Language and Linguistics Compass最新文献

Second language (L2) aptitude has been broadly defined as the rate and ease of initially acquiring a second language. Historically, L2 aptitude has been understood as a stable trait that predetermined L2 achievement, regardless of individual learners’ efforts to acquire an L2. This traditional view of L2 aptitude as fixed and stable has led to it being a relatively neglected area of research within second language acquisition (SLA) studies. The little research that was in fact conducted was diagnostic in nature, and mostly used tests such as the Modern Language Aptitude Test (MLAT) to select potentially gifted L2 learners. Given that six decades have passed since the publication of the MLAT, now is a good time to revisit the literature and investigate whether L2 aptitude continues to be viewed as an individual difference of little interest to SLA research. While summative literature reviews of L2 aptitude research have been written, few systematic reviews exist. This article conducts a systematic quantitative literature review (SQLR) to provide a principled, comprehensive and reproducible synthesis of research into L2 aptitude published over the last 60 years (1959–2019). In this SQLR, close to one hundred journal articles and PhD dissertations were examined to discern generalisations and limitations in the field. This SQLR identifies a shift in the rationale for L2 aptitude testing, in which a diagnostic focus has been replaced by an explanatory perspective. Furthermore, our article points to a renewed interest in L2 aptitude research, which has come to be characterised by a more nuanced and sophisticated understanding of the concept and its components.

The starting point of this work is the notion of explanatory adequacy. After summarising and analysing the presentation that Chomsky gives of this notion in the first works of generative grammar, I consider the role that such notion has been playing in the construction of the theory of generative grammar and in the foundation of the Universal Grammar research program, focussing, in particular, on the last phase of generative grammar, that is, minimalism.

As online false information continues to grow, automated fact-checking has gained an increasing amount of attention in recent years. Researchers in the field of Natural Language Processing (NLP) have contributed to the task by building fact-checking datasets, devising automated fact-checking pipelines and proposing NLP methods to further research in the development of different components. This article reviews relevant research on automated fact-checking covering both the claim detection and claim validation components.

Recently, much psycholinguistic research has been devoted to examining cognitive mechanisms engaged in language processing in the bilingual context, as this might provide in-depth insights into how different languages interact with one another as well as how and to what extent language competence affects cognition. At the same time, along with such a growing interest in studying bilingual populations, psycholinguistics as a discipline has been rapidly developing owing to the more and more widespread use of physiological research methods. Particularly, electroencephalography (EEG) has received much scholarly attention, as it can elucidate the exact time course of language processing. The present contribution explores current trends in EEG research on bilingual language processing and discusses how event-related potential (ERP) studies and their findings have been extended from the monolingual to the bilingual context. To this end, the paper discusses selected ERP experiments into language co-activation across different modalities, bilingual executive control, electrophysiological correlates of new meaning construction, neural changes accompanying foreign language learning, and the automaticity of emotional responding in bilingual speakers.

Recently, there has been an increased interest in demographically grounded bias in natural language processing (NLP) applications. Much of the recent work has focused on describing bias and providing an overview of bias in a larger context. Here, we provide a simple, actionable summary of this recent work. We outline five sources where bias can occur in NLP systems: (1) the data, (2) the annotation process, (3) the input representations, (4) the models, and finally (5) the research design (or how we conceptualize our research). We explore each of the bias sources in detail in this article, including examples and links to related work, as well as potential counter-measures.

Research in discourse processing has provided us with a strong foundation for understanding the characteristics of text and discourse, as well as their influence on our processing and representation of texts. However, recent advances in computational techniques have allowed researchers to examine discourse processes in new ways. The purpose of the current paper is to build on prior work in this domain and describe how new methodologies that consider the multi-dimensional nature of texts can serve as a complement to the existing literature. We focus on natural language processing (NLP) methodologies, in which computers calculate information about the linguistic and semantic properties of language data. We first provide a context for the origins of computational discourse analysis through the integration of research across computer science and psychology. We then provide an overview of different NLP methodologies and describe prior work that has leveraged these techniques to advance theoretical perspectives of discourse comprehension and production. Finally, we propose new areas of research that integrate these advances with traditional research methodologies in the field.

When the COVID-19 pandemic halted in-person data collection, many linguists adopted modern technologies to replace traditional methods, including speaker-led options in which participants record themselves using their own personal computers or smartphones and then email or upload the sound files to online storage sites for researchers to retrieve later. This study evaluated the suitability of such ‘home-made’ recordings for phonetic analysis of vowel space configurations, mergers, and nasalization by comparing simultaneous recordings from several popular personal devices (Macbook, PC laptop, iPad, iPhone and Android smartphone) to those taken from professional equipment (H4n field recorder, Focusrite with Audio Technica 2021 microphone). All personal devices conveyed vowel arrangements and nasalization patterns relatively faithfully (especially laptops), but absolute measurements varied, particularly for the female speaker and in the 750–1500 Hz range, which affected the locations (F1 × F2) of low and back vowels and reduced nasalization measurements (A1−P0) for the female's pre-nasal vowels. Based on these results, we assess the validity of remote recording using these consumer devices and offer recommendations for best practices for collecting high fidelity acoustic phonetic data from a distance.

Most readers have had the experience of initially failing to notice an omission or repetition of a function word, or a transposition of two adjacent words. In the present article, we review recent research investigating this phenomenon. We emphasize that failure to notice such errors is of substantial theoretical interest, given what we have learned about how systematically and incrementally readers inspect and process text. We endorse the idea that a process of rational inference may play a critical role, while we cast doubt on the idea that failure to notice errors arises from parallel processing of multiple words. We review a number of recent studies from our own laboratory that have investigated the relationship between eye movements during reading and noticing, or failing to notice, an error. While the conclusions from these studies are broadly consistent with a rational inference account, we find that when readers fail to notice an error, their eye movements generally show no indication that the error was registered at all. On its surface, this finding may be viewed as inconsistent with the idea that the rational inference process that enables readers to overlook errors is genuinely post-perceptual. We suggest a mechanism by which eye movement control models could account for this finding.