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16504 papers
Neuroscientists have formulated the model of emotional intelligence (EI) based on brain imaging findings of individual differences in EI. The main objective of our study was to operationalize the advantage of high EI individuals in emotional information processing and regulation both at behavioral and neural levels of investigation. We used a self-report measure and a cognitive reappraisal task to demonstrate the role of EI in emotional perception and regulation. Participants saw pictures with negative or neutral captions and shifted (reappraised) from negative context to neutral while we registered brain activation. Behavioral results showed that higher EI participants reported more unpleasant emotions. The Utilization of emotions scores negatively correlated with the valence ratings and the subjective difficulty of reappraisal. In the negative condition, we found activation in hippocampus (HC), parahippocampal gyrus, cingulate cortex, insula and superior temporal lobe. In the neutral context, we found elevated activation in vision-related areas and HC. During reappraisal (negative-neutral) condition, we found activation in the medial frontal gyrus, temporal areas, vision-related regions and in cingulate gyrus. We conclude that higher EI is associated with intensive affective experiences even if emotions are unpleasant. Strong skills in utilizing emotions enable one not to repress negative feelings but to use them as source of information. High EI individuals use effective cognitive processes such as directing attention to relevant details; have advantages in allocation of cognitive resources, in conceptualization of emotional scenes and in building emotional memories; they use visual cues, imagination and executive functions to regulate negative emotions effectively.
We propose a morphology-based method for low-resource (LR) dependency parsing. We train a morphological inflector for target LR languages, and apply it to related rich-resource (RR) treebanks to create cross-lingual (xinflected) treebanks that resemble the target LR language. We use such inflected treebanks to train parsers in zero-(training on x-inflected treebanks) and few-shot (training on x-inflected and target language treebanks) setups. The results show that the method sometimes improves the baselines, but not consistently.
Probing has become an important tool for analyzing representations in Natural Language Processing (NLP). For graphical NLP tasks such as dependency parsing, linear probes are currently limited to extracting undirected or unlabeled parse trees which do not capture the full task. This work introduces DepProbe, a linear probe which can extract labeled and directed dependency parse trees from embeddings while using fewer parameters and compute than prior methods. Leveraging its full task coverage and lightweight parametrization, we investigate its predictive power for selecting the best transfer language for training a full biaffine attention parser. Across 13 languages, our proposed method identifies the best source treebank 94% of the time, outperforming competitive baselines and prior work. Finally, we analyze the informativeness of task-specific subspaces in contextual embeddings as well as which benefits a full parser’s non-linear parametrization provides.
Abstract Nouns and verbs are known to differ in the types of grammatical information they encode. What is less well known is the relationship between verbal and nominal coding within and across languages. The equi-complexity hypothesis holds that all languages are equally complex overall, which entails trade-offs between coding in different domains. From a diachronic point of view, this hypothesis implies that the loss and gain of coding in different domains can be expected to balance each other out. In this study, we test to what extent such inverse coevolution can be observed in a sample of 244 languages, using data from a comprehensive cross-linguistic database (Grambank) and applying computational phylogenetic modelling to control for genealogical relatedness. We find evidence for coevolutionary relationships between specific features within nominal and verbal domains on a global scale, but not for overall degrees of grammatical coding between languages. Instead, these amounts of nominal and verbal coding are positively correlated in Sino-Tibetan languages and inversely correlated in Indo-European languages. Our findings indicate that accretion and loss of grammatical information in nominal words and verbs are lineage-specific.
OBJECTIVES: Gender has been suggested to play a critical role in how facial expressions of pain are perceived by others. With the present study we aim to further investigate how gender might impact the decoding of facial expressions of pain, (i) by varying both the gender of the observer as well as the gender of the expressor and (ii) by considering two different aspects of the decoding process, namely intensity decoding and pain recognition. METHODS: In two online-studies, videos of facial expressions of pain as well as of anger and disgust displayed by male and female avatars were presented to male and female participants. In the first study, valence and arousal ratings were assessed (intensity decoding) and in the second study, participants provided intensity ratings for different affective states, that allowed for assessing intensity decoding as well as pain recognition. RESULTS: The gender of the avatar significantly affected the intensity decoding of facial expressions of pain, with higher ratings (arousal, valence, pain intensity) for female compared to male avatars. In contrast, the gender of the observer had no significant impact on intensity decoding. With regard to pain recognition (differentiating pain from anger and disgust), neither the gender of the avatar, nor the gender of the observer had any affect. CONCLUSIONS: Only the gender of the expressor seems to have a substantial impact on the decoding of facial expressions of pain, whereas the gender of the observer seems of less relevance. Reasons for the tendency to see more pain in female faces might be due to psychosocial factors (e.g., gender stereotypes) and require further research.
OBJECTIVE: The purpose of this study was to examine implicit affect toward suicide (i.e., how good/bad suicide is perceived). Some people might be more likely to think about/choose suicide because they perceive it as a good option (to gain relief) relative to available alternatives. METHOD: Implicit affect toward suicide among adults (N = 72) and adolescents (N = 174) with and without suicidal thoughts was examined using first-person (FP) perspective suicide pictures in the affect misattribution procedure (AMP). RESULTS: Suicidal adults' implicit positive affect toward suicide was associated with STB variables, such as explicit valence (r = 0.34) and arousal (r = 0.44) ratings of suicide pictures, and implicit affect differentiated groups above and beyond explicit valence ratings. Contrary to our hypothesis, suicidal participants did not display higher implicit positive affect toward suicide than nonsuicidal participants. However, suicidal participants displayed consistent implicit affect toward different suicide pictures, whereas nonsuicidal participants evaluated some pictures as more pleasant than others (ORs = 1.92-2.27). CONCLUSIONS: Implicit affect toward suicide may relate to STB, but stimuli characteristics (e.g., color) likely influence the accuracy of assessment with the AMP and should be a focus of future research involving this and other implicit measures.
Automatically inferring drivers’ emotions during driver-pedestrian interactions to improve road safety remains a challenge for designing in-vehicle, empathic interfaces. To that end, we carried out a lab-based study using a combination of camera and physiological sensors. We collected participants’ (N=21) real-time, affective (emotion self-reports, heart rate, pupil diameter, skin conductance, and facial temperatures) responses towards non-verbal, pedestrian crossing videos from the Joint Attention for Autonomous Driving (JAAD) dataset. Our findings reveal that positive, non-verbal, pedestrian crossing actions in the videos elicit higher valence ratings from participants, while non-positive actions elicit higher arousal. Different pedestrian crossing actions in the videos also have a significant influence on participants’ physiological signals (heart rate, pupil diameter, skin conductance) and facial temperatures. Our findings provide a first step toward enabling in-car empathic interfaces that draw on behavioural and physiological sensing to in situ infer driver emotions during non-verbal pedestrian interactions.
Purpose: To identify whether exposing medical students to a multimodal curriculum of complementary and alternative medicine (CAM) practices improves their understanding of CAM clinical applications. Background: A significant portion of the U.S. population uses CAM: 34% of adults and 12% of children. Integrative medicine combines the best of conventional and CAM practices. Despite the increased clinical acceptance of CAM, medical education has been lagging, leaving gaps in learners' knowledge. It is important for medical education to keep pace with these developments by educating students and expanding the view of interprofessional care. Methods: A total of 101 first-year medical students at the University of Connecticut participated in a multimodal CAM curriculum. This included (1) an hour lecture, (2) an online research assignment for a continuity patient, and (3) 2 of 4 modules: acupuncture, hypnotherapy, Reiki, or pet therapy. Pre- and post-tests were administered 1 week apart to assess familiarity with CAM practices and the perceived safety and efficacy of each modality. The familiarity was rated on a scale of 0 (not familiar) to 10 (very familiar). Paired Student's t-tests assessed changes from pre- to post-tests at significant levels (p < 0.01). Results: Overall, the mean percentage of students who were able to identify 1 of the top 8 CAM modalities increased from 38% to 49%. The average familiarity rating of CAM significantly increased from 4.7 pretest to 6.6 post-test (p < 0.01). The top 8 CAM modalities, as selected by students, included acupuncture, meditation, yoga, massage, Reiki, chiropractic, hypnosis, and pet therapy. Overall, the familiarity ratings increased for both safety and effectiveness with intermodule variability from pre- and post-test (p < 0.01). Larger increases in effectiveness familiarity were found than of safety familiarity (p < 0.01). Conclusions: This multimodal curriculum significantly improved medical students' familiarity with CAM modalities and the perceived safety and effectiveness of the modalities.
We present substructure distribution projection (SUBDP), a technique that projects a distribution over structures in one domain to another, by projecting substructure distributions separately. Models for the target domain can then be trained, using the projected distributions as soft silver labels. We evaluate SUBDP on zeroshot cross-lingual dependency parsing, taking dependency arcs as substructures: we project the predicted dependency arc distributions in the source language(s) to target language(s), and train a target language parser on the resulting distributions. Given an English treebank as the only source of human supervision, SUBDP achieves better unlabeled attachment score than all prior work on the Universal Dependencies v2.2 (Nivre et al., 2020) test set across eight diverse target languages, as well as the best labeled attachment score on six languages. In addition, SUBDP improves zeroshot cross-lingual dependency parsing with very few (e.g., 50) supervised bitext pairs, across a broader range of target languages.
Recent approaches to Word Sense Disambiguation (WSD) have profited from the enhanced contextualized word representations coming from contemporary Large Language Models (LLMs).This advancement is accompanied by a renewed interest in WSD applications in Humanities research, where the lack of suitable, specific WSD-annotated resources is a hurdle in developing ad-hoc WSD systems.Because they can exploit sentential context, LLMs are particularly suited for disambiguation tasks.Still, the application of LLMs is often limited to linear classifiers trained on top of the LLM architecture.In this paper, we follow recent developments in non-parametric learning and show how LLMs can be efficiently fine-tuned to achieve strong few-shot performance on WSD for historical languages (English and Dutch, date range: 1450-1950).We test our hypothesis using (i) a large, general evaluation set taken from large lexical databases, and (ii) a small real-world scenario involving an ad-hoc WSD task.Moreover, this paper marks the release of GysBERT, a LLM for historical Dutch.
Land Use / Land Cover (LULC) classification is considered one of the basic tasks that decision makers and map makers rely on to evaluate the infrastructure, using different types of satellite data, despite the large spectral difference or overlap in the spectra in the same land cover in addition to the problem of aberration and the degree of inclination of the images that may be negatively affect rating performance. The main objective of this study is to develop a working method for classifying the land cover using high-resolution satellite images using object based method. Maximum likelihood pixel based supervised as well as object approaches were examined on QuickBird satellite image in Karbala, Iraq. This study illustrated that use of textural data during the object image classification approach can considerably enhance land use classification performance. Moreover, the results showed higher overall accuracy (86.02%) in the o object based method than pixel based (79.06%) in urban extractions. The object based performed much more capabilities than pixel based.
Associative learning and memory mechanisms drive interoceptive signaling along the gut-brain axis, thus shaping affective-emotional reactions and behavior. Specifically, learning to predict potentially harmful, visceral pain is assumed to succeed within very few trials. However, the temporal dynamics of cerebellar and cerebral fMRI signal changes underlying early acquisition and extinction of learned fear signals and the concomitant evolvement of safety learning remain incompletely understood. 3 T fMRI data of healthy individuals from three studies were uniformly processed across the whole brain and the cerebellum. All studies employed differential delay conditioning (N = 94) with one visual cue (CS+) being repeatedly paired with visceral pain as unconditioned stimulus (US) while a second cue remained unpaired (CS-). During subsequent extinction (N = 51), all CS were presented without US. Behavioral results revealed increased CS+-aversiveness and CS--pleasantness after conditioning and diminished valence ratings for both CS following extinction. During early acquisition, the CS- induced linearly increasing neural activation in the insula, midcingulate cortex, hippocampus, precuneus as well as cerebral and cerebellar somatomotor regions. The comparison between acquisition and extinction phases yielded a CS--induced linear increase in the posterior cingulate cortex and precuneus during early acquisition, while there was no evidence for linear fMRI signal changes for the CS+ during acquisition and for both CS during extinction. Based on theoretical accounts of discrimination and temporal difference learning, these results suggest a gradual evolvement of learned safety cues that engage emotional arousal, memory, and cortical modulatory networks. As safety signals are presumably more difficult to learn and to discriminate from learned threat cues, the underlying temporal dynamics may reflect enhanced salience and prediction processing as well as increasing demands for attentional resources and the integration of multisensory information. Maladaptive responses to learned safety signals are a clinically relevant phenotype in multiple conditions, including chronic visceral pain, and can be exceptionally resistant to modification or extinction. Through sustained hypervigilance, safety seeking constitutes one key component in pain and stress-related avoidance behavior, calling for future studies targeting the mechanisms of safety learning and extinction to advance current cognitive-behavioral treatment approaches.
In order to achieve deep natural language understanding, syntactic constituent parsing is a vital step, highly demanded by many artificial intelligence systems to process both text and speech. One of the most recent proposals is the use of standard sequence-to-sequence models to perform constituent parsing as a machine translation task, instead of applying task-specific parsers. While they show a competitive performance, these text-to-parse transducers are still lagging behind classic techniques in terms of accuracy, coverage and speed. To close the gap, we here extend the framework of sequence-to-sequence models for constituent parsing, not only by providing a more powerful neural architecture for improving their performance, but also by enlarging their coverage to handle the most complex syntactic phenomena: discontinuous structures. To that end, we design several novel linearizations that can fully produce discontinuities and, for the first time, we test a sequence-to-sequence model on the main discontinuous benchmarks, obtaining competitive results on par with task-specific discontinuous constituent parsers and achieving state-of-the-art scores on the (discontinuous) English Penn Treebank.
This work presents two experiments with the goal of replicating the transferability of dependency parsers and POS taggers trained on closely related languages within the lowresource language family Tupan. The experiments include both zero-shot settings as well as multilingual models. Previous studies have found that even a comparably small treebank from a closely related language will improve sequence labelling considerably in such cases. Results from both POS tagging and dependency parsing confirm previous evidence that the closer the phylogenetic relation between two languages, the better the predictions for sequence labelling tasks get. In many cases, the results are improved if multiple languages from the same family are combined. This suggests that in addition to leveraging similarity between two related languages, the incorporation of multiple languages of the same family might lead to better results in transfer learning for NLP applications.
BACKGROUND AND OBJECTIVES: Individuals are thought to be biased towards approaching positive stimuli and avoiding negative stimuli. Yet, it is unclear whether this general pattern applies to all stimulus classes or whether biases are more specific. We expected significant approach biases towards two types of positive stimuli, appetitive foods and butterflies; and avoidance biases away from two types of negative stimuli, spoiled foods and spiders. METHODS: A touchscreen-based Approach-Avoidance Task (AAT), using hand gestures toward or away from stimuli assessed biases. Questionnaires and image ratings assessed individual differences in stimulus evaluations. RESULTS: Approach biases for butterflies and appetitive foods were found, the latter being strongest towards individually liked foods. There was no avoidance bias for spoiled foods. An avoidance bias for spiders was found in individuals with elevated spider fear. LIMITATIONS: Incomplete counterbalancing precluded direct comparison between both positive and negative stimuli. CONCLUSIONS: Behavioural biases in the touchscreen AAT generally co-vary with individuals' affective evaluation of the stimuli. Approach biases were elicited by positive stimuli independently of whether they were regularly (foods) or rarely (butterflies) approached in everyday life. This may hint towards a tendency to approach positive stimuli regardless of the specific category, whereas avoidance biases may be more stimulus specific.
Focus on language-specific properties with insights from formal minimalist syntax can improve universal dependency (UD) parsing. Such improvements are especially sensitive for low-resource African languages, like Wolof, which have fewer UD treebanks in number and amount of annotations, and fewer contributing annotators. For two different UD parser pipelines, one parser model was trained on the original Wolof treebank, and one was trained on an edited treebank. For each parser pipeline, the accuracy of the edited treebank was higher than the original for both the dependency relations and dependency labels. Accuracy for universal dependency relations improved as much as 2.90%, while accuracy for universal dependency labels increased as much as 3.38%. An annotation scheme that better fits a language's distinct syntax results in better parsing accuracy.
The Penn-Helsinki Parsed Corpus of Early Modern English (PPCEME), a 1.7-millionword treebank that is an important resource for research in syntactic change, has several properties that present potential challenges for NLP technologies. We describe these key features of PPCEME that make it challenging for parsing, including a larger and more varied set of function tags than in the Penn Treebank, and present results for this corpus using a modified version of the Berkeley Neural Parser and the approach to function tag recovery of While this approach to function tag recovery gives reasonable results, it is in some ways inappropriate for span-based parsers. We also present further evidence of the importance of in-domain pretraining for contextualized word representations. The resulting parser will be used to parse Early English Books Online, a 1.5 billion word corpus whose utility for the study of syntactic change will be greatly increased with the addition of accurate parse trees.
The Arabic syntactic diacritics restoration problem is often solved using long short-term memory (LSTM) networks. Handcrafted features are used to augment these LSTM networks or taggers to improve performance. A transformer-based machine learning technique known as bidirectional encoder representations from transformers (BERT) has become the state-of-the-art method for natural language understanding in recent years. In this paper, we present a novel tagger based on BERT models to restore Arabic syntactic diacritics. We formulated the syntactic diacritics restoration as a token sequence classification task similar to named-entity recognition (NER). Using the Arabic TreeBank (ATB) corpus, the developed BERT tagger achieves a 1.36% absolute case-ending error rate (CEER) over other systems.
Abstract Previous studies analysing the differences in emotionality in first and second language suggest that affective content of lexical items is modulated in certain contexts. This paper investigates the differences in valence and arousal ratings for 300 early words, in both oral and written modalities, through speakers’ subjective appraisal of words given by two immersion groups of Spanish late bilinguals (Chinese and European) compared with a group of native speakers. The main goal of our study is to identify the lexical areas where variability occurs, regarding to a set of affective (emotional charge and intensity), grammatical (nouns, adjectives and verbs) and semantic (concreteness) features of words. Our results show that valence is the dimension where the greatest variability is observed between native and bilinguals, although the influence of the independent factors differs considerably. Besides, arousal yields illuminating data regarding the grammatical category of words and differentiation between the groups of participants.
Abstract Discourse parsing has been studied for decades. However, it still remains challenging to utilize discourse parsing for real-world applications because the parsing accuracy degrades significantly on out-of-domain text. In this paper, we report and discuss the effectiveness and limitations of bootstrapping methods for adapting modern BERT-based discourse dependency parsers to out-of-domain text without relying on additional human supervision. Specifically, we investigate self-training, co-training, tri-training, and asymmetric tri-training of graph-based and transition-based discourse dependency parsing models, as well as confidence measures and sample selection criteria in two adaptation scenarios: monologue adaptation between scientific disciplines and dialogue genre adaptation. We also release COVID-19 Discourse Dependency Treebank (COVID19-DTB), a new manually annotated resource for discourse dependency parsing of biomedical paper abstracts. The experimental results show that bootstrapping is significantly and consistently effective for unsupervised domain adaptation of discourse dependency parsing, but the low coverage of accurately predicted pseudo labels is a bottleneck for further improvement. We show that active learning can mitigate this limitation.
The present study investigated the effect of background luminance on the self-reported valence ratings of auditory stimuli, as suggested by some earlier work. A secondary aim was to better characterise the effect of auditory valence on pupillary responses, on which the literature is inconsistent. Participants were randomly presented with sounds of different valence categories (negative, neutral, and positive) obtained from the IADS-E database. At the same time, the background luminance of the computer screen (in blue hue) was manipulated across three levels (i.e., low, medium, and high), with pupillometry confirming the expected strong effect of luminance on pupil size. Participants were asked to rate the valence of the presented sound under these different luminance levels. On a behavioural level, we found evidence for an effect of background luminance on the self-reported valence rating, with generally more positive ratings as background luminance increased. Turning to valence effects on pupil size, irrespective of background luminance, interestingly, we observed that pupils were smallest in the positive valence and the largest in negative valence condition, with neutral valence in between. In sum, the present findings provide evidence concerning a relationship between luminance perception (and hence pupil size) and self-reported valence of auditory stimuli, indicating a possible cross-modal interaction of auditory valence processing with completely task-irrelevant visual background luminance. We furthermore discuss the potential for future applications of the current findings in the clinical field.
Humans have systematic and reliable color preferences. The dominant account of color preference is that individuals like some colors more than others due to the valence of objects that they associate with colors (Ecological Valence Theory). In support of this theory, Palmer and Schloss show that the average valence of objects associated with a color, when weighted (the WAVE), explains up to 80% of the variation in color preference for adults from the United States (US). Here we investigate whether Ecological Valence Theory can account for the color preferences of female and male adults from Saudi Arabia to test how well the theory generalizes across cultures and how well it accounts for sex differences in color preference. We also extend the investigation of EVT by investigating whether abstract concept associations as well as object associations can account for preference. Saudi adults' color preferences, color object and concept associations, and association valence ratings were collected, and the WAVE was computed and correlated with preference ratings. The WAVE accounted for no more than half of the variance in Saudi color preferences, although there was some degree of sex specificity in the relationship of the WAVE and color preference. Adding abstract concept associations did not account for more variance than object associations alone, but the number of abstract concept associations did account for a significant amount of the variance in color preference for females, but not males. The findings converge with other cross-cultural studies in suggesting that the success of EVT in accounting for color preference varies across cultures and indicates that additional factors other than color associations are likely also at play.
Producing easily usable, professional-looking descriptive dictionaries on a shoestring budget in a short time span is a priority for documentation, but hard to achieve. The usual procedure for field dictionaries is to compile a target language-to-contact language lexical database (e.g. Chechen-English, in our case) and generate a contact-to-target dictionary or index from the glosses, This is economical but not always fully satisfactory. Here we describe our solutions to some common problems of field lexicography based on several years’ experience at compiling, editing, and publishing dictionaries of Chechen and Ingush, close sister languages of the Nakh-Daghestanian language family spoken in the central Caucasus. They are languages with large, literate speech communities for which dictionaries need to be sizable, attractive, and linguistically sophisticated and for which two different alphabets are needed, and we hope that our experience in trying to meet these goals will be helpful to linguists embarking on lexical documentation.
In this study, we propose a morpheme-based scheme for Korean dependency parsing and adopt the proposed scheme to Universal Dependencies. We present the linguistic rationale that illustrates the motivation and the necessity of adopting the morpheme-based format, and develop scripts that convert between the original format used by Universal Dependencies and the proposed morpheme-based format automatically. The effectiveness of the proposed format for Korean dependency parsing is then testified by both statistical and neural models, including UDPipe and Stanza, with our carefully constructed morpheme-based word embedding for Korean. morphUD outperforms parsing results for all Korean UD treebanks, and we also present detailed error analyses.
Many studies in the literature attempt recognition of emotions through the use of videos or images, but very few have explored the role that sounds have in evoking emotions. In this study we have devised an experimental protocol for elicitation of emotions by using, separately and jointly, images and sounds from the widely used International Affective Pictures System and International Affective Digital Sounds databases. During the experiments we have recorded the skin conductance and pupillary signals and processed them with the goal of extracting indices linked to the autonomic nervous system, thus revealing specific patterns of behavior depending on the different stimulation modalities. Our results show that skin conductance helps discriminate emotions along the arousal dimension, whereas features derived from the pupillary signal are able to discriminate different states along both valence and arousal dimensions. In particular, the pupillary diameter was found to be significantly greater at increasing arousal and during elicitation of negative emotions in the phases of viewing images and images with sounds. In the sound-only phase, on the other hand, the power calculated in the high and very high frequency bands of the pupillary diameter were significantly greater at higher valence (valence ratings > 5). Clinical relevance- This study demonstrates the ability of physiological signals to assess specific emotional states by providing different activation patterns depending on the stimulation through images, sounds and images with sounds. The approach has high clinical relevance as it could be extended to evaluate mood disorders (e.g. depression, bipolar disorders, or just stress), or to use physiological patterns found for sounds in order to study whether hearing aids can lead to increased emotional perception.
Abstract Facial expressions are indispensable in daily human communication. Previous neuroimaging studies investigating facial expression processing have presented pre-recorded stimuli and lacked live face-to-face interaction. Our paradigm alternated between presentations of real-time model performance and pre-recorded videos of dynamic facial expressions to participants. Simultaneous functional magnetic resonance imaging (fMRI) and facial electromyography activity recordings, as well as post-scan valence and arousal ratings were acquired from 44 female participants. Live facial expressions enhanced the subjective valence and arousal ratings as well as facial muscular responses. Live performances showed greater engagement of the right posterior superior temporal sulcus (pSTS), right inferior frontal gyrus (IFG), right amygdala and right fusiform gyrus, and modulated the effective connectivity within the right mirror neuron system (IFG, pSTS, and right inferior parietal lobule). A support vector machine algorithm could classify multivoxel activation patterns in brain regions involved in dynamic facial expression processing in the mentalizing networks (anterior and posterior cingulate cortex). These results indicate that live social interaction modulates the activity and connectivity of the right mirror neuron system and enhances spontaneous mimicry, further facilitating emotional contagion. Highlights We alternately presented real-time and pre-recorded dynamic facial expressions. Live facial expressions enhanced emotion contagion and spontaneous facial mimicry. Live conditions modulated mirror neuron system activity and effective connectivity. The mentalizing network showed distinctive multivoxel patterns in live conditions. The results support the validity of second-person design in social neuroscience.
Huge amount of data is being produced every second for microblogs, different content sharing sites, and social networking. Sentimental classification is a tool that is frequently used to identify underlying opinions and sentiments present in the text and classifying them. It is widely used for social media platforms to find user's sentiments about a particular topic or product. Capturing, assembling, and analyzing sentiments has been challenge for researchers. To handle these challenges, we present a comparative sentiment analysis study in which we used the fine-grained Stanford Sentiment Treebank (SST) dataset, based on 215,154 exclusive texts of different lengths that are manually labeled. We present comparative sentiment analysis to solve the fine-grained sentiment classification problem. The proposed approach takes start by pre-processing the data and then apply eight machine-learning algorithms for the sentiment classification namely Support Vector Machine (SVM), Logistic Regression (LR), Neural Networks (NN), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbor (KNN), Adaboost and Naïve Bayes (NB). On the basis of results obtained the accuracy, precision, recall and F1-score were calculated to draw a comparison between the classification approaches being used.
Introduction: Fear is associated with perceptual biases. People who are afraid of spiders perceive spiders as larger than people without this fear. It is yet unclear, however, whether this effect can be influenced by using implicit (non-deliberate) emotion regulation (ER) processes and explicit (deliberate) ER strategies, such as reappraisal and suppression. Method: This study examined the link between implicit and explicit ER and size estimation among women afraid of spiders. After performing an implicit ER (cognitive control) task, participants rated the size and valence of spiders, wasps and butterflies shown in pictures. Participants' tendency to use reappraisal and suppression was assessed using the Emotion Regulation Questionnaire. Results: Results showed no effect of implicit ER on size and valence ratings. A greater tendency to use reappraisal was linked to reduced negative feelings on seeing the pictures of spiders. Greater use of suppression, however, was linked to increased size estimation of the spider stimuli. Discussion: These results highlight the role of ER in perceptual biases and offer avenues for future ER-based treatments for specific phobias.
Misophonia is characterized by excessive aversive reactions to specific “trigger” sounds. Although this disorder is increasingly recognized in the literature, its etiological mechanisms and maintaining factors are currently unclear. Several etiological models propose a role of Pavlovian conditioning, an associative learning process heavily researched in similar fear and anxiety-related disorders. In addition, generalization of learned associations has been noted as a potential causal or contributory factor. Building upon this framework, we hypothesized that Misophonia symptoms arise as a consequence of overgeneralized associative learning, in which aversive responses to a noxious event also occur in response to similar events. Alternatively, heightened discrimination between conditioned threat and safety cues may be present in participants high in Misophonia symptoms, as predicted by associative learning models of Misophonia. This preliminary report ( n = 34) examines auditory generalization learning using self-reported behavioral (i.e., valence and arousal ratings) and EEG alpha power reduction. Participants listened to three sine tones differing in pitch, with one pitch (i.e., CS+) paired with an aversive loud white noise blast, prompting aversive Pavlovian generalization learning. We assessed the extent to which overgeneralization versus heightened discrimination learning is associated with self-reported Misophonia symptoms, by comparing aversive responses to the CS+ and other tones similar in pitch. Behaviorally, all participants learned the contingencies between CS+ and noxious noise, with individuals endorsing elevated Misophonia showing heightened aversive sensitivity to all stimuli, regardless of conditioning and independent of hyperacusis status. Across participants, parieto-occipital EEG alpha-band power reduction was most pronounced in response to the CS+ tone, and this difference was greater in those with self-reported Misophonia symptoms. The current preliminary findings do not support the notion that overgeneralization is a feature of self-reported emotional experience in Misophonia, but that heightened sensitivity and discrimination learning may be present at the neural level.
In recent years, large-scale pre-trained language models (PLMs) have made extraordinary progress in most NLP tasks. But, in the unsupervised POS tagging task, works utilizing PLMs are few and fail to achieve stateof-the-art (SOTA) performance. The recent SOTA performance is yielded by a Guassian HMM variant proposed by However, as a generative model, HMM makes very strong independence assumptions, making it very challenging to incorporate contexualized word representations from PLMs. In this work, we for the first time propose a neural conditional random field autoencoder (CRF-AE) model for unsupervised POS tagging. The discriminative encoder of CRF-AE can straightforwardly incorporate PLM word representations. Moreover, inspired by featurerich HMM, we reintroduce hand-crafted features into the decoder of CRF-AE. Finally, experiments clearly show that our model outperforms previous state-of-the-art models by a large margin on Penn Treebank and multilingual Universal Dependencies treebank v2.0.
Physical effort exertion has a substantial affective influence, which can impact performance and adherence in athletes and the general population. Yet, both the level of effort exertion and affective state during exercise are hard to monitor without the use of questionnaires, which suffer from certain biases and inaccuracies. Here, we examined whether prosodic features, which are among the most prominent characteristics of human expression, reflect the effort level and its related affect during physical exercise. To this end, we extracted prosodic features from verbal affective valence ratings recorded in a previously published study (n = 20; 10 women; nobs = 2,428) of resistance exercises performed until task failure. We found that the mean and SD of the pitch predicted effort-related affective valence and proximity to task failure in the two subsets of the data, and in three separate physical exercises. These results imply that mean pitch elevation and the decrease of the SD of the pitch during effort exertion may serve as a signal of distress as task difficulty increases. Moreover, the consistency of the findings across different types of exercises suggests that the mean and the SD of the pitch may be used to monitor physical effort and affect in various settings and help uncover the nature of physical effort in its different manifestations.
The viability and need for eye movement-based authentication has been well established in light of the recent adoption of Virtual Reality headsets and Augmented Reality glasses. Previous research has demonstrated the practicality of eye movement-based authentication, but there still remains space for improvement in achieving higher identification accuracy. In this study, we focus on incorporating linguistic features in eye movement based authentication, and we compare our approach to authentication based purely on common first-order metrics across 9 machine learning models. Using GazeBase, a large eye movement dataset with 322 participants, and the CELEX lexical database, we show that AdaBoost classifier is the best performing model with an average F1 score of 74.6%. More importantly, we show that the use of linguistic features increased the accuracy of most classification models. Our results provide insights on the use of machine learning models, and motivate more work on incorporating text analysis in eye movement based authentication.
The value of quality treebanks is steadily increasing due to the crucial role they play in the development of natural language processing tools. The creation of such treebanks is enormously labor-intensive and time-consuming. Especially when the size of treebanks is considered, tools that support the annotation process are essential. Various annotation tools have been proposed, however, they are often not suitable for agglutinative languages such as Turkish. BoAT v1 was developed for annotating dependency relations and was subsequently used to create the manually annotated BOUN Treebank (UD_Turkish-BOUN). In this work, we report on the design and implementation of a dependency annotation tool BoAT v2 based on the experiences gained from the use of BoAT v1, which revealed several opportunities for improvement. BoAT v2 is a multi-user and web-based dependency annotation tool that is designed with a focus on the annotator user experience to yield valid annotations. The main objectives of the tool are to: (1) support creating valid and consistent annotations with increased speed, (2) significantly improve the user experience of the annotator, (3) support collaboration among annotators, and (4) provide an open-source and easily deployable web-based annotation tool with a flexible application programming interface (API) to benefit the scientific community. This paper discusses the requirements elicitation, design, and implementation of BoAT v2 along with examples.
While 360° videos watched in a VR headset are gaining in popularity, it is necessary to lower the required bandwidth to stream these immersive videos and obtain a satisfying quality of experience. Doing so requires predicting the user's head motion in advance, which has been tackled by a number of recent prediction methods considering the video content and the user's past motion. However, human motion is a complex process that can depend on many more parameters, including the type of attentional phase the user is currently in, and their emotions, which can be difficult to capture. This is the first article to investigate the effects of user emotions on the predictability of head motion, in connection with video-centric parameters. We formulate and verify hypotheses, and construct a structural equation model of emotion, motion and predictability. We show that the prediction error is higher for higher valence ratings, and that this relationship is mediated by head speed. We also show that the prediction error is lower for higher arousal, but that spatial information moderates the effect of arousal on predictability. This work opens the path to better capture important factors in human motion, to help improve the training process of head motion predictors.
The growth of social media and technology has given online reviews more importance and popularity. Consumer-generated visuals (pictures and videos), together with words and numerical components, are increasingly being used in online reviews. However, more research is necessary to understand how these components interact. This study aims to examine the relationships between review valence, numerical ratings, and hotel booking intentions, and investigate the interactions between consumer-generated visuals and demographics on these relationships. An online questionnaire was used to collect data using a convenience sample of 418 customers from Oman. The proposed model was tested using Structural Equation Modeling. The results demonstrated that negative review valence, positive review valence, and rating usefulness are all significant predictors of hotel booking intentions. The results also show that young and female customers are more affected by review valence and rating usefulness. Consumer-generated visuals play a moderating role, where the relationships between hotel booking intentions and review valence and ratings are weaker when customers are attentive to visuals. The study’s results underline the role of negative valence, rating usefulness and visuals, and offer theoretical and practical implications.
Downy mildew is a major disease of grapevine. Conventional methods for assessing crop diseases are time-consuming and require trained personnel. This work aimed to develop and validate a new method to automatically estimate the severity of downy mildew in grapevine leaves using fuzzy logic and computer vision techniques. Leaf discs of two grapevine varieties were inoculated with Plasmopara viticola and subsequently, RGB images were acquired under indoor conditions. Computer vision techniques were applied for leaf disc location in Petri dishes, image pre-processing and segmentation of pre-processed disc images to separate the pixels representing downy mildew sporulation from the rest of the leaf. Fuzzy logic was applied to improve the segmentation of disc images, rating pixels with a degree of infection according to the intensity of sporulation. To validate the new method, the downy mildew severity was visually evaluated by eleven experts and averaged score was used as the reference value. A coefficient of determination (R2) of 0.87 and a root mean squared error (RMSE) of 7.61 % was observed between the downy mildew severity obtained by the new method and the visual assessment values. Classification of the severity of the infection into three levels was also attempted, achieving an accuracy of 86 % and an F1 score of 0.78. These results indicate that computer vision and fuzzy logic can be used to automatically estimate the severity of downy mildew in grapevine leaves. A new method has been developed and validated to assess the severity of downy mildew in grapevine. The new method can be adapted to assess the severity of other diseases and crops in agriculture.
Artificial voices are nowadays embedded into our daily lives with latest neural voices approaching human voice consistency (naturalness). Nevertheless, behavioral, and neuronal correlates of the perception of less naturalistic emotional prosodies are still misunderstood. In this study, we explored the acoustic tendencies that define naturalness from human to synthesized voices. Then, we created naturalness-reduced emotional utterances by acoustic editions of human voices. Finally, we used Event-Related Potentials (ERP) to assess the time dynamics of emotional integration when listening to both human and synthesized voices in a healthy adult sample. Additionally, listeners rated their perceptions for valence, arousal, discrete emotions, naturalness, and intelligibility. Synthesized voices were characterized by less lexical stress (i.e., reduced difference between stressed and unstressed syllables within words) as regards duration and median pitch modulations. Besides, spectral content was attenuated toward lower F2 and F3 frequencies and lower intensities for harmonics 1 and 4. Both psychometric and neuronal correlates were sensitive to naturalness reduction. (1) Naturalness and intelligibility ratings dropped with emotional utterances synthetization, (2) Discrete emotion recognition was impaired as naturalness declined, consistent with P200 and Late Positive Potentials (LPP) being less sensitive to emotional differentiation at lower naturalness, and (3) Relative P200 and LPP amplitudes between prosodies were modulated by synthetization. Nevertheless, (4) Valence and arousal perceptions were preserved at lower naturalness, (5) Valence (arousal) ratings correlated negatively (positively) with Higuchi's fractal dimension extracted on neuronal data under all naturalness perturbations, (6) Inter-Trial Phase Coherence (ITPC) and standard deviation measurements revealed high inter-individual heterogeneity for emotion perception that is still preserved as naturalness reduces. Notably, partial between-participant synchrony (low ITPC), along with high amplitude dispersion on ERPs at both early and late stages emphasized miscellaneous emotional responses among subjects. In this study, we highlighted for the first time both behavioral and neuronal basis of emotional perception under acoustic naturalness alterations. Partial dependencies between ecological relevance and emotion understanding outlined the modulation but not the annihilation of emotional integration by synthetization.
Successful social interactions depend on the ability to quickly evaluate emotional facial expressions. Research has shown that head orientation and eye gaze are informative affective signals. Across four experiments, we explored a novel eye-gaze cue grounded in a consideration of English spatial metaphors, where up connotes positive feelings (“I’m flying high”) and down connotes negative feelings (“I’m feeling low”). Participants either rated the valence of or categorised a set of sad and happy faces gazing in different directions along the vertical axis. We expected to find a spatial–valence congruency effect, where valence ratings and reaction times would be moderated by whether or not the face was gazing in a metaphor-consistent direction. The results partially supported this hypothesis: sad faces gazing upwards (as opposed to downwards) were rated as happier or more positive (Experiments 1 and 2) and classified slower (Experiments 3 and 4). This was true whether the looking direction was cued by eye gaze in front-view faces (Experiment 1) or by the orientation of profile faces (Experiments 2–4). In addition, this spatial–valence congruency effect was only reliable in the environmental frame of reference (Experiment 4). We found little evidence for a comparable effect of gaze direction on judgements of happy faces, suggesting that eye gaze along the vertical axis may differentially affect judgements of approach and avoidance-related emotional expressions. This has implications for the inferences scholars draw about underlying cognitive representations from observations of conventional metaphorical language.
Leichte Sprache (LS; easy-to-read German) defines a variety of German characterized by simplified syntactic constructions and a small vocabulary. It provides barrier-free information for a wide spectrum of people with cognitive impairments, learning difficulties, and/or a low level of literacy in the German language. The levels of difficulty of a range of syntactic constructions were systematically evaluated with LS readers as part of the recent LeiSA project ( Bock, 2019 ). That study identified a number of constructions that were evaluated as being easy to comprehend but which fell beyond the definition of LS. We therefore want to broaden the scope of LS to include further constructions that LS readers can easily manage and that they might find useful for putting their thoughts into words. For constructions not considered in the LeiSA study, we performed a comparative treebank study of constructions attested to in a collection of 245 LS documents from a variety of sources. Employing the treebanks TüBa-D/S (also called VERBMOBIL) and TüBa-D/Z, we compared the frequency of such constructions in those texts with their incidence in spoken and written German sources produced without the explicit goal of facilitating comprehensibility. The resulting extension is called Extended Leichte Sprache (ELS). To date, text in LS has generally been produced by authors proficient in standard German. In order to enable text production by LS readers themselves, we developed a computational linguistic system, dubbed ExtendedEasyTalk. This system supports LS readers in formulating grammatically correct and semantically coherent texts covering constructions in ELS. This paper outlines the principal components: (1) a natural-language paraphrase generator that supports fast and correct text production while taking readership-design aspects into account, and (2) explicit coherence specifications based on Rhetorical Structure Theory (RST) to express the communicative function of sentences. The system’s writing-workshop mode controls the options in (1) and (2). Mandatory questions generated by the system aim to teach the user when and how to consider audience-design concepts. Accordingly, users are trained in text production in a similar way to elementary school students, who also tend to omit audience-design cues. Importantly, we illustrate in this paper how to make the dialogues of these components intuitive and easy to use to avoid overtaxing the user. We also report the results of our evaluation of the software with different user groups.
Extensive research has shown that children’s early words are learned through sensorimotor experience. Thus, early-acquired words tend to have more concrete meanings. Abstract word meanings tend to be learned later but less is known about their acquisition. We collected meaning-specific concreteness ratings and examined their relationship with age-of-acquisition. Earlier-acquired meanings were rated as more concrete while later-acquired meanings as more abstract, particularly for words typically considered to be concrete. The results suggest that sensorimotor experiences are important to early-acquired word meanings, and other experiences (e.g., linguistic) are important to later-acquired meanings, consistent with a multi-representational view of lexical semantics.
BACKGROUND: Prior research mainly focussed on the impact of the teacher-student relationship on teachers emotions and wellbeing. Current data shows a relationship between the quality of the teacher-student relationship and children's mental health. Unfortunately, it has not yet been investigated whether meaningful experiences with teachers also have an impact on students' well-being and whether an effect can still be found in adulthood. This work examines the impact of meaningful experiences with teachers during childhood and adolescence on the well-being of adults. METHODS: The data in this study was collected by using a questionnaire. The current well-being of the participants was assessed with measures of life satisfaction, resilience, anxiety, stress, depressiveness, and self-esteem. Also, participants were asked to briefly write about their most meaningful experiences with teachers and rate them regarding their valence. These experiences were categorized into seven categories using a summarizing content analysis. We then conducted a statistical analysis with the data obtained. RESULTS: The results showed a highly significant correlation between the participants' self-esteem and the valence ratings of their experiences. Furthermore, the experience category had a substantial effect on individual self-esteem. Overall, this study demonstrated that a relationship exists between the well-being of adults and their experiences with teachers during childhood and adolescence. CONCLUSION: The results of this study call for a reflective, fair, authentic, and empathetic approach to students. Accordingly, teachers should be intensively trained to establish a relationship with their students that is characterized by appreciation and empathy.
The difference between children’s and adults’ speech consists in unusual lexemes or forms of familiar words as well as non-standard meanings of common words occurring in the former. The term error is not appropriate for such cases since these are legitimate elements of the child’s emerging language system. Despite their uniqueness, they do not hinder children’s communication with grown-ups. These units violating the linguistic norm are called innovations. The analysis of children’s innovations enables linguists to explore the nature of language rules and their hierarchical structure as well as to identify accidental gaps (or lacunae) in the language. The article presents a typology of children’s speech innovations from the standpoint of the language norm.
Abstract Chunks are multi-word sequences that constitute an important component of the mental lexicon. In second language (L2) acquisition, chunking is essential for attaining fluency and idiomaticity. In the present study, in order to examine whether chunks provide a processing advantage over non-chunks for L2 learners at different levels of proficiency, three groups (beginner, intermediate, and advanced) English-speaking learners of Chinese participated in an online acceptability judgment task and a familiarity rating task. Our results revealed that the participants in all three groups processed chunks faster and with fewer errors than they did non-chunks. It was also found that the observed processing advantage of chunks could not be explained by a familiarity effect alone, thus suggesting that L2 learners across the board store chunks as holistic units. The implications of chunk instruction in relation to input frequency and variability in L2 settings are also discussed.
Abstract This paper argues that Wittgenstein does not assimilate certainties to either linguistic norms or empirical propositions but assigns them to a liminal space between rule and experience. This liminal space is also brought into play in remarks written at the same time as those compiled in On Certainty, but attributed to different bodies of text ( Remarks on Colour, Last Writings on the Philosophy of Psychology ). The paper maintains that certainties express the agreement and constancy in judgements without which – as Wittgenstein contends in his Philosophical Investigations – rule-following would not be possible. It is shown that this intrinsic relation between rule-following and certainties can explain the liminal status of the latter.
The semantic variant of a primary progressive aphasia (svPPA) is characterized by progressive disruption of semantic knowledge. This study aimed to compare the semantic features of words produced during a narrative speech in svPPA and the logopenic variant of PPA (lvPPA) and to explore their neuroanatomical correlates. Six patients with svPPA and sixteen with lvPPA underwent narrative speech tasks. For all the content words, a semantic depth index (SDI) was determined based on the taxonomic structure of a large lexical database. Study participants underwent an MRI examination. Cortical thickness measures were extracted according to the Desikan atlas. Correlations were computed between SDI and the thickness of cortical regions. Mean SDI was lower for svPPA than for lvPPA. Correlation analyses showed a positive association between the SDI and the cortical thickness of the bilateral temporal pole, parahippocampal and entorhinal cortices, and left middle and superior temporal cortices. Disruption of semantic knowledge observed in svPPA leads to the production of generic terms in narrative speech, and the SDI may be useful for quantifying the level of semantic impairment. The measure was associated with the cortical thickness of brain regions associated with semantic memory.
We introduce a graph polynomial that distinguishes tree structures to represent dependency grammar and a measure based on the polynomial representation to quantify syntax similarity. The polynomial encodes accurate and comprehensive information about the dependency structure and dependency relations of words in a sentence. We apply the polynomial-based methods to analyze sentences in the Parallel Universal Dependencies treebanks. Specifically, we compare the syntax of sentences and their translations in different languages, and we perform a syntactic typology study of available languages in the Parallel Universal Dependencies treebanks. We also demonstrate and discuss the potential of the methods in measuring syntax diversity of corpora.