Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
16504 papers
In search of causative factors of social anxiety disorder (SAD), classical conditioning has been discussed as a potential trigger mechanism for many years. Recent findings suggest that the social relevance of the unconditioned stimulus (US) might play a major role in learning theories of SAD. Thus, this study applied a social conditioning paradigm with disorder-relevant US to examine the electrocortical correlates of affective learning. Twenty-four high socially anxious (HSA) and 23 age- and gender-matched low socially anxious (LSA) subjects were conditioned to 3 different faces flickering at a frequency of 15 Hz which were paired with auditory insults, compliments or neutral comments (US). The face-evoked electrocortical response was measured via steady-state visually evoked potentials and subjective measures of valence and arousal were obtained. Results revealed a significant interaction of social anxiety and conditioning, with LSA showing highest cortical activity to faces paired with insults and lowest activity to faces paired with compliments, while HSA did not differentiate between faces. No group differences were discovered in the affective ratings. The findings indicate a potentially impaired ability of HSA to discriminate between relevant and irrelevant social stimuli, which may constitute a perpetuating factor of SAD.
BACKGROUND: The standard clinical acquisition for left ventricular functional parameter analysis with cardiovascular magnetic resonance (CMR) uses a multi-breathhold multi-slice segmented balanced SSFP sequence. Performing multiple long breathholds in quick succession for ventricular coverage in the short-axis orientation can lead to fatigue and is challenging in patients with severe cardiac or respiratory disorders. This study combines the encoding efficiency of a six-fold undersampled 3D stack of spirals balanced SSFP sequence with 3D through-time spiral GRAPPA parallel imaging reconstruction. This 3D spiral method requires only one breathhold to collect the dynamic data. METHODS: Ten healthy volunteers were recruited for imaging at 3 T. The 3D spiral technique was compared against 2D imaging in terms of systolic left ventricular functional parameter values (Bland-Altman plots), total scan time (Welch's t-test) and qualitative image rating scores (Wilcoxon signed-rank test). RESULTS: Systolic left ventricular functional values were not significantly different (i.e. 3D-2D) between the methods. The 95% confidence interval for ejection fraction was -0.1 ± 1.6% (mean ± 1.96*SD). The total scan time for the 3D spiral technique was 48 s, which included one breathhold with an average duration of 14 s for the dynamic scan, plus 34 s to collect the calibration data under free-breathing conditions. The 2D method required an average of 5 min 40s for the same coverage of the left ventricle. The difference between 3D and 2D image rating scores was significantly different from zero (Wilcoxon signed-rank test, p < 0.05); however, the scores were at least 3 (i.e. average) or higher for 3D spiral imaging. CONCLUSION: The 3D through-time spiral GRAPPA method demonstrated equivalent systolic left ventricular functional parameter values, required significantly less total scan time and yielded acceptable image quality with respect to the 2D segmented multi-breathhold standard in this study. Moreover, the 3D spiral technique used just one breathhold for dynamic imaging, which is anticipated to reduce patient fatigue as part of the complete cardiac examination in future studies that include patients.
Bilingual Base Noun Phrase (BaseNP) extraction is one of the key tasks of Natural Language Processing (NLP). This task is more challenging for the pair of English-Vietnamese due to the lack of available Vietnamese language resources such as treebanks, part-of-speech taggers, and parsers. In this paper, we propose a combination model that uses language characteristics based on statistics and the projection method to extract BaseNP correspondences from a bilingual corpus. The language characteristics used in this model include the word segmentation, word order and word classification [1]. Our model overcomes not only the lack of resources of Vietnamese, but also improves the performance of miss-alignment, null-alignment, overlap and conflict projection of the existing methods. The proposed model can be easily applied to other language pairs. Experiment on 66,646 pairs of sentences in the English-Vietnamese bilingual corpus shows that our proposed model is very satisfactory.
In this paper, we investigate the relationship between the number of frames, the length and the frequency of verbs in Hungarian, based on data gathered from the short business news sub-corpus of the Szeged Dependency Treebank. We hypothesize that the most frequent verbs have the most valency frames, the shortest verbs are the most frequent ones and the shortest verbs have the most valency frames. We extend our investigations to full valency frames as well, where arguments and adjuncts are treated in the same way. We also compare the valency frames gathered from the treebank to those found in a valency lexicon constructed on a theoretical basis. Our results support the above hypotheses in the case of valency frames and full valency frames as well.
We explore the extent to which highresource manual annotations such as treebanks are necessary for the task of semantic role labeling (SRL). We examine how performance changes without syntactic supervision, comparing both joint and pipelined methods to induce latent syntax. This work highlights a new application of unsupervised grammar induction and demonstrates several approaches to SRL in the absence of supervised syntax. Our best models obtain competitive results in the high-resource setting and state-ofthe-art results in the low resource setting, reaching 72.48% F1 averaged across languages. We release our code for this work along with a larger toolkit for specifying arbitrary graphical structure. 1
We introduce three techniques for improving constituent parsing for morphologically rich languages. We propose a novel approach to automatically find an optimal preterminal set by clustering morphological feature values and we conduct experiments with enhanced lexical models and feature engineering for rerankers. These techniques are specially designed for morphologically rich languages (but they are language-agnostic). We report empirical results on the treebanks of five morphologically rich languages and show a considerable improvement in accuracy and in parsing speed as well.
There are all too few examples of good urban governance in the ‘South’. One city which improved its performance dramatically after 1992 was Bogotá, the capital of Colombia. It joined the ranks of exemplar cities and its former mayors toured the world advertising this ‘miracle’. Unfortunately, after 2008, the city’s administration became mired in corruption and its image ratings have dived. The current administration has so far failed to revive trust in the city’s governance. Based on interviews with key personalities in the city, this paper examines the causes of Bogotá’s recovery and its recent relapse. Bogotá’s experience is useful to students of urban governance in showing not only how a city in the ‘South’ can improve its performance but also that any improvement is fragile. A decent working relationship between technocrats and politicians is critical in guaranteeing both public support and progress in implementing major public works.
OBJECTIVE: To investigate the perception of facial asymmetry in young adults to identify the amounts of chin asymmetry that can be regarded as normal and may benefit from correction. MATERIALS AND METHODS: Three-dimensional (3D) images of 56 individuals of mixed ethnicity were obtained and used to produce average 3D images of male and female faces. Distortion was then applied to these average faces using a 3D graphics package to simulate different amounts of chin point asymmetry. Five observer groups (lay individuals, dental students, dental care professionals, dental practitioners, and orthodontists) assessed timed presentations of 3D images, rating them as "normal," "acceptable," or "would benefit from correction." Time-to-event analysis was used to assess the level of chin asymmetry perceived as normal and beneficial for correction for each group. RESULTS: The factors influencing the perception of facial asymmetry were the degree of asymmetry and the observer group. Direction of the asymmetry and gender of the assessed individual did not affect the perception of asymmetry, except in the 4- to 6-mm distortion range. The gender of the observer had no influence on perception. There were statistically significant differences in the amounts of asymmetry that the laypeople and orthodontists considered to be normal (5.6 ± 2.7 mm and 3.6 ± 1.5 mm, respectively; P <.001) and felt would benefit from surgical correction (11.8 ± 4.0 mm and 9.7 ± 3.0 mm, respectively; P =.001). CONCLUSIONS: Perception of asymmetry is affected by the amount of asymmetry and the observer group, with orthodontists being more critical.
This paper presents several techniques for managing ambiguity in LFG parsing of Wolof, a less-resourced Niger-Congo language. Ambiguity is pervasive in Wolof and This raises a number of theoretical and practical issues for managing ambiguity associated with different objectives. From a theoretical perspective, the main aim is to design a large-scale grammar for Wolof that is able to make linguistically motivated disambiguation decisions, and to find appropriate ways of controlling ambiguity at important interface representations. The practical aim is to develop disambiguation strategies to improve the performance of the grammar in terms of efficiency, robustness and coverage.To achieve these goals, different avenues are explored to manage ambiguity in the Wolof grammar, including the formal encoding of noun class indeterminacy, lexical specifications, the use of Constraint Grammar models (Karlsson 1990) for morphological disambiguation, the application of the c-structure pruning mechanism (Cahill et al. 2007, 2008; Crouch et al. 2013), and the use of optimality marks for preferences (Frank et al. 1998, 2001). The parsing system is further controlled by packing ambiguities. In addition, discriminant-based techniques for parse disambiguation (Rosén et al. 2007) are applied for treebanking purposes.
This chapter explores the challenges of developing the field of Latin Computational Linguistics. Computational Linguistics aims at designing, implementing, and applying computational models for natural languages. A large part of Computational Linguistics research has been developed for English, or at least tested on this language. A crucial aspect of a fruitful exchange between the disciplines of Latin Linguistics and Computational Linguistics concerns the way Latin texts are collected, accessed, and investigated for linguistic analyses. So, any attempt into Latin Computational Linguistics is likely to start from corpora. Annotation provides each word form in a sentence with one or more labels that mark its attributes; for example, a morpho-syntactic annotation would add a 'genitive' tag to puellarum. The chapter advocates the use of corpora in Latin Linguistics by reporting on research based on Latin treebanks, to show their potential for Historical Linguistics research.Keywords: historical corpora; historical languages; historical Linguistics research; Latin Computational Linguistics; morpho-syntactic annotation
While smokers are known to find smoking-related stimuli motivationally salient, the extent to which former smokers do so is largely unknown. In this study, we collected event-related potential (ERP) data from former and never smokers and compared them to a sample of current smokers interested in quitting who completed the same ERP paradigm prior to smoking cessation treatment. All participants (n = 180) attended 1 laboratory session where we recorded dense-array ERPs in response to cigarette-related, pleasant, unpleasant, and neutral pictures and where we collected valence and arousal ratings of the pictures. We identified 3 spatial and temporal regions of interest, corresponding to the P1 (120-132 ms), early posterior negativity (EPN; 244-316 ms), and late positive potential (LPP; 384-800 ms) ERP components. We found that all participants produced larger P1 responses to cigarette-related pictures compared to the other picture categories. With the EPN component, we found that, similar to pleasant and unpleasant pictures, cigarette-related pictures attracted early attentional resources, regardless of smoking status. Both former and never smokers produced reduced LPP responses to cigarette-related and pleasant pictures compared to current smokers. Current smokers rated the cigarette-related pictures as being more pleasant and arousing than the former and never smokers. The LPP and picture-rating results suggest that former smokers, like never smokers, do not find cigarette-related stimuli to be as motivationally salient as current smokers.
status: Published
Less-configurational languages such as German often show not just morphological variation but also free word order and nonprojectivity. German is not exceptional in this regard, as other morphologically-rich languages such as Czech, Tamil or Greek, offer similar challenges that make context-free constituent parsing less attractive. Advocates of dependency parsing have long pointed out that the free(r) word order and non-projective phenomena are handled in a more straightforward way by dependency parsing. How-ever, certain other phenomena in language, such as gapping, ellipses or verbless sentences, are difficult to handle in a dependency formalism. In this paper, we show that parsing of discontinuous constituents can be achieved using easy-first parsing with online reordering, an approach that previously has only been used for dependencies, and that the approach yields very fast parsing with reasonably accurate results that are close to the state of the art, surpassing existing results that use treebank grammars. We also investigate the question whether phenomena where dependency representations may be problematic – in particular, verbless clauses – can be handled by this model. 1
Dependency parsing is a core task in NLP, and it is widely used by many applications such as information extraction, question answering, and machine translation. In the era of social media, a big challenge is that parsers trained on traditional newswire corpora typically suffer from the domain mismatch issue, and thus perform poorly on social media data. We present a new GFL/FUDG-annotated Chinese treebank with more than 18K tokens from Sina Weibo (the Chinese equivalent of Twitter). We formulate the dependency parsing problem as many small and parallelizable arc prediction tasks: for each task, we use a programmable probabilistic firstorder logic to infer the dependency arc of a token in the sentence. In experiments, we show that the proposed model outperforms an off-the-shelf Stanford Chinese parser, as well as a strong MaltParser baseline that is trained on the same in-domain data.
Discourse parsing is a challenging task and plays a critical role in discourse analysis. In this paper, we focus on labeling full argument spans of discourse connectives in the Penn Discourse Treebank (PDTB). Previous studies cast this task as a linear tagging or subtree extraction problem. In this paper, we propose a novel constituent-based approach to argument labeling, which integrates the advantages of both linear tagging and subtree extraction. In particular, the proposed approach unifies intra-and intersentence cases by treating the immediately preceding sentence as a special constituent. Besides, a joint inference mechanism is introduced to incorporate global information across arguments into our constituent-based approach via integer linear programming. Evaluation on PDT-B shows significant performance improvements of our constituent-based approach over the best state-of-the-art system. It also shows the effectiveness of our joint inference mechanism in modeling global information across arguments.
We present novel computational experiments using William Labov’s theory of narrative analysis. We describe his six elements of narrative structure and construct a new corpus based on his most recent work on narrative. Using this corpus, we explore the correspondence between Labovs elements of narrative structure and the implicit discourse relations of the Penn Discourse Treebank, and we construct a mapping between the elements of narrative structure and the discourse relation classes of the PDTB. We present first experiments on detecting Complicating Actions, the most common of the elements of narrative structure, achieving an f-score of 71.55. We compare the contributions of features derived from narrative analysis, such as the length of clauses and the tenses of main verbs, with those of features drawn from work on detecting implicit discourse relations. Finally, we suggest directions for future research on narrative structure, such as applications in assessing text quality and in narrative generation.
This paper designed a human-computer interaction graphical syntax tagging system based on the Sentence Pattern Structure.It's designed directly to support the Treebank constructing and deeply research base on the Sentence Pattern Structure.With the constraint of sentence pattern system and the supprot of lexical knowledge database,the hierarchy and word type tags of results are normalized effectively.To a certain extent,the consistency and quality of syntax results can be ensured.This paper illustrated the creative mode and experience of this system from the perspective of practice.
The increasing diversity of languages used on the web introduces a new level of complexity to Information Retrieval (IR) systems. We can no longer assume that textual content is written in one language or even the same language family. In this paper, we demonstrate how to build massive multilingual annotators with minimal human expertise and intervention. We describe a system that builds Named Entity Recognition (NER) annotators for 40 major languages using Wikipedia and Freebase. Our approach does not require NER human annotated datasets or language specific resources like treebanks, parallel corpora, and orthographic rules. The novelty of approach lies therein - using only language agnostic techniques, while achieving competitive performance. Our method learns distributed word representations (word embeddings) which encode semantic and syntactic features of words in each language. Then, we automatically generate datasets from Wikipedia link structure and Freebase attributes. Finally, we apply two preprocessing stages (oversampling and exact surface form matching) which do not require any linguistic expertise. Our evaluation is two fold: First, we demonstrate the system performance on human annotated datasets. Second, for languages where no gold-standard benchmarks are available, we propose a new method, distant evaluation, based on statistical machine translation.
Processing unpleasant affective cues induces elevated momentary symptom reports, especially in persons with high levels of symptom reporting in daily life. The present study aimed to examine whether applying an emotion regulation strategy, i.e. affect labeling, can inhibit these emotion influences on symptom reporting. Student participants (N = 61) with varying levels of habitual symptom reporting completed six picture viewing trials of homogeneous valence (three pleasant, three unpleasant) under three conditions: merely viewing, emotional labeling, or content (non-emotional) labeling. Affect ratings and symptom reports were collected after each trial. Participants completed a motor inhibition task and self-control questionnaires as indices of their inhibitory capacities. Heart rate variability was also measured. Labeling, either emotional or non-emotional, significantly reduced experienced affect, as well as the elevated symptoms reports observed after unpleasant picture viewing. These labeling effects became more pronounced with increasing levels of habitual symptom reporting, suggesting a moderating role of the latter variable, but did not correlate with any index of general inhibitory capacity. Our findings suggest that using an emotion regulation strategy, such as labeling emotional stimuli, can reverse the effects of unpleasant stimuli on symptom reporting and that such strategies can be especially beneficial for individuals suffering from medically unexplained physical symptoms.
A phrase dependency treebank(PDT)integrating phrase structure grammar and dependency grammar is proposed and elaborated to cater for translation studies.The construction of DUT Parallel Chinese-English PDT(DUT-CEPDT)is reported.PDT favors flat structures and the dependency is based on semantics rather than syntactic functions,which differs from the mainstream dependency analysis that favors binary branching.The raw texts of DUT-CEPDT are Chinese government work reports and White Papers and their official English translation.First of all,after word segmentation and part of speech(POS)tagging,Chinese PDT and English PDT are constructed manually with the aid of LingTreeConstructor,a tool tailored for linguists.Then,node alignment, which covers translation alignments of words,phrases,clauses up to the whole passage,is proposed instead of traditional word or sentence alignment to provide more translation knowledge.Lastly, semantic roles based on the FrameNet are labeled simultaneously on the aligned nodes of the English and Chinese trees.DUT-CEPDT can serve as a resource and standard of the training and assessment of both human translators and machine translation systems.
In this paper, we report the obtained results of two constituency parsers trained with BulTreeBank, an HPSG-based treebank for Bulgarian. To reduce the data sparsity problem, we propose using the Brown word clustering to do an off-line clustering and map the words in the treebank to create a class-based treebank. e observations show that when the classes outnumber the POS tags, the results are beer. Since this approach adds on another dimension of abstraction (in comparison to the lemma), its coarse-grained representation can be used further for training statistical parsers.
This chapter investigates problems that novice writers, especially learners of English, have in acquiring lexical features of written discourse. It describes features of an interactive writing kit (WordPilot) which assists them to access authentic text, then to transfer patterns they explore there to their own writing. Relevant text corpora and specific lexical databases are made available during the writing process through the mediation of this integrated electronic reference, which incorporates several applications, including a concordancer, dictionary and thesaurus. This learning and production device has been implemented for Cantonese-speaking students in Hong Kong for whom English is a Foreign Language, but the general principles have relevance to writers at any point on the continuum of language proficiency. The chapter also illustrates problems in the presentation of lexical patterns to novice writers of limited English proficiency. As a gateway to text, it provides novice writers opportunities to explore language features, and it assists academic gate keepers in initiating learners into discourse.
Cross-lingual learning has become a popular approach to facilitate the development of resources and tools for low density languages. Its underlying idea is to make use of existing tools and annotations in resource-rich languages to create similar tools and resources for resource-poor languages. Typically, this is achieved by either projecting annotations across parallel corpora, or by transferring models from one or more source languages to a target language. In this paper, we explore a third strategy by using machine translation to create synthetic training data from the original source-side annotations. Specifically, we apply this technique to dependency parsing, using a cross-lingually unified treebank for adequate evaluation. Our approach draws on annotation projection but avoids the use of noisy source-side annotation of an unrelated parallel corpus and instead relies on manual treebank annotation in combination with statistical machine translation, which makes it possible to train fully lexicalized parsers. We show that this approach significantly outperforms delexicalized transfer parsing.% despite the error-prone translation step.
Official releases of the PROIEL treebank of ancient Indo-European languages
This paper is a brief review of three current efforts to provide an open and transparent path to the automated production of event data: • EL:DIABLO: an open, user-friendly modular system for the acquisition and coding of web-based news sources which is intended to allow small research teams to generate customized event data sets with a minimum of effort • PETRARCH: a Python-based event data coder using fully-parsed Penn Treebank input • The Open Event Data Alliance, a new professional organization for the promotion and provision of fully transparent open event data All truth passes through three stages. First, it is ridiculed. Second, it is violently opposed. Third, it is accepted as being self-evident. Arthur Schopenhauer
Official releases of the PROIEL treebank of ancient Indo-European languages
Official releases of the PROIEL treebank of ancient Indo-European languages
OBJECTIVE: The neuropeptide oxytocin is implicated in social processing, and recent research has begun to explore how gender relates to the reported effects. This study examined the effects of oxytocin on social affective perception and learning. METHODS: Forty-seven male and female participants made judgments of faces during two different tasks, after being randomized to either double-blinded intranasal oxytocin or placebo. In the first task, "unseen" affective stimuli were presented in a continuous flash suppression paradigm, and participants evaluated faces paired with these stimuli on dimensions of competence, trustworthiness, and warmth. In the second task, participants learned affective associations between neutral faces and affective acts through a gossip learning procedure and later made affective ratings of the faces. RESULTS: In both tasks, we found that gender moderated the effect of oxytocin, such that male participants in the oxytocin condition rated faces more negatively, compared with placebo. The opposite pattern of findings emerged for female participants: they rated faces more positively in the oxytocin condition, compared with placebo. CONCLUSIONS: These findings contribute to a small but growing body of research demonstrating differential effects of oxytocin in men and women.
The objective of this paper is to provide an overview of the CDT annotation design with special emphasis on the modelling of the interface between the syntactic level and two other linguistic levels, viz. morphology and discourse. In connection with the description of NP annotation we present the fundamentals of how CDT is marked up with semantic relations in accordance with the dependency principles governing the annotation on the other levels of CDT. Specifically, focus will be on how Generative Lexicon (GL) theory has been incorporated into the unitary theoretical dependency framework of CDT. An annotation scheme for lexical semantics has been designed so as to account for the lexico-semantic structure of complex NPs, and the four GL qualia also appear in some of the CDT discourse relation labels as a description of parallel semantic relations at this level.
We present a novel toolkit that implements the long short-term memory (LSTM) neural network concept for language modeling. The main goal is to provide a software which is easy to use, and which allows fast training of standard recurrent and LSTM neural network language models. The toolkit obtains state-of-the-art performance on the standard Treebank corpus. To reduce the training time, BLAS and related libraries are supported, and it is possible to evaluate multiple word sequences in parallel. In addition, arbitrary word classes can be used to speed up the computation in case of large vocabulary sizes. Finally, the software allows easy integration with SRILM, and it supports direct decoding and rescoring of HTK lattices. The toolkit is available for download under an open source license.
Methylphenidate mainly enhances dopamine neurotransmission whereas 3,4-methylenedioxymethamphetamine (MDMA, "ecstasy") mainly enhances serotonin neurotransmission. However, both drugs also induce a weaker increase of cerebral noradrenaline exerting sympathomimetic properties. Dopaminergic psychostimulants are reported to increase sexual drive, while serotonergic drugs typically impair sexual arousal and functions. Additionally, serotonin has also been shown to modulate cognitive perception of romantic relationships. Whether methylphenidate or MDMA alter sexual arousal or cognitive appraisal of intimate relationships is not known. Thus, we evaluated effects of methylphenidate (40 mg) and MDMA (75 mg) on subjective sexual arousal by viewing erotic pictures and on perception of romantic relationships of unknown couples in a double-blind, randomized, placebo-controlled, crossover study in 30 healthy adults. Methylphenidate, but not MDMA, increased ratings of sexual arousal for explicit sexual stimuli. The participants also sought to increase the presentation time of implicit sexual stimuli by button press after methylphenidate treatment compared with placebo. Plasma levels of testosterone, estrogen, and progesterone were not associated with sexual arousal ratings. Neither MDMA nor methylphenidate altered appraisal of romantic relationships of others. The findings indicate that pharmacological stimulation of dopaminergic but not of serotonergic neurotransmission enhances sexual drive. Whether sexual perception is altered in subjects misusing methylphenidate e.g., for cognitive enhancement or as treatment for attention deficit hyperactivity disorder is of high interest and warrants further investigation.
It is well established that categorising the emotional content of facial expressions may differ depending on contextual information. Whether this malleability is observed in the auditory domain and in genuine emotion expressions is poorly explored. We examined the perception of authentic laughter and crying in the context of happy, neutral and sad facial expressions. Participants rated the vocalisations on separate unipolar scales of happiness and sadness and on arousal. Although they were instructed to focus exclusively on the vocalisations, consistent context effects were found: For both laughter and crying, emotion judgements were shifted towards the information expressed by the face. These modulations were independent of response latencies and were larger for more emotionally ambiguous vocalisations. No effects of context were found for arousal ratings. These findings suggest that the automatic encoding of contextual information during emotion perception generalises across modalities, to purely non-verbal vocalisations, and is not confined to acted expressions.
Measuring the similarity of words is important in accurately representing and comparing documents, and thus improves the results of many natural language processing (NLP) tasks. The NLP community has proposed various measurements based on WordNet, a lexical database that contains relationships between many pairs of words. Recently, a number of techniques have been proposed to address software engineering issues such as code search and fault localization that require understanding natural language documents, and a measure of word similarity could improve their results. However, WordNet only contains information about words senses in general-purpose conversation, which often differ from word senses in a software-engineering context, and the software-specific word similarity resources that have been developed rely on data sources containing only a limited range of words and word uses.
BACKGROUND: Patients with schizophrenia often experience problems regulating their emotions. Non-affected relatives show similar difficulties, although to a lesser extent, and the neural basis of such difficulties remains to be elucidated. In the current paper we investigated whether schizophrenia patients, non-affected siblings and healthy controls (HC) exhibit differences in brain activation during emotion regulation. METHODS: All subjects (n = 20 per group) performed an emotion regulation task while they were in an fMRI scanner. The task contained two experimental conditions for the down-regulation of emotions (reappraise and suppress), in which IAPS pictures were used to generate a negative affect. We also assessed whether the groups differed in emotion regulation strategies used in daily life by means of the emotion regulation questionnaire (ERQ). RESULTS: Though the overall negative affect was higher for patients as well as for siblings compared to HC for all conditions, all groups reported decreased negative affect after both regulation conditions. Nonetheless, neuroimaging results showed hypoactivation relative to HC in VLPFC, insula, middle temporal gyrus, caudate and thalamus for patients when reappraising negative pictures. In siblings, the same pattern was evident as in patients, but only in cortical areas. CONCLUSIONS: Given that all groups performed similarly on the emotion regulation task, but differed in overall negative affect ratings and brain activation, our findings suggest reduced levels of emotion regulation processing in neural circuits in patients with schizophrenia. Notably, this also holds for siblings, albeit to a lesser extent, indicating that it may be part and parcel of a vulnerability for psychosis.
After a period when the focus was essentially on mental architecture, the cognitive sciences are increasingly integrating the social dimension. The rise of a cognitive sociolinguistics is part of this trend. The article argues that this process requires a re-evaluation of some entrenched positions in linguistics: those that see linguistic norms as antithetical to a descriptive and variational linguistics. Once such a re-evaluation has taken place, however, the social recontextualization of cognition will enable linguistics (including sociolinguistics as an integral part), to eliminate the cracks in the foundations that were the result of suppressing the sociocultural underpinnings of linguistic facts. Structuralism, cognitivism and social constructionism introduced new and necessary distinctions, but in their strong forms they all turned into unnecessary divides. The article tries to show that an evolutionary account can reintegrate the opposed fragments into a whole picture that puts each of them in their ‘ecological position’ with respect to each other. Empirical usage facts should be seen in the context of operational norms in relation to which actual linguistic choices represent adaptations. Variational patterns should be seen in the context of structural categories without which there would be only ‘differences’ rather than variation. And emergence, individual choice, and flux should be seen in the context of the individual’s dependence on lineages of community practice sustained by collective norms.
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OBJECTIVE: This study investigated how lexical effects account for word recognition in monolinguals versus bilinguals. DESIGN: Listener-specific error rate and familiarity rating of 200 NU-6 words were obtained. Lexical data (normative familiarity, frequency of occurrence, neighborhood density, and frequency of neighborhood competitors) for these words were obtained from the Hoosier mental lexicon. STUDY SAMPLE: Participants included 10 monolinguals and three groups of 10 bilinguals differing mainly in age of acquisition and length of schooling/working in English. RESULTS: Lexical effects were minimal for monolinguals' word recognition. Listener-specific familiarity rating correlated to error rate better than the Hoosier normative rating. Frequency of occurrence was the most significant lexical variable in accounting for bilinguals' measures and its effect was the greatest on bilinguals foreign born and educated. Age of English acquisition tended to affect familiarity rating, whereas length of schooling/working in English tended to affect error rate. CONCLUSIONS: Frequency of word occurrence significantly affects bilinguals' familiarity rating and error rate of the NU-6 words. Listener-specific familiarity rating should be obtained to best predict error rate on the test.
Sentiment analysis has now become a popular research problem to tackle in NLP field. However, there are very few researches conducted on sentiment analysis for Chinese. Progress is held back due to lack of large and labelled corpus and powerful models. To remedy this deficiency, we build a Chinese Sentiment Treebank over social data. It concludes 13550 labeled sentences which are from movie reviews. Furthermore, we introduce a novel Recursive Neural Deep Model (RNDM) to predict sentiment label based on recursive deep learning. We consider the problem of classifying one sentence by overall sentiment, determining a review is positive or negative. On predicting sentiment label at sentence level, our model outperforms other commonly used baselines, such as Naïve Bayes, Maximum Entropy and SVM, by a large margin.
Although exposure therapy is an effective treatment for anxiety disorders, fear sometimes returns following successful therapy. The Rescorla–Wagner model predicts that presenting two fear-provoking stimuli simultaneously (compound extinction) will maximize learning during exposure and reduce the likelihood of relapse. Participants were presented with either single extinction trials only or single extinction trials followed by compound extinction trials. In addition, participants within each extinction group were randomized to caffeine or placebo ingestion prior to extinction to investigate the mechanism by which compound extinction may maximize learning (enhanced associative change or enhanced responding). Participants presented with compound trials demonstrated significantly less fear responding at spontaneous recovery compared with participants who received single extinction trials only. Ingestion of caffeine also provided some protection from spontaneous recovery (as measured by valence ratings). At the reinstatement test, only compound extinction trials predicted less fear responding; caffeine ingestion prior to extinction did not attenuate reinstatement effects.
Our first impression of others is highly influenced by their facial appearance. However, the perception and evaluation of faces is not only guided by internal features such as facial expressions, but also highly dependent on contextual information such as secondhand information (verbal descriptions) about the target person. To investigate the time course of contextual influences on cortical face processing, event-related brain potentials were investigated in response to neutral faces, which were preceded by brief verbal descriptions containing cues of affective valence (negative, neutral, positive) and self-reference (self-related vs. other-related). ERP analysis demonstrated that early and late stages of face processing are enhanced by negative and positive as well as self-relevant descriptions, although faces per se did not differ perceptually. Affective ratings of the faces confirmed these findings. Altogether, these results demonstrate for the first time both on an electrocortical and behavioral level how contextual information modifies early visual perception in a top-down manner.
Recent work on Chinese analysis has led to large-scale annotations of the internal structures of words, enabling characterlevel analysis of Chinese syntactic structures. In this paper, we investigate the problem of character-level Chinese dependency parsing, building dependency trees over characters. Character-level information can benefit downstream applications by offering flexible granularities for word segmentation while improving wordlevel dependency parsing accuracies. We present novel adaptations of two major shift-reduce dependency parsing algorithms to character-level parsing. Experimental results on the Chinese Treebank demonstrate improved performances over word-based parsing methods.
Prior research suggests that repeatedly approaching or avoiding a certain stimulus changes the liking of this stimulus. We investigated whether these effects of approach and avoidance training occur also when participants do not perform these actions but are merely instructed about the stimulus-action contingencies. Stimulus evaluations were registered using both implicit (Implicit Association Test and evaluative priming) and explicit measures (valence ratings). Instruction-based approach-avoidance effects were observed for relatively neutral fictitious social groups (i.e., Niffites and Luupites), but not for clearly valenced well-known social groups (i.e., Blacks and Whites). We conclude that instructions to approach or avoid stimuli can provide sufficient bases for establishing both implicit and explicit evaluations of novel stimuli and discuss several possible reasons for why similar instruction-based approach-avoidance effects were not found for valenced well-known stimuli.
In this paper, we present our work of humor recognition on Twitter, which will facilitate affect and sentimental analysis in the social network. The central question of what makes a tweet (Twitter post) humorous drives us to design humor-related features, which are derived from influential humor theories, linguistic norms, and affective dimensions. Using machine learning techniques, we are able to recognize humorous tweets with high accuracy and F-measure. More importantly, we single out features that contribute to distinguishing non-humorous tweets from humorous tweets, and humorous tweets from other short humorous texts (non-tweets). This proves that humorous tweets possess discernible characteristics that are neither found in plain tweets nor in humorous non-tweets. We believe our novel findings will inform and inspire the burgeoning field of computational humor research in the social media.
Neural substrates underlying the human-pet relationship are largely unknown. We examined fMRI brain activation patterns as mothers viewed images of their own child and dog and an unfamiliar child and dog. There was a common network of brain regions involved in emotion, reward, affiliation, visual processing and social cognition when mothers viewed images of both their child and dog. Viewing images of their child resulted in brain activity in the midbrain (ventral tegmental area/substantia nigra involved in reward/affiliation), while a more posterior cortical brain activation pattern involving fusiform gyrus (visual processing of faces and social cognition) characterized a mother's response to her dog. Mothers also rated images of their child and dog as eliciting similar levels of excitement (arousal) and pleasantness (valence), although the difference in the own vs. unfamiliar child comparison was larger than the own vs. unfamiliar dog comparison for arousal. Valence ratings of their dog were also positively correlated with ratings of the attachment to their dog. Although there are similarities in the perceived emotional experience and brain function associated with the mother-child and mother-dog bond, there are also key differences that may reflect variance in the evolutionary course and function of these relationships.
BACKGROUND: Music can elicit strong emotions and can be remembered in connection with these emotions even decades later. Yet, the brain correlates of episodic memory for highly emotional music compared with less emotional music have not been examined. We therefore used fMRI to investigate brain structures activated by emotional processing of short excerpts of film music successfully retrieved from episodic long-term memory. METHODS: Eighteen non-musicians volunteers were exposed to 60 structurally similar pieces of film music of 10 s length with high arousal ratings and either less positive or very positive valence ratings. Two similar sets of 30 pieces were created. Each of these was presented to half of the participants during the encoding session outside of the scanner, while all stimuli were used during the second recognition session inside the MRI-scanner. During fMRI each stimulation period (10 s) was followed by a 20 s resting period during which participants pressed either the "old" or the "new" button to indicate whether they had heard the piece before. RESULTS: Musical stimuli vs. silence activated the bilateral superior temporal gyrus, right insula, right middle frontal gyrus, bilateral medial frontal gyrus and the left anterior cerebellum. Old pieces led to activation in the left medial dorsal thalamus and left midbrain compared to new pieces. For recognized vs. not recognized old pieces a focused activation in the right inferior frontal gyrus and the left cerebellum was found. Positive pieces activated the left medial frontal gyrus, the left precuneus, the right superior frontal gyrus, the left posterior cingulate, the bilateral middle temporal gyrus, and the left thalamus compared to less positive pieces. CONCLUSION: Specific brain networks related to memory retrieval and emotional processing of symphonic film music were identified. The results imply that the valence of a music piece is important for memory performance and is recognized very fast.