reality is about some target referent; (c) that for a shared reality to occur, the commonality of inner states must be appropriately motivated; and (d) that shared reality involves the experience of a successful connection to other people's inner states. In reviewing relevant evidence, we emphasize research on the saying-is-believing effect, which illustrates the creation of shared reality in interpersonal communication. We discuss why shared reality provides a better explanation of the findings from saying-is-believing studies than do other formulations. Finally, we examine relations between our conceptualization of shared reality and related constructs (including empathy, perspective taking, theory of mind, common ground, embodied synchrony, and socially distributed knowledge) and indicate how our approach may promote a comprehensive and differentiated understanding of social-sharing phenomena.

This paper proposes a semantics of anaphora in attitude contexts within the framework of Discourse Representation Theory (DRT). The paper first focuses on intentional identity, a special kind of cross-attitudinal anaphora. Based on the DRT semantics of attitude reports summarized by Kamp et al. (in: D. Gabbay and F. Guenthner (Eds.), Handbook of philosophical logic, 2011), the author proposes a semantics of intentional identity that implements the following two ideas: (1) indefinites and pronouns appearing in attitude contexts introduce metadiscourse referents, which represent one’s mental files and record appearances of discourse referents in attitude contexts; and (2) what underlies the relevant kind of anaphoric links between indefinites and pronouns across attitude contexts is the coordination relation between mental files, which is represented by using metadiscourse referents. Next, the paper expands the semantics to cover de re anaphora, in which an anaphoric pronoun in an attitude context takes as its antecedent an expression appearing outside any attitude context.

I review Ninan’s Hundred Tickets case pertaining to quantification into epistemic modal contexts, and his counterpart theoretic way to address it (Ninan, Philos Rev, 2018. https://doi.org/10.1215/00318108-6973010). Ninan’s solution employs a ‘counterpart relation’ parameter intended to reflect how the domain of quantification is thought of in a context. This approach theoretically rules out the possibility of contexts where different ways of thinking about the domain can be deployed through different quantificational noun phrases. I bring out the case of the multiply de re modal statement Any ticket in photo #2 might be any ticket in photo #1 to challenge Ninan’s approach. I propose a different approach adapting a more complex ‘counterpart relation’ parameter due to Rabern (Inquiry, 2021. https://doi.org/10.1080/0020174X.2018.1470568). I attempt to flesh it out by relating it to a finer grained notion of epistemic possibility involving assignments to discourse referents. My approach can account for the aforementioned multiply de re statement, as well as address the Hundred Tickets case.

Neoliberalism has been a popular concept within anthropological scholarship over the past decade; this very popularity has also elicited a fair share of criticism. This review examines current anthropological engagements with neoliberalism and explains why the concept has been so attractive for anthropologists since the millennium. It briefly outlines the history of neoliberal thought and explains how neoliberalism is different from late capitalism. Although neoliberalism is a polysemic concept with multiple referents, anthropologists have most commonly understood neoliberalism in two main ways: as a structural force that affects people's life-chances and as an ideology of governance that shapes subjectivities. Neoliberalism frequently functions as an index of the global political-economic order and allows for a vast array of ethnographic sites and topics to be contained within the same frame. However, as an analytical framework, neoliberalism can also obscure ethnographic particularities and foreclose certain avenues of inquiry.

We use an experiment across the Arab Twittersphere and a nationally representative survey experiment in Lebanon to evaluate what types of counter-speech interventions are most effective in reducing sectarian hate speech online. We explore whether and to what extent messages priming common national identity or common religious identity, with and without elite endorsements, decrease the use of hostile anti-outgroup language. We find that elite-endorsed messages that prime common religious identity are the most consistently effective in reducing the spread of sectarian hate speech. Our results provide suggestive evidence that religious elites may play an important role as social referents—alerting individuals to social norms of acceptable behavior. By randomly assigning counter-speech treatments to actual producers of online hate speech and experimentally evaluating the effectiveness of these messages on a representative sample of citizens that might be incidentally exposed to such language, this work offers insights for researchers and policymakers on avenues for combating harmful rhetoric on and offline.

Artificial intelligence chatbots such as ChatGPT (OpenAI) have garnered excitement about their potential for delegating writing tasks ordinarily performed by humans. Many of these tasks (eg, writing recommendation letters) have social and professional ramifications, making the potential social biases in ChatGPT's underlying language model a serious concern. Three preregistered studies used the text analysis program Linguistic Inquiry and Word Count to investigate gender bias in recommendation letters written by ChatGPT in human-use sessions (N=1400 total letters). We conducted analyses using 22 existing Linguistic Inquiry and Word Count dictionaries, as well as 6 newly created dictionaries based on systematic reviews of gender bias in recommendation letters, to compare recommendation letters generated for the 200 most historically popular \"male\" and \"female\" names in the United States. Study 1 used 3 different letter-writing prompts intended to accentuate professional accomplishments associated with male stereotypes, female stereotypes, or neither. Study 2 examined whether lengthening each of the 3 prompts while holding the between-prompt word count constant modified the extent of bias. Study 3 examined the variability within letters generated for the same name and prompts. We hypothesized that when prompted with gender-stereotyped professional accomplishments, ChatGPT would evidence gender-based language differences replicating those found in systematic reviews of human-written recommendation letters (eg, more affiliative, social, and communal language for female names; more agentic and skill-based language for male names). Significant differences in language between letters generated for female versus male names were observed across all prompts, including the prompt hypothesized to be neutral, and across nearly all language categories tested. Historically female names received significantly more social referents (5/6, 83% of prompts), communal or doubt-raising language (4/6, 67% of prompts), personal pronouns (4/6, 67% of prompts), and clout language (5/6, 83% of prompts). Contradicting the study hypotheses, some gender differences (eg, achievement language and agentic language) were significant in both the hypothesized and nonhypothesized directions, depending on the prompt. Heteroscedasticity between male and female names was observed in multiple linguistic categories, with greater variance for historically female names than for historically male names. ChatGPT reproduces many gender-based language biases that have been reliably identified in investigations of human-written reference letters, although these differences vary across prompts and language categories. Caution should be taken when using ChatGPT for tasks that have social consequences, such as reference letter writing. The methods developed in this study may be useful for ongoing bias testing among progressive generations of chatbots across a range of real-world scenarios. OSF Registries osf.io/ztv96; https://osf.io/ztv96.

Different languages rely on distinct sets of terminology to classify relatives, such as maternal grandfather in English, and precision in language usage is a key component for successful communication (see the Perspective by Levinson ). Kemp and Regier (p. 1049 ) propose an organizing framework whereby kinship classification systems can all be seen to optimize or nearly optimize both simplicity and precision. The labels applied to kin are constructed from simple units and are precise enough to reduce confusion and ambiguity when used in communication. Frank and Goodman (p. 998 ) show that simplicity and precision also explain how listeners correctly infer the meaning of speech in the context of referential communication. A Bayesian inference model predicts how listeners decode communications. One of the most astonishing features of human language is its capacity to convey information efficiently in context. Many theories provide informal accounts of communicative inference, yet there have been few successes in making precise, quantitative predictions about pragmatic reasoning. We examined judgments about simple referential communication games, modeling behavior in these games by assuming that speakers attempt to be informative and that listeners use Bayesian inference to recover speakers’ intended referents. Our model provides a close, parameter-free fit to human judgments, suggesting that the use of information-theoretic tools to predict pragmatic reasoning may lead to more effective formal models of communication.

This paper explores the idea that scalar implicatures are computed with respect to discourse referents. Given the general consensus that a proper account of pronominal anaphora in natural language requires discourse referents separately from the truth-conditional meaning, it is naturally expected that the anaphoric information that discourse referents carry play a role in the computation of scalar implicatures, but the literature has so far mostly exclusively focused on the truth-conditional dimension of meaning. This paper offers a formal theory of scalar implicatures with discourse referents couched in dynamic semantics, and demonstrates its usefulness through a case study on the plurality inferences of plural nouns in English.