Linguistic Cues Research Articles

Linguistic structure modulates attention in reading: Evidence from negative concord in Italian

ABSTRACT We report the reading performance of an Italian speaker with egocentric Neglect Dyslexia on sentences with Negative Concord structures, which contain a linguistic cue to the presence of a preceding negative marker and compare it to sentences with no such cue. As predicted, the frequency of reading the whole sentence, including the initial negative marker non, was higher in Negative Concord structures than in sentences which also started with non, but crucially, lacked the medially positioned linguistic cue to the presence of non. These data support the claim that the presence of linguistic cues to sentence structure modulates attention during reading in Neglect Dyslexia.

Linguistic and non-linguistic cues in motion event endpoint description: The selection between English to and towards

The current study aims to explore the factors that could affect people’s description of a motion event endpoint. The study conducted by Liao, Dijkstra, and Zwaan (2021, Language and Cognition, 13[2], 161–190) found that two non-linguistic factors (i.e., the actor’s goal and the interlocutor’s social status) affect people’s choice between two Dutch directional prepositions (i.e., naar and richting) during event description tasks. The current study aims to extend these findings by examining the choice between a similar pair of directional prepositions in English (i.e., to and towards). Moreover, we aim to study whether grammatical aspect (i.e., the English simple present and the English progressive aspect) affects the sensitivity to the two non-linguistic factors and consequently also affects how people describe a motion event endpoint. In Experiment 1, we used the English simple present for all sentence stimuli (e.g., he walks (?) the trash bin). We found a significant effect of Interlocutor (the interlocutor’s social status) on preposition choice, but no significant effect of Intention (the actor’s goal). In Experiment 2, we replaced the English simple present with the English progressive aspect (e.g., he is walking (?) the trash bin). We found significant main effects of both Interlocutor and Intention on preposition choice. These findings extend those reported in Liao et al. (2021) Language, Cognition and Neuroscience, 35(4), 498–520 in that protagonist intention and interlocutor status were found to indeed affect motion event endpoint description. The current findings furthermore show that grammatical aspect affects people’s sensitivity to these factors, thus also affecting how a motion event endpoint is described.

Identifying psychiatric manifestations in schizophrenia and depression from audio-visual behavioural indicators through a machine-learning approach

Schizophrenia (SCZ) and depression (MDD) are two chronic mental disorders that seriously affect the quality of life of millions of people worldwide. We aim to develop machine-learning methods with objective linguistic, speech, facial, and motor behavioral cues to reliably predict the severity of psychopathology or cognitive function, and distinguish diagnosis groups. We collected and analyzed the speech, facial expressions, and body movement recordings of 228 participants (103 SCZ, 50 MDD, and 75 healthy controls) from two separate studies. We created an ensemble machine-learning pipeline and achieved a balanced accuracy of 75.3% for classifying the total score of negative symptoms, 75.6% for the composite score of cognitive deficits, and 73.6% for the total score of general psychiatric symptoms in the mixed sample containing all three diagnostic groups. The proposed system is also able to differentiate between MDD and SCZ with a balanced accuracy of 84.7% and differentiate patients with SCZ or MDD from healthy controls with a balanced accuracy of 82.3%. These results suggest that machine-learning models leveraging audio-visual characteristics can help diagnose, assess, and monitor patients with schizophrenia and depression.

Quantifying gender biases towards politicians on Reddit.

Despite attempts to increase gender parity in politics, global efforts have struggled to ensure equal female representation. This is likely tied to implicit gender biases against women in authority. In this work, we present a comprehensive study of gender biases that appear in online political discussion. To this end, we collect 10 million comments on Reddit in conversations about male and female politicians, which enables an exhaustive study of automatic gender bias detection. We address not only misogynistic language, but also other manifestations of bias, like benevolent sexism in the form of seemingly positive sentiment and dominance attributed to female politicians, or differences in descriptor attribution. Finally, we conduct a multi-faceted study of gender bias towards politicians investigating both linguistic and extra-linguistic cues. We assess 5 different types of gender bias, evaluating coverage, combinatorial, nominal, sentimental and lexical biases extant in social media language and discourse. Overall, we find that, contrary to previous research, coverage and sentiment biases suggest equal public interest in female politicians. Rather than overt hostile or benevolent sexism, the results of the nominal and lexical analyses suggest this interest is not as professional or respectful as that expressed about male politicians. Female politicians are often named by their first names and are described in relation to their body, clothing, or family; this is a treatment that is not similarly extended to men. On the now banned far-right subreddits, this disparity is greatest, though differences in gender biases still appear in the right and left-leaning subreddits. We release the curated dataset to the public for future studies.

Characteristics of Early Expressive Vocabulary in Mandarin-Speaking Children With Cochlear Implants.

The aim of this study is to evaluate whether Mandarin-speaking children with cochlear implants (CIs) demonstrated early lexical composition similar to their hearing peers who were at the same vocabulary level and the extent to which children with CIs were sensitive to linguistic and conceptual properties when developing early lexicon. Participants were 77 Mandarin-speaking children with CIs who received CIs before 30 months of age. Their expressive vocabulary was documented using the Infant Checklist of the Early Vocabulary Inventory for Mandarin Chinese 9 or 12 months after CI activation. Percent social words, common nouns, predicates (verbs, adjectives), and closed-class words in total vocabulary were computed for children at different vocabulary levels. Common nouns and verbs were further coded for their word class (noun, verb), word frequency, word length, and imageability to predict how likely a given noun or verb would be produced by children with CIs. Like children with typical hearing, social words were the most dominant category when vocabulary size in children with CIs was smaller than 20 words; common nouns became the most dominant category when the vocabulary size reached 21 words. The difference in percent common nouns and percent predicates (i.e., noun bias) was similar in children with CIs and their hearing peers. In addition, verbs, common words, monosyllabic words, and more imageable words were more likely to be produced by children with CIs than their counterparts. Mandarin children with CIs showed language-specific patterns in early lexical composition like their hearing peers. They were able to use multiple linguistic and conceptual cues when approaching early expressive vocabulary despite perceptual and processing constraints. https://doi.org/10.23641/asha.21357723.

Trimodal prediction of speaking and listening willingness to help improve turn-changing modeling.

Participants in a conversation must carefully monitor the turn-management (speaking and listening) willingness of other conversational partners and adjust their turn-changing behaviors accordingly to have smooth conversation. Many studies have focused on developing actual turn-changing (i.e., next speaker or end-of-turn) models that can predict whether turn-keeping or turn-changing will occur. Participants' verbal and non-verbal behaviors have been used as input features for predictive models. To the best of our knowledge, these studies only model the relationship between participant behavior and turn-changing. Thus, there is no model that takes into account participants' willingness to acquire a turn (turn-management willingness). In this paper, we address the challenge of building such models to predict the willingness of both speakers and listeners. Firstly, we find that dissonance exists between willingness and actual turn-changing. Secondly, we propose predictive models that are based on trimodal inputs, including acoustic, linguistic, and visual cues distilled from conversations. Additionally, we study the impact of modeling willingness to help improve the task of turn-changing prediction. To do so, we introduce a dyadic conversation corpus with annotated scores of speaker/listener turn-management willingness. Our results show that using all three modalities (i.e., acoustic, linguistic, and visual cues) of the speaker and listener is critically important for predicting turn-management willingness. Furthermore, explicitly adding willingness as a prediction task improves the performance of turn-changing prediction. Moreover, turn-management willingness prediction becomes more accurate when this joint prediction of turn-management willingness and turn-changing is performed by using multi-task learning techniques.

EEG analysis based on dynamic visual stimuli

This paper reviews best practices for experimental design and analysis for sign language research using neurophysiological methods, such as electroencephalography (EEG) and other methods with high temporal resolution, as well as identifies methodological challenges in neurophysiological research on natural sign language processing. In particular, we outline the considerations for generating linguistically and physically well-controlled stimuli accounting for 1) the layering of manual and non-manual information at different timescales, 2) possible unknown linguistic and non-linguistic visual cues that can affect processing, 3) variability across linguistic stimuli, and 4) predictive processing. Two specific concerns with regard to the analysis and interpretation of observed event related potential (ERP) effects for dynamic stimuli are discussed in detail. First, we discuss the “trigger/effect assignment problem”, which describes the difficulty of determining the time point for calculating ERPs. This issue is related to the problem of determining the onset of a critical sign (i.e., stimulus onset time), and the lack of clarity as to how the border between lexical (sign) and transitional movement (motion trajectory between individual signs) should be defined. Second, we discuss possible differences in the dynamics within signing that might influence ERP patterns and should be controlled for when creating natural sign language material for ERP studies. In addition, we outline alternative approaches to EEG data analyses for natural signing stimuli, such as the timestamping of continuous EEG with trigger markers for each potentially relevant cue in dynamic stimuli. Throughout the discussion, we present empirical evidence for the need to account for dynamic, multi-channel, and multi-timescale visual signal that characterizes sign languages in order to ensure the ecological validity of neurophysiological research in sign languages.

CHALLENGING THE ORAL-ONLY NARRATIVE: ENHANCING EARLY SIGNED INPUT FOR DEAF CHILDREN WITH HEARING PARENTS.

Learning a language is, at its core, a process of noticing patterns in the language input surrounding the learner. Although many of these language patterns are complex and difficult for adult speakers/signers to recognize, infants are able to find and learn them from the youngest age, without explicit instruction. However, this impressive feat is dependent on children's early access to ample and well-formed input that displays the regular patterns of natural language. Such input is far from guaranteed for the great majority of deaf and hard of hearing (DHH) children, leading to well-documented difficulties and delays in linguistic development. Efforts to remedy this situation have focused disproportionately on amplifying DHH children's hearing levels, often through cochlear implants, as young as possible to facilitate early access to spoken language. Given the time required for cochlear implantation, its lack of guaranteed success, and the critical importance of exposing infants to quality language input as early as possible, a bimodal bilingual approach can optimize DHH infants' chances for on-time language development by providing them with both spoken and signed language input from the start. This paper addresses the common claim that signing with DHH children renders the task of learning spoken language more difficult, leading to delays and inferior language development, compared to DHH children in oral-only environments. That viewpoint has most recently been articulated by Geers et al. (2017a), which I will discuss as a representative of the many studies promoting an oral-only approach. Contrary to their claims that signing degrades the language input available to DHH children, recent research has demonstrated that the formidable pattern-finding skills of newborn infants extends to linguistic cues in both the spoken and signed modalities, and that the additional challenge of simultaneously acquiring two languages is offset by important "bilingual advantages." Of course, securing early access to high quality signed input for DHH children from hearing families requires considerable effort, especially since most hearing parents are still novice signers. This paper closes with some suggestions for how to address this challenge through partnerships between linguistics researchers and early intervention programs to support family-centered bimodal bilingual development for DHH children.

Backers Beware: Characteristics and Detection of Fraudulent Crowdfunding Campaigns.

Crowdfunding has seen an enormous rise, becoming a new alternative funding source for emerging companies or new startups in recent years. As crowdfunding prevails, it is also under substantial risk of the occurrence of fraud. Though a growing number of articles indicate that crowdfunding scams are a new imminent threat to investors, little is known about them primarily due to the lack of measurement data collected from real scam cases. This paper fills the gap by collecting, labeling, and analyzing publicly available data of a hundred fraudulent campaigns on a crowdfunding platform. In order to find and understand distinguishing characteristics of crowdfunding scams, we propose to use a broad range of traits including project-based traits, project creator-based ones, and content-based ones such as linguistic cues and Named Entity Recognition features, etc. We then propose to use the feature selection method called Forward Stepwise Logistic Regression, through which 17 key discriminating features (including six original and hitherto unused ones) of scam campaigns are discovered. Based on the selected 17 key features, we present and discuss our findings and insights on distinguishing characteristics of crowdfunding scams, and build our scam detection model with 87.3% accuracy. We also explore the feasibility of early scam detection, building a model with 70.2% of classification accuracy right at the time of project launch. We discuss what features from which sections are more helpful for early scam detection on day 0 and thereafter.

Someone out there? A study on the social presence of anthropomorphized chatbots

Companies are increasingly employing text-based chatbots as a time and cost-efficient way to interact with customers. While companies begin to explore anthropomorphic chatbot designs by imbuing chatbots with human-like characteristics, the effectiveness of chatbot anthropomorphism remains unclear. We conducted three experiments to assess the effectiveness of chatbot anthropomorphism in customer–chatbot interactions. By equipping chatbots with human-like linguistic cues, we evoke different levels of chatbot anthropomorphism. Our results show significant positive effects of chatbot anthropomorphism on trust, purchase intention, word of mouth, and satisfaction with the shopping experience. More importantly, we identify social presence as the underlying mediating mechanism of these effects. These effects are robust and not contingent on different shopping contexts distinguished by hedonic versus utilitarian shopping motivations or the disclosure of (non-)sensitive information by customers. The present research derives managerial implications for companies that seek to effectively employ chatbots in customer interactions. Further, this study advances research on customers’ reactions towards anthropomorphized chatbots and demonstrates that social presence is a critical driver of successful customer-chatbot interactions.

Syntax Matters: Exploring the Effect of Linguistic Similarity in Third Language Acquisition

AbstractOver the last two decades, the question of to which linguistic cues learners pay attention when they decode a new language has been subject to controversy in the field of third language (L3) acquisition. In this article, we present an artificial language learning experiment that investigated how lexical and syntactic similarities between an artificial L3 and preexisting grammars impact crosslinguistic influence at the very beginning of the acquisition process. We exposed four groups of 30 Norwegian–English bilinguals each to one of four L3s and gave them training in that L3. The participants gave forced‐choice acceptability judgments on pairs of nonsubject‐initial declarative clauses that differed in word order, one grammatical in English, the other grammatical in Norwegian. The participants had not been exposed to nonsubject‐initial declaratives during the exposure and training phases to avoid confounds with learning. The results showed that both lexical and syntactic similarities affect crosslinguistic influence. We discuss this result considering contemporary accounts of L3 acquisition.

Preverbal syntactic complexity leads to local coherence effects

ABSTRACT The effective use of preverbal linguistic cues to make successful clause-final verbal prediction as well as robust maintenance of such predictions has been argued to be a cross-linguistic generalisation for SOV languages such as German and Japanese. In this paper, we show that native speakers of Hindi (an SOV language) falter in forming a clause-final structure in the presence of a centre-embedded relative clause with a non-canonical word order. In particular, the fallibility of the parser is illustrated by the formation of a grammatically illicit locally coherent parse during online processing. Such a parse should not be formed if the grammatically licit matrix clause final structure was being successfully formed. The formation of a locally coherent parse is further illustrated by probing various syntactic dependencies via targeted questions. We show that the parser's susceptibility to form such structures is not driven by top-down processing, rather the effect can only be explained through a bottom-up parsing approach. Further, our investigation suggests that while plausibility is essential, presence of overt agreement features might not be necessary for forming a locally coherent parse in Hindi. These results go against top-down proposals to local coherence such as lossy surprisal and are consistent with the good-enough processing model to comprehension while only partially supporting the SOPARSE account. The work highlights how top-down processing and bottom-up information interact during sentence comprehension in SOV languages – prediction suffers with increased complexity of the preverbal linguistic environment.

How cues to social categorization impact children's inferences about social categories

Social categorization involves two crucial processes: First, children seek properties on which they can categorize individuals, i.e., they learn to form social categories; then children make inferences based on social category membership and might develop affective responses toward social categories. Over the last decade, a growing number of research in developmental psychology started to use novel social categories to investigate how children learn and reason about social categories. To date, three types of cues have been put forward as means to form social categories, namely linguistic, visual, and behavioral cues. Based on social category membership, children draw inferences about the shared properties of social category members and about how social category members ought to behave and interact with each other. With additional input, children might apply essentialist beliefs to social categories and develop affective responses toward social categories. This article aims to provide key insights on the development of stereotypes and intergroup biases by reviewing recent works that investigated how children learn to form novel social categories and the kind of inferences they make about these novel social categories.

The role of syntactic cues in monolingual and bilingual two-year-olds’ novel word disambiguation

Although linguistic and nonlinguistic cues help young children infer meaning when presented with unfamiliar words, little is known about how syntactic information and early bilingual experience shape word learning. This study examined how monolingual and bilingual 24- to 30-month-olds’ disambiguation of novel words during a mutual exclusivity task differs as a function of syntactic cues, age, and productive vocabulary. English monolinguals and Spanish-English bilinguals were presented with familiar and novel objects within a syntactic context (e.g., “Give me the blick!”) or in isolation (e.g., “Blick!”). Results showed that monolinguals and bilinguals adhered to mutual exclusivity more often when provided with syntactic cues than when those cues were absent. Furthermore, bilinguals’ mutually exclusive disambiguation of novel words increased with age, but only when syntactic cues were available. These results provide insight into factors that influence children’s disambiguation of novel words. The theoretical implications of these findings are discussed.

Do they speak like me? Exploring how perceptions of linguistic difference may influence patient perceptions of healthcare providers

ABSTRACT The increased utilization of telehealth has provided patients with the opportunity to interact with racially diverse healthcare providers (HCPs). While evidence of racial stereotypes in healthcare is well documented, less is known about whether linguistic cues increase or decrease racial bias in healthcare interactions. The purpose of this pilot study was to use virtual clinicians (VCs) to examine how varying linguistic features affect patient perceptions of Black-identifying HCPs. Participants (N = 282) were recruited to participate in an online pilot study using a two-arm posttest-only experimental design. Participants were randomly assigned to interact with a Black VC that used vocal cues associated with either Standard American English (SAE) or African American English (AAE) on the topic of colorectal cancer. After the interaction, participants completed a posttest questionnaire. Resulting data were analyzed using mediation.

Effects of Global Discourse Coherence on Local Contextual Predictions

Context is vitally important for how we understand the world around us, and this is particularly so with language. We know that humans use a range of linguistic cues when understanding language to help tailor their expectations about upcoming linguistic material, contributing to the seamless nature of comprehension. Linguistic cues fall into two different levels of context – the local, sentence context and the wider, global context. Some linguistic cues that are considered to be global are knowledge about the world or the speaker. These have been found to facilitate semantic processing when they align with linguistic expectations through faster reading times of predictable content. One other global contextual cue that may be of use to how we represent meaning and understand the world around us is the coherence of a discourse. The current, pre-registered study investigates the interaction between discourse coherence and word predictability during language comprehension. To do so, we used an online, self-paced reading paradigm and had participants read three-sentence discourses that differed on the coherence of the final target sentence with the first two preamble sentences, and also on the predictability of a critical word within the target sentence. From our first experiment, we found that people were sensitive to the coherence of the overall three-sentence narrative, with slower reading times for trials that had less coherent preamble contexts. After exploration of our data, we pre-registered two further studies to investigate whether this effect is still present in more extreme experimental settings. For Experiment 2, we altered the ratio of less and more coherent contexts to see if people still make use of discourse coherence as a linguistic cue for informing their expectations when there was a greater proportion of more coherent trials. We found this to be the case. In Experiment 3, we replaced our less predictable critical words that were semantically relevant to the overall message of the target sentence with completely anomalous words. Here, we found people were faster to read the highly predictable critical words and slower to read the anomalous critical words when they first read more coherent contexts, but not when they first read less coherent contexts. This suggests that people are able to use relevant linguistic cues from both levels of context, and do so flexibly depending on the degree of contextual support at the global discourse level.

Language concatenates perceptual features into representations during comprehension

Although many studies have investigated the activation of perceptual representations during language comprehension, to our knowledge only one previous study has directly tested how perceptual features are combined into representations during comprehension. In their classic study, Potter and Faulconer [(1979). Understanding noun phrases. Journal of Verbal Learning and Verbal Behavior, 18, 509–521.] investigated the perceptual representation of adjective-noun combinations. However, their non-orthogonal design did not allow the differentiation between conjunctive vs. disjunctive representations. Using randomized orthogonal designs, we observe evidence for disjunctive perceptual representations when participants represent feature combinations simultaneously (in several experiments; N = 469), and we observe evidence for conjunctive perceptual representations when participants represent feature combinations sequentially (In several experiments; N = 628). Our findings show that the generation of conjunctive representations during comprehension depends on the concatenation of linguistic cues, and thus suggest the construction of elaborate perceptual representations may critically depend on language.

Veronica Koman’s Tweets During West Papuan Uproar

The current study is to emphasize Veronica Koman's perspective and how she became one of the most famous human rights advocates during the November 2019 West Papuan protest. The tweets of Veronica Koman were evaluated using CDA and transitivity analysis. The researchers used observation and documentation to detect processes and other linguistic cues in the documented tweets. Koman used transitivity to tweet about what transpired in West Papua. They said her tweets evoked pictures of the crisis, encouraging people across the country and globally to learn about West Papua. As a social-political journalist, she aimed to guarantee that injustice and bigotry were criticized by the Indonesian people, and pushed them to express their displeasure.

Linguistic cues of chief executive officer personality and its effect on performance

Chief Executive Officer (CEO) personality belongs to the most important constructs for business practice and research, but its measurement remains a major obstacle. Therefore, I propose a language‐based approach of freely available, large‐scale corporate documents to study linguistic hints regarding CEOs' personality dimensions by applying a validated, closed‐based approach developed to a sample of Fortune 500 companies. I compare the results to validated, machine learning‐trained open‐based results obtained from conference calls of 3000 CEOs of S&P 1500 companies. In order to provide a basis for comparison, I supplement these linguistic personality profiles with a sample of convicted criminal CEOs that provide an archetype of extreme linguistic cases. Finally, after controlling for commonly used content analytical constructs (e.g., entrepreneurial orientation, market orientation) to capture CEOs' cognitive state, I link these cross‐sectional linguistic profiles to longitudinal firm‐level performance data. The analysis indicates that the linguistic manifestations of CEO personality explain a great share of firm performance in conjunction with observable individual, industry, and firm‐level data. While there appear to be complementarities between open‐based and closed‐based language approaches, the analysis also indicates that the open‐based linguistic trait of neuroticism is negatively related to various measures of firm performance. In particular, these measures appear to be more robust against genre effects and language that are unique among CEOs (self‐selection effects). Therefore, the approach sheds light on the important role of validation procedures in the realm of computer‐aided content analysis (CATA) and the conducive and detrimental effects of CEO personality cues on firm performance. The paper discusses practical implications for stakeholders and shareholders wishing to infer personality cues of key decision‐makers from large‐scale corporate documents and theoretical implications for the advancement of the upper echelon research.

One Fee, Two Fees; Red Fee, Blue Fee: People Use the Valence of Others’ Speech in Social Relational Judgments

We present an empirical demonstration that people rely on linguistic valence as a direct cue to a speaker’s group membership. Members of the U.S. voting public judge positive words as more likely to be spoken by members of their political in-group, and negative words as more likely to be spoken by members of their political out-group (three studies with 655 participants). We further find that participants perceive pluralized forms of nouns as more extremely valenced than singular forms (one study with 280 participants). This allowed us to control for the semantic content of words while eliciting systematic differences in the source attributions made by partisans. Our work contributes to both theory and methodology used to understand the linguistic cues people use to make social relational judgments.