Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
18265 papers
Congenital amusia is a lifelong disorder of fine-grained pitch processing in music and speech. However, it remains unclear whether amusia is a pitch-specific deficit, or whether it affects frequency/spectral processing more broadly, such as the perception of formant frequency in vowels, apart from pitch. In this study, in order to illuminate the scope of the deficits, we compared the performance of 15 Cantonese-speaking amusics and 15 matched controls on the categorical perception of sound continua in four stimulus contexts: lexical tone, pure tone, vowel, and voice onset time (VOT). Whereas lexical tone, pure tone and vowel continua rely on frequency/spectral processing, the VOT continuum depends on duration/temporal processing. We found that the amusic participants performed similarly to controls in all stimulus contexts in the identification, in terms of the across-category boundary location and boundary width. However, the amusic participants performed systematically worse than co)
In tonal languages, such as Mandarin Chinese, the pitch contour of vowels discriminates lexical meaning, which is not the case in non-tonal languages such as German. Recent data provide evidence that pitch processing is influenced by language experience. However, there are still many open questions concerning the representation of such phonological and language-related differences at the level of the auditory cortex (AC). Using magnetoencephalography (MEG), we recorded transient and sustained auditory evoked fields (AEF) in native Chinese and German speakers to investigate language related phonological and semantic aspects in the processing of acoustic stimuli. AEF were elicited by spoken meaningful and meaningless syllables, by vowels, and by a French horn tone. Speech sounds were recorded from a native speaker and showed frequency-modulations according to the pitch-contours of Mandarin. The sustained field (SF) evoked by natural speech signals was significantly larger for Chinese th)
Automatic extraction of protein-protein interaction (PPI) pairs from biomedical literature is a widely examined task in biological information extraction. Currently, many kernel based approaches such as linear kernel, tree kernel, graph kernel and combination of multiple kernels has achieved promising results in PPI task. However, most of these kernel methods fail to capture the semantic relation information between two entities. In this paper, we present a special type of tree kernel for PPI extraction which exploits both syntactic (structural) and semantic vectors information known as Distributed Smoothed Tree kernel (DSTK). DSTK comprises of distributed trees with syntactic information along with distributional semantic vectors representing semantic information of the sentences or phrases. To generate robust machine learning model composition of feature based kernel and DSTK were combined using ensemble support vector machine (SVM). Five different corpora (AIMed, BioInfer, HPRD50, )
In spite of decades of theorizing, the origins of Zipf’s law remain elusive. I propose that a Zipfian distribution straightforwardly follows from the interaction of syntax (word classes differing in class size) and semantics (words having to be sufficiently specific to be distinctive and sufficiently general to be reusable). These factors are independently motivated and well-established ingredients of a natural-language system. Using a computational model, it is shown that neither of these ingredients suffices to produce a Zipfian distribution on its own and that the results deviate from the Zipfian ideal only in the same way as natural language itself does. [ABSTRACT FROM AUTHOR], Copyright of PLoS ONE is the property of Public Library of Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be )
Background: Most of earlier studies in the field of literature-based discovery have adopted Swanson's ABC model that links pieces of knowledge entailed in disjoint literatures. However, the issue concerning their practicability remains to be solved since most of them did not deal with the context surrounding the discovered associations and usually not accompanied with clinical confirmation. In this study, we aim to propose a method that expands and elaborates the existing hypothesis by advanced text mining techniques for capturing contexts. We extend ABC model to allow for multiple B terms with various biological types. Results: We were able to concretize a specific, metabolite-related hypothesis with abundant contextual information by using the proposed method. Starting from explaining the relationship between lactosylceramide and arterial stiffness, the hypothesis was extended to suggest a potential pathway consisting of lactosylceramide, nitric oxide, malondialdehyde, and arteria)
Despite the ongoing growth in the number of published randomized controlled trials (RCTs) and increased quality assessment of RCTs, the association between the quality and characteristics in the text has not been sufficiently studied. We are interested in a specific question: what kind of sentences is a good indicator of high quality RCTs? To help researchers to efficiently screen articles worth reading, this study aims 1) to quantify the linguistic features of articles and 2) to build a document assessment model to evaluate quality of RCTs using only the abstract. All RCTs that were conducted in Japan in 2010 as original articles were included in the analysis. Data were independently assessed by two reviewers using a risk-of-bias tool. Three aspects of linguistic style were quantitatively measured, and a document model was constructed to evaluate the RCTs. A total of 302 RCTs were selected for quality assessment. Of these, 255 articles were assessed as high quality and 47 as low qual)
Infants preferentially discriminate between speech tokens that cross native category boundaries prior to acquiring a large receptive vocabulary, implying a major role for unsupervised distributional learning strategies in phoneme acquisition in the first year of life. Multiple sources of between-speaker variability contribute to children’s language input and thus complicate the problem of distributional learning. Adults resolve this type of indexical variability by adjusting their speech processing for individual speakers. For infants to handle indexical variation in the same way, they must be sensitive to both linguistic and indexical cues. To assess infants’ sensitivity to and relative weighting of indexical and linguistic cues, we familiarized 12-month-old infants to tokens of a vowel produced by one speaker, and tested their listening preference to trials containing a vowel category change produced by the same speaker (linguistic information), and the same vowel category produced )
Background: To facilitate informed consent, consent forms should use language below the grade eight level. Research Ethics Boards (REBs) provide consent form templates to facilitate this goal. Templates with inappropriate language could promote consent forms that participants find difficult to understand. However, a linguistic analysis of templates is lacking. Methods: We reviewed the websites of 124 REBs for their templates. These included English language medical school REBs in Australia/New Zealand (n = 23), Canada (n = 14), South Africa (n = 8), the United Kingdom (n = 34), and a geographically-stratified sample from the United States (n = 45). Template language was analyzed using Coh-Metrix linguistic software (v.3.0, Memphis, USA). We evaluated the proportion of REBs with five key linguistic outcomes at or below grade eight. Additionally, we compared quantitative readability to the REBs’ own readability standards. To determine if the template’s country of origin or the presenc)
In decision making, similarity measure and distance between two objects are crucial to be able to determine the relationship between those objects. Many researchers have received much attention for their research on this subject. In this study, we propose two novel similarity measures between hesitant fuzzy linguistic term sets (HFLTSs). In addition, two extensions of Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are proposed in the hesitant fuzzy linguistic environments. Furthermore, an example of an application concerning traditional Chinese medical diagnosis and an MCDM problem have been given to illustrate the applicability and validation of these similarity measures of HFLTSs. Furthermore, the results of examples demonstrate that the Dice and Jaccard similarity measures are more reasonable than the cosine similarity measure with respect to HFLTSs. [ABSTRACT FROM AUTHOR], Copyright of PLoS ONE is the property of Public Library of Science and its conten)
We present a new open source software tool called BEASTling, designed to simplify the preparation of Bayesian phylogenetic analyses of linguistic data using the BEAST 2 platform. BEASTling transforms comparatively short and human-readable configuration files into the XML files used by BEAST to specify analyses. By taking advantage of Creative Commons-licensed data from the Glottolog language catalog, BEASTling allows the user to conveniently filter datasets using names for recognised language families, to impose monophyly constraints so that inferred language trees are backward compatible with Glottolog classifications, or to assign geographic location data to languages for phylogeographic analyses. Support for the emerging cross-linguistic linked data format (CLDF) permits easy incorporation of data published in cross-linguistic linked databases into analyses. BEASTling is intended to make the power of Bayesian analysis more accessible to historical linguists without strong programmi)
Paintings have high cultural and commercial value, so that needs to be preserved. Many techniques have been attempted to analyze properties of paintings, including X-ray analysis and optical coherence tomography (OCT) methods, and enable conservation of paintings from forgeries. In this paper, we suggest a simple and accurate optical analysis system to protect them from counterfeit which is comprised of fiber optics reflectance spectroscopy (FORS) and line laser-based topographic analysis. The system is designed to fully cover the whole area of paintings regardless of its size for the accurate analysis. For additional assessments, a line laser-based high resolved OCT was utilized. Some forgeries were created by the experts from the three different styles of genuine paintings for the experiments. After measuring surface properties of paintings, we could observe the results from the genuine works and the forgeries have the distinctive characteristics. The forgeries could be distinguishe)
Recent theories propose that language comprehension can influence perception at the low level of perceptual system. Here, we used an adaptation paradigm to test whether processing language caused color adaptation in the visual system. After prolonged exposure to a color linguistic context, which depicted red, green, or non-specific color scenes, participants immediately performed a color detection task, indicating whether they saw a green color square in the middle of a white screen or not. We found that participants were more likely to perceive the green color square after listening to discourses denoting red compared to discourses denoting green or conveying non-specific color information, revealing that language comprehension caused an adaptation aftereffect at the perceptual level. Therefore, semantic representation of color may have a common neural substrate with color perception. These results are in line with the simulation view of embodied language comprehension theory, which )
Collective behaviour is a fascinating and easily observable phenomenon, attractive to a wide range of researchers. In biology, computational models have been extensively used to investigate various properties of collective behaviour, such as: transfer of information across the group, benefits of grouping (defence against predation, foraging), group decision-making process, and group behaviour types. The question ‘why,’ however remains largely unanswered. Here the interest goes into which pressures led to the evolution of such behaviour, and evolutionary computational models have already been used to test various biological hypotheses. Most of these models use genetic algorithms to tune the parameters of previously presented non-evolutionary models, but very few attempt to evolve collective behaviour from scratch. Of these last, the successful attempts display clumping or swarming behaviour. Empirical evidence suggests that in fish schools there exist three classes of behaviour; swarmi)
We learn language from our social environment. In general, the more sources we have, the less informative each of them is, and the less weight we should assign it. If this is the case, people who interact with fewer others should be more susceptible to the influence of each of their interlocutors. This paper tests whether indeed people who interact with fewer other people have more malleable phonological representations. Using a perceptual learning paradigm, this paper shows that individuals who regularly interact with fewer others are more likely to change their boundary between /d/ and /t/ following exposure to an atypical speaker. It further shows that the effect of number of interlocutors is not due to differences in ability to learn the speaker’s speech patterns, but specific to likelihood of generalizing the learned pattern. These results have implications for both language learning and language change, as they suggest that individuals with smaller social networks might play an )
For the past 50 years, acknowledgments have been studied as important paratextual traces of research practices, collaboration, and infrastructure in science. Since 2008, funding acknowledgments have been indexed by Web of Science, supporting large-scale analyses of research funding. Applying advanced linguistic methods as well as Correspondence Analysis to more than one million acknowledgments from research articles and reviews published in 2015, this paper aims to go beyond funding disclosure and study the main types of contributions found in acknowledgments on a large scale and through disciplinary comparisons. Our analysis shows that technical support is more frequently acknowledged by scholars in Chemistry, Physics and Engineering. Earth and Space, Professional Fields, and Social Sciences are more likely to acknowledge contributions from colleagues, editors, and reviewers, while Biology acknowledgments put more emphasis on logistics and fieldwork-related tasks. Conflicts of intere)
Humans are highly adept at categorizing visual stimuli, but studies of human categorization are typically validated by verbal reports. This makes it difficult to perform comparative studies of categorization using non-human animals. Interpretation of comparative studies is further complicated by the possibility that animal performance may merely reflect reinforcement learning, whereby discrete features act as discriminative cues for categorization. To assess and compare how humans and monkeys classified visual stimuli, we trained 7 rhesus macaques and 41 human volunteers to respond, in a specific order, to four simultaneously presented stimuli at a time, each belonging to a different perceptual category. These exemplars were drawn at random from large banks of images, such that the stimuli presented changed on every trial. Subjects nevertheless identified and ordered these changing stimuli correctly. Three monkeys learned to order naturalistic photographs; four others, close-up sectio)
The increasing growth of literature in biodiversity presents challenges to users who need to discover pertinent information in an efficient and timely manner. In response, text mining techniques offer solutions by facilitating the automated discovery of knowledge from large textual data. An important step in text mining is the recognition of concepts via their linguistic realisation, i.e., terms. However, a given concept may be referred to in text using various synonyms or term variants, making search systems likely to overlook documents mentioning less known variants, which are albeit relevant to a query term. Domain-specific terminological resources, which include term variants, synonyms and related terms, are thus important in supporting semantic search over large textual archives. This article describes the use of text mining methods for the automatic construction of a large-scale biodiversity term inventory. The inventory consists of names of species, amongst which naming variati)
Physical capacity and coordination cannot alone predict success in team sports such as soccer. Instead, more focus has been directed towards the importance of cognitive abilities, and it has been suggested that executive functions (EF) are fundamentally important for success in soccer. However, executive functions are going through a steep development from adolescence to adulthood. Moreover, more complex EF involving manipulation of information (higher level EF) develop later than simple executive functions such as those linked to simple working memory capacity (Core EF). The link between EF and success in young soccer players is therefore not obvious. In the present study we investigated whether EF are associated with success in soccer in young elite soccer players. We performed tests measuring core EF (a demanding working memory task involving a variable n-back task; dWM) and higher level EF (Design Fluency test; DF). Color-Word Interference Test and Trail Making Test were performed)
We examined if external cues such as other agents' actions can influence the choice of language during voluntary and cued object naming in bilinguals in three experiments. Hindi–English bilinguals first saw a cartoon waving at a color patch. They were then asked to either name a picture in the language of their choice (voluntary block) or to name in the instructed language (cued block). The colors waved at by the cartoon were also the colors used as language cues (Hindi or English). We compared the influence of the cartoon’s choice of color on naming when speakers had to indicate their choice explicitly before naming (Experiment 1) as opposed to when they named directly on seeing the pictures (Experiment 2 and 3). Results showed that participants chose the language indicated by the cartoon greater number of times (Experiment 1 and 3). Speakers also switched significantly to the language primed by the cartoon greater number of times (Experiment 1 and 2). These results suggest that choi)
Because a biomass gasification station includes various hazard factors, hazard assessment is needed and significant. In this article, the cloud model (CM) is employed to improve set pair analysis (SPA), and a novel hazard assessment method for a biomass gasification station is proposed based on the cloud model-set pair analysis (CM-SPA). In this method, cloud weight is proposed to be the weight of index. In contrast to the index weight of other methods, cloud weight is shown by cloud descriptors; hence, the randomness and fuzziness of cloud weight will make it effective to reflect the linguistic variables of experts. Then, the cloud connection degree (CCD) is proposed to replace the connection degree (CD); the calculation algorithm of CCD is also worked out. By utilizing the CCD, the hazard assessment results are shown by some normal clouds, and the normal clouds are reflected by cloud descriptors; meanwhile, the hazard grade is confirmed by analyzing the cloud descriptors. After that)
This work is the first to take advantage of recurrent neural networks to predict influenza-like illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data and the state-of-the-art machine learning models, we build and evaluate the predictive power of neural network architectures based on Long Short Term Memory (LSTMs) units capable of nowcasting (predicting in “real-time”) and forecasting (predicting the future) ILI dynamics in the 2011 – 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, embeddings, word ngrams, stylistic patterns, and communication behavior using hashtags and mentions. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks using a diverse set of evaluation metrics. Finally, we )
Today, a considerable proportion of the public political discourse on nationwide elections proceeds in Online Social Networks. Through analyzing this content, we can discover the major themes that prevailed during the discussion, investigate the temporal variation of positive and negative sentiment and examine the semantic proximity of these themes. According to existing studies, the results of similar tasks are heavily dependent on the quality and completeness of dictionaries for linguistic preprocessing, entity discovery and sentiment analysis. Additionally, noise reduction is achieved with methods for sarcasm detection and correction. Here we report on the application of these methods on the complete corpus of tweets regarding two local electoral events of worldwide impact: the Greek referendum of 2015 and the subsequent legislative elections. To this end, we compiled novel dictionaries for sentiment and entity detection for the Greek language tailored to these events. We subsequen)
Objective: Knowing which specific verbal techniques “good” therapists use in their daily work is important for training and evaluation purposes. In order to systematize what is being practiced in the field, our aim was to empirically identify verbal techniques applied in psychodynamic sessions and to differentiate them according to their basic semantic features using a bottom-up, qualitative approach. Method: Mixed-Method-Design: In a comprehensive qualitative study, types of techniques were identified at the level of utterances based on transcribed psychodynamic therapy sessions using Qualitative Content Analysis (4211 utterances). The definitions of the identified categories were successively refined and modified until saturation was achieved. In a subsequent quantitative study, inter-rater reliability was assessed both at the level of utterances (n = 8717) and at the session level (n = 38). The convergent validity of the categories was investigated by analyzing associations with )
We report the results of a bilingual continuous recognition memory task during which single- and multi-neuron activity was recorded in human subjects with intracranial microwire implants. Subjects (n = 5) were right-handed Spanish-English bilinguals who were undergoing evaluation prior to surgery for severe epilepsy. Subjects were presented with Spanish and English words and the task was to determine whether any given word had been seen earlier in the testing session, irrespective of the language in which it had appeared. Recordings in the left and right hippocampus revealed notable laterality, whereby both Spanish and English items that had been seen previously in the other language (switch trials) triggered increased neural firing in the left hippocampus. Items that had been seen previously in the same language (repeat trials) triggered increased neural firings in the right hippocampus. These results are consistent with theories that propose roles of both the left- and right-hemisph)
The judgement of skill experience and its levels is ambiguous though it is crucial for decision-making in sport sciences studies. We developed a fuzzy decision support system to classify experience of non-elite distance runners. Two Mamdani subsystems were developed based on expert running coaches’ knowledge. In the first subsystem, the linguistic variables of training frequency and volume were combined and the output defined the quality of running practice. The second subsystem yielded the level of running experience from the combination of the first subsystem output with the number of competitions and practice time. The model results were highly consistent with the judgment of three expert running coaches (r>0.88, p<0.001) and also with five other expert running coaches (r>0.86, p<0.001). From the expert’s knowledge and the fuzzy model, running experience is beyond the so-called "10-year rule" and depends not only on practice time, but on the quality of practice (training volume and)
Complex networks are often organized in groups or communities of agents that share the same features and/or functions, and this structural organization is built naturally with the formation of the system. In social networks, we argue that the dynamic of linguistic interactions of agreement among people can be a crucial factor in generating this community structure, given that sharing opinions with another person bounds them together, and disagreeing constantly would probably weaken the relationship. We present here a computational model of opinion exchange that uncovers the community structure of a network. Our aim is not to present a new community detection method proper, but to show how a model of social communication dynamics can reveal the (simple and overlapping) community structure in an emergent way. Our model is based on a standard Naming Game, but takes into consideration three social features: trust, uncertainty and opinion preference, that are built over time as agents comm)
Using new direct measures of numeracy and literacy skills among 85,875 adults in 17 Western countries, we find that foreign-born adults have lower mean skills than native-born adults of the same age (16 to 64) in all of the examined countries. The gaps are small, and vary substantially between countries. Multilevel models reveal that immigrant populations’ demographic and socioeconomic characteristics, employment, and language proficiency explain about half of the cross-national variance of numeracy and literacy skills gaps. Differences in origin countries’ average education level also account for variation in the size of the immigrant-native skills gap. The more protective labor markets in immigrant-receiving countries are, the less well immigrants are skilled in numeracy and literacy compared to natives. For those who migrate before their teens (the 1.5 generation), access to an education system that accommodates migrants’ special needs is crucial. The 1 and 1.5 generation have smal)
Regenerative medicine offers potentially ground-breaking treatments of blindness and low vision. However, as new methodologies are developed, a critical question will need to be addressed: how do we monitor in vivo for functional success? In the present study, we developed novel behavioral assays to examine vision in a vertebrate model system. In the assays, zebrafish larvae are imaged in multiwell or multilane plates while various red, green, blue, yellow or cyan objects are presented to the larvae on a computer screen. The assays were used to examine a loss of vision at 4 or 5 days post-fertilization and a gradual recovery of vision in subsequent days. The developed assays are the first to measure the loss and recovery of vertebrate vision in microplates and provide an efficient platform to evaluate novel treatments of visual impairment. [ABSTRACT FROM AUTHOR], Copyright of PLoS ONE is the property of Public Library of Science and its content may not be copied or emailed to multiple)
From an evolutionary perspective, environmental threats relevant for survival constantly challenged human beings. Current research suggests the evolution of a fear processing module in the brain to cope with these threats. Recently, humans increasingly encountered modern threats (e.g., guns or car accidents) in addition to evolutionary threats (e.g., snakes or predators) which presumably required an adaptation of perception and behavior. However, the neural processes underlying the perception of these different threats remain to be elucidated. We investigated the effect of image content (i.e., evolutionary vs. modern threats) on the activation of neural networks of emotion processing. During functional magnetic resonance imaging (fMRI) 41 participants watched affective pictures displaying evolutionary-threatening, modern-threatening, evolutionary-neutral and modern-neutral content. Evolutionary-threatening stimuli evoked stronger activations than modern-threatening stimuli in left inferior frontal gyrus and thalamus, right middle frontal gyrus and parietal regions as well as bilaterally in parietal regions, fusiform gyrus and bilateral amygdala. We observed the opposite effect, i.e., higher activity for modern-threatening than for evolutionary-threatening stimuli, bilaterally in the posterior cingulate and the parahippocampal gyrus. We found no differences in subjective arousal ratings between the two threatening conditions. On the valence scale though, subjects rated modern-threatening pictures significantly more negative than evolutionary-threatening pictures, indicating a higher level of perceived threat. The majority of previous studies show a positive relationship between arousal rating and amygdala activity. However, comparing fMRI results with behavioral findings we provide evidence that neural activity in fear processing areas is not only driven by arousal or valence, but presumably also by the evolutionary content of the stimulus. This has also fundamental methodological implications, in the sense to suggest a more elaborate classification of stimulus content to improve the validity of experimental designs.
Reinforcement learning tasks are often used to assess participants’ tendency to learn more from the positive or more from the negative consequences of one’s action. However, this assessment often requires comparison in learning performance across different task conditions, which may differ in the relative salience or discriminability of the stimuli associated with more and less rewarding outcomes, respectively. To address this issue, in a first set of studies, participants were subjected to two versions of a common probabilistic learning task. The two versions differed with respect to the stimulus (Hiragana) characters associated with reward probability. The assignment of character to reward probability was fixed within version but reversed between versions. We found that performance was highly influenced by task version, which could be explained by the relative perceptual discriminability of characters assigned to high or low reward probabilities, as assessed by a separate discrimina)
The article is about socio-and-political terminology and its peculiarities that are caused by linguistic factors which make it different from scientific and technical terminology. It doesn’t have such isolation as another term systems have. Socio-and-political Ukrainian terminology is relatively stable and fixed lexical-semantic system which is in a state of continuous movement and progressive improvement. Сhanges in the political lexicon are documented in written sources, particularly in dictionaries. There is the first lexicographic work which contains military, biological, medical and socio-political terms – Лексикон славено-латинський (Lexicon slavic-latin) by Epiphanii Slavynetskyi (1649). Ukrainian social-and-political terms of the late XIX – early XX cent. are fully revealed by I. Franko in his famous scientific works on the socio-political and socio-economic issues. Socio-political vocabulary simultaneously with other lexical and thematic groups was popular among linguists in the language analysis of individual documental sights by B. Khmelnytskyi, Lviv Stauropegion fraternities, historical-and-memoir prose of the first half of the 19th cent. Ideological differentiation of society in the early 90ies of XX cent. caused the reformation of political speech, that all appeared in the renewal conceptual and formal content. This includes: 1) large ammount of lexical innovations to describe new social and political reality; 2) the process of renaming, converting the key nominations of society, expansion of thematic areas because of previous taboo subject; 3) the emergence of new objects and political nominations; 4) the new rating system, the existence of double assessments of the same phenomenon; 5) changes inside pragmatic assessment structures, the negative vector moved from external to internal political areas; 6) formation of a new stylistic norm: the trend to simplification, democratization of broadcasting; 7) approaching to the spoken speech, rendering the stylistically reduced elements; 8) brief presentation, the desire to get rid of irrelevant information. All these are active language creative processes could not be unnoticed by researchers. The development of Ukrainian media language in the pre-October and later periods is shown; its role is defined in enriching and normalization of Ukrainian language: lexical, grammatical structure and spelling; creation of journalistic and scientific style.
The aim of this article is to investigate the growth of lexical norms with a focus on legal language during the end of the 17th century. The materials used are the first two legal handbooks in Swedish, the protocols from the King’s committee for the great revision of Swedish Law, known as the Law of 1734, and texts written by three Swedish lexicographers and linguistic authorities during the early 1800th century. The article is based on empirical studies of legal vocabulary and discussions of lexical norms, and the results give reason to believe that the official linguistic norm in modern Swedish, i.e. the functional norm, is based on the same fundamental mindset concerning the establishment of linguistic novelties as in the 17th century, although the political and democratic conditions have changed over the years.
Researchers have recently introduced various LexTALE-type word recognition tests in order to assess vocabulary size in a second language (L2) mastered by participants. These tests correlate well with other measures of language proficiency in unbalanced bilinguals whose second language ability is well below the level of their native language. In the present study, we investigated whether LexTALE-type tests also discriminate at the high end of the proficiency range. In several regions of Spain, people speak both the regional language (e.g., Catalan or Basque) and Spanish to very high degrees. Still, because of their living circumstances, some consider themselves as either Spanish-dominant or regional-language dominant. We showed that these two groups perform differently on the recently published Spanish Lextale-Esp: The Spanish-dominant group had significantly higher scores than the Catalan-dominant group. We also showed that the noncognate words of the test have the highest discrimination power. This indicates that the existing Lextale-Esp can be used to estimate proficiency differences in highly proficient bilinguals with Spanish as an L2, and that a more sensitive test could be built by replacing the cognates.
Cheating threatens the validity of unproctored online achievement tests. To address this problem, we developed PageFocus, a JavaScript that detects when participants abandon test pages by switching to another window or browser tab. In a first study, we aimed at testing whether PageFocus could detect and prevent cheating. We asked 115 lab and 186 online participants to complete a knowledge test comprising items that were difficult to answer but easy to look up on the Internet. Half of the participants were invited to look up the solutions, which significantly increased their test scores. The PageFocus script detected test takers who abandoned the test page with very high sensitivity and specificity, and successfully reduced cheating by generating a popup message that asked participants not to cheat. In a second study, 510 online participants completed a knowledge test comprising items that could easily be looked up and a reasoning task involving matrices that were impossible to look up. In a first group, a performance-related monetary reward was promised to the top scorers; in a second group, participants took part in a lottery that provided performance-unrelated rewards; and in a third group, no incentive was offered. PageFocus revealed that participants cheated more when performance-related incentives were offered. As expected, however, this effect was limited to items that could easily be looked up. We recommend that PageFocus be routinely employed to detect and prevent cheating on online achievement tests.
Human action perception is so powerful that people can identify movement efficiently in the absence of pictorial information, such as in point-light displays. Interest is growing in this type of stimulus for research in neuroscience. This interest stems from the advantage of separating the component of pure human action kinematics from other pictorial information, such as facial expression and muscle contraction. Although several groups have previously developed datasets of human point-light actions, due to the lack of datasets composed of daily actions with short durations, we developed 20 biological and 40 control (scrambled) point-light movements by using the technique of recording people wearing reflector patches. The videos are about 1 s long. Subsequently, we performed a judgment task in which 100 participants (50 male and 50 female) evaluated each video according to three categories: human action resemblance, performed action, and gender of actor. We present the mean scores of each evaluation for each video, and further propose a selection of the most suitable videos to be used as human point-light action displays and scrambled point-light displays for control. Finally, we discuss our findings on the gender attributions of the point-light displays.
In this article, we introduce an explicit count-based strategy to build word space models with syntactic contexts (dependencies). A filtering method is defined to reduce explicit word-context vectors. This traditional strategy is compared with a neural embedding (predictive) model also based on syntactic dependencies. The comparison was performed using the same parsed corpus for both models. Besides, the dependency-based methods are also compared with bag-of-words strategies, both count-based and predictive ones. The results show that our traditional count-based model with syntactic dependencies outperforms other strategies, including dependency-based embeddings, but just for the tasks focused on discovering similarity between words with the same function (i.e. near-synonyms).
The present study investigated interactions between cognitive processes and finger actions called “kusho,” meaning “air-writing” in Japanese. Kanji-culture individuals often employ kusho behavior in which they move their fingers as a substitute for a pen to write mostly done when they are trying to recall the shape of a Kanji character or the spelling of an English word. To further examine the visualization role of kusho behavior on cognitive processing, we conducted a Kanji construction task in which a stimulus (i.e., sub-parts to be constructed) was simultaneously presented. In addition, we conducted a Kanji vocabulary test to reveal the relation between the kusho benefit and vocabulary size. The experiment provided two sets of novel findings. First, executing kusho behavior improved task performance (correct responses) as long as the participants watched their finger movements while solving the task. This result supports the idea that visual feedback of kusho behavior helps cogniti)
The usual event-related potential (ERP) estimation is the average across epochs time-locked on stimuli of interest. These stimuli are repeated several times to improve the signal-to-noise ratio (SNR) and only one evoked potential is estimated inside the temporal window of interest. Consequently, the average estimation does not take into account other neural responses within the same epoch that are due to short inter stimuli intervals. These adjacent neural responses may overlap and distort the evoked potential of interest. This overlapping process is a significant issue for the eye fixation-related potential (EFRP) technique in which the epochs are time-locked on the ocular fixations. The inter fixation intervals are not experimentally controlled and can be shorter than the neural response’s latency. To begin, the Tikhonov regularization, applied to the classical average estimation, was introduced to improve the SNR for a given number of trials. The generalized cross validation was chosen to obtain the optimal value of the ridge parameter. Then, to deal with the issue of overlapping, the general linear model (GLM), was used to extract all neural responses inside an epoch. Finally, the regularization was also applied to it. The models (the classical average and the GLM with and without regularization) were compared on both simulated data and real datasets from a visual scene exploration in co-registration with an eye-tracker, and from a P300 Speller experiment. The regularization was found to improve the estimation by average for a given number of trials. The GLM was more robust and efficient, its efficiency actually reinforced by the regularization.
There has been a recent boom in research relating semantic space computational models to fMRI data, in an effort to better understand how the brain represents semantic information. In the first study reported here, we expanded on a previous study to examine how different semantic space models and modeling parameters affect the abilities of these computational models to predict brain activation in a data-driven set of 500 selected voxels. The findings suggest that these computational models may contain distinct types of semantic information that relate to different brain areas in different ways. On the basis of these findings, in a second study we conducted an additional exploratory analysis of theoretically motivated brain regions in the language network. We demonstrated that data-driven computational models can be successfully integrated into theoretical frameworks to inform and test theories of semantic representation and processing. The findings from our work are discussed in light of future directions for neuroimaging and computational research.
In the last decades, dialectometry has emerged as a new field of dialectology. As this kind of research requires large amounts of data, many dialectometric studies used data from “traditional” dialect atlases (e. g. ALF, AIS, RND) which were collected by investigating representatives of the oldest dialects available in the survey locations (i.e. the so-called NORMs, cf. Chambers & Trudgill 2004: 29). Moreover, these data contained mostly lexical and phonological (and sometimes morphological) variables, while syntactic phenomena are largely absent in traditional atlases. In this paper we would like to present results of a dialectometric study that focuses on three aspects which have not been given much attention in previous research. The first aspect concerns the research area, German-speaking Switzerland. Although it is one of the liveliest and at the same time best researched dialect areas in Central Europe, until recently (cf. Goebl et al. 2013, Scherrer & Stoeckle accepted) there have been very few dialectometric studies in this area (cf. Kelle 2001). The second aspect regards the investigated linguistic level: our analyses are based on syntax data from the Syntactic Atlas of German-speaking Switzerland (‘Syntaktischer Atlas der deutschen Schweiz', SADS; cf. Glaser & Bart 2015) which were collected between 2000 and 2002 in 383 locations German-speaking Switzerland. A special characteristic of this atlas – which leads to the third aspect we will focus on – lies in the large number of informants and their varying socio-demographic backgrounds. Whereas in traditional atlas projects, generally one or two representatives were interviewed at each survey location, in the SADS a total of almost 3200 informants participated in the survey (i. e. on average about 8 speakers per location). This gives us not only the possibility to work with frequency instead of binary data for each location, but more importantly, this setting allows us to include socio-demographic variables into our analyses. In other geographic and sociolinguistic contexts, extralinguistic variables other than geography turned out to be important explanatory factors for dialect variation (cf. Hansen-Morath 2016, Hansen-Morath & Stoeckle 2014). As for German-speaking Switzerland, various studies focusing on single phenomena from the SADS revealed high correlations between syntactic and socio-demographic variation (cf. Stoeckle accepted, Friedli 2012, Richner-Steiner 2011). However, it is still unclear whether this correlation can be observed for aggregated data and what role socio-demographic variables play in explaining syntactic variation. In order to answer these questions, we will pursue a twofold approach. On the one hand, we will create different subsets with respect to socio-demographic variables and perform dialectometric analyses for each of these subsets. A comparison of the results will help to answer the question whether a change in the geographic dialect structuring can be observed. On the other hand, we will perform regression analyses in order to determine the importance of different extralinguistic factors in explaining linguistic variation. Finally, the results will have to be interpreted in the light of the specific Swiss-German diaglossic situation, where (contrary to many other contexts) change toward both dialectal and standard structures can be observed.
Huntington's disease (HD) is a neurodegenerative movement disorder associated with deficits in the processing of emotional stimuli, including alterations in the self-reported subjective experience of emotion when presented with pictures of emotional scenes. The aim of this study was to determine whether individuals with HD, compared to unaffected controls, display abnormal visual scanning of emotionally evocative natural scenes. Using eye-tracking, we recorded eye-movements of 25 HD participants (advanced pre-symptomatic and early symptomatic) and 25 age-matched unaffected control participants during a picture viewing task. Participants viewed pictures of natural scenes associated with different emotions: anger, fear, disgust, happiness, or neutral, and evaluated those pictures on a valence rating scale. Individuals with HD displayed abnormal visual scanning patterns, but did not differ from controls with respect to their valence ratings. Specifically, compared to controls, HD participants spent less time fixating on the pictures and made longer scan paths. This finding highlights the importance of taking visual scanning behavior into account when investigating emotion processing in HD. The visual scanning patterns displayed by HD participants could reflect a heightened, but possibly unfocussed, search for information, and might be linked to attentional deficits or to altered subjective emotional experiences in HD. Another possibility is that HD participants may have found it more difficult than controls to evaluate the emotional valence of the scenes, and the heightened search for information was employed as a compensatory strategy.
The performance of Neural Network (NN)-based language models is steadily improving due to the emergence of new architectures, which are able to learn different natural language characteristics. This paper presents a novel framework, which shows that a significant improvement can be achieved by combining different existing heterogeneous models in a single architecture. This is done through 1) a feature layer, which separately learns different NN-based models and 2) a mixture layer, which merges the resulting model features. In doing so, this architecture benefits from the learning capabilities of each model with no noticeable increase in the number of model parameters or the training time. Extensive experiments conducted on the Penn Treebank (PTB) and the Large Text Compression Benchmark (LTCB) corpus showed a significant reduction of the perplexity when compared to state-of-the-art feedforward as well as recurrent neural network architectures.
Abstract This conversation analytic study explores the nexus of goal orientation and linguistic identity (particularly of L1 English speakers) in ELF interaction. While goal orientation constitutes a hallmark of ELF scholarship, the latter notion has received limited scholarly attention. To address this gap, this study examines a dyadic, institutional interaction between two students in the United States (L1 British English and L1 Arabic) who met for an intercultural conversation assigned by their instructors. In the interest of accomplishing the goal of obtaining intercultural information, the participants did not bring their differences in linguistic identity to the fore of the interaction, while it was also found that the pursuit of an institutional goal can at times manifest itself as orientations to linguistic norms. The examination of these overt orientations to the institutional goal is followed by a study of deviant cases in which the L1 speaking participant appeared to make relevant his superior linguistic identity at first glance. While they could be interpreted as claims of linguistic superiority, a closer look revealed that these instances also reflected the participants’ cooperative orientations to the emergent communicative needs so as to jointly accomplish the shared goal. Highlighting the problem of presuming a correlation between interactional behavior and linguistic identity, the study suggests the need for the analyst to withhold his/her preconceptions about interactants’ identities. Further research involving diverse groups of interactants, including L1 speakers of English, is needed to contribute to the recent theoretical developments that characterize ELF interaction as situated within diverse linguacultural ecologies and power dynamics.
Since exposure therapy for anxiety disorders incorporates extinction of contextual anxiety, relapses may be due to reinstatement processes. Animal research demonstrated more stable extinction memory and less anxiety relapse due to vagus nerve stimulation (VNS). We report a valid human three-day context conditioning, extinction and return of anxiety protocol, which we used to examine effects of transcutaneous VNS (tVNS). Seventy-five healthy participants received electric stimuli (unconditioned stimuli, US) during acquisition (Day1) when guided through one virtual office (anxiety context, CTX+) but never in another (safety context, CTX-). During extinction (Day2), participants received tVNS, sham, or no stimulation and revisited both contexts without US delivery. On Day3, participants received three USs for reinstatement followed by a test phase. Successful acquisition, i.e. startle potentiation, lower valence, higher arousal, anxiety and contingency ratings in CTX+ versus CTX-, the disappearance of these effects during extinction, and successful reinstatement indicate validity of this paradigm. Interestingly, we found generalized reinstatement in startle responses and differential reinstatement in valence ratings. Altogether, our protocol serves as valid conditioning paradigm. Reinstatement effects indicate different anxiety networks underlying physiological versus verbal responses. However, tVNS did neither affect extinction nor reinstatement, which asks for validation and improvement of the stimulation protocol.
We present a method for automatically converting the Dutch Lassy Small treebank, a phrasal dependency treebank, to UD. All of the information required to produce accurate UD annotation appears to be available in the underlying annotation. However, we also note that the close connection between POS-tags and dependency labels that is present in UD is missing in the Lassy treebanks. As a consequence, annotation decisions in the Dutch<br/>data for such phenomena as nominalization<br/>and clausal complements of prepositions<br/>seem to differ to some extent from comparable data in English and German. Because the conversion is automatic, we can now also compare three state-of-theart dependency parsers trained on UD Lassy Small with Alpino, a hybrid Dutch parser which produces output that is compatible with the original Lassy annotations.