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16504 papers
Extinction of appetitive conditioning is regarded as an important model for the treatment of psychiatric disorders like addiction. However, very few studies have investigated its neural correlates. Therefore, we investigated neural correlates of appetitive extinction in a large human sample including all genders (N = 76, 40 females) to replicate and extend results from a previous study. During differential appetitive conditioning, one stimulus (CS+) was paired with the chance to win a monetary reward, whereas another stimulus (CS-) was not. During appetitive extinction on the next day, neither the CS+ nor the CS- were reinforced. After successful acquisition of appetitive conditioning, the extinction phase elicited significant reductions of valence and arousal ratings toward the CS+ and a significant reduction in skin conductance responses to the CS+ from early to late extinction. On a neural level, early extinction showed significant differential (CS+ - CS-) activation in dACC and hippocampus, whereas involvement of the vACC and caudate nucleus did not replicate. The differential activation of amygdala and nucleus accumbens during late extinction was replicated, with the amygdala displaying significantly higher differential activation during the late phase of extinction as compared to the early phase of extinction. We show discernible signals for reward learning and extinction in subregions of amygdala and nucleus accumbens after extinction learning. This successful replication underlines the role of nucleus accumbens and amygdala in neural models of appetitive extinction in humans that was previously only based on animal findings.
Word embeddings have been extensively used for sentiment analysis tasks. However, typical existing algorithms only model the syntactic context of words but fail to capture sufficient sentiment information of text, which affects the performance of sentiment analysis. Therefore, this paper presents an word vector refinement model based on improved genetic algorithm, which uses sentiment lexicon to obtain the sentiment ranking of the semantic nearest neighbors of the target word, and uses an improved genetic algorithm to optimize the vector representations of words such that they get the sentiment information of the word. Experimental results show that the proposed model can improve conventional word embeddings for binary classification on Internet Movie Database (IMDB) and fine-grained classification on Stanford Sentiment Treebank (SST).
BACKGROUND: Interest in the measurement of the temporal dynamics of people's emotional lives has risen substantially in psychological and medical research. Emotions fluctuate and change over time, and measuring the ebb and flow of people's affective experiences promises enhanced insights into people's health and functioning. Researchers have used a variety of intensive longitudinal assessment (ILA) methods to create measures of emotion dynamics, including ecological momentary assessments (EMAs), end-of-day (EOD) diaries, and the day reconstruction method (DRM). To date, it is unclear whether they can be used interchangeably or whether ostensibly similar emotion dynamics captured by the methods differ in meaningful ways. OBJECTIVE: This study aims to examine the extent to which different ILA methods yield comparable measures of intraindividual emotion dynamics. METHODS: Data from 90 participants aged 50 years or older were collected in a probability-based internet panel, the Understanding America Study, and analyzed. Participants provided positive and negative affect ratings using 3 ILA methods: (1) smartphone-based EMA, administered 6 times per day over 1 week, (2) web-based EOD diaries, administered daily over the same week, and (3) web-based DRM, administered once during that week. We calculated 11 measures of emotion dynamics (addressing mean levels, variability, instability, and inertia separately for positive and negative affect, as well as emotion network density, mixed emotions, and emotional dialecticism) from each ILA method. The analyses examined mean differences and correlations of scores addressing the same emotion dynamic across the ILA methods. We also compared the patterns of intercorrelations among the emotion dynamics and their relationships with health outcomes (general health, pain, and fatigue) across ILA methods. RESULTS: Emotion dynamics derived from EMAs and EOD diaries demonstrated moderate-to-high correspondence for measures of mean emotion levels (ρ≥0.95), variability (ρ≥0.68), instability (ρ≥0.51), mixed emotions (ρ=0.92), and emotional dialecticism (ρ=0.57), and low correspondence for measures of inertia (ρ≥0.17) and emotion network density (ρ=0.36). DRM-derived measures showed correlations with EMAs and EOD diaries that were high for mean emotion levels and mixed emotions (ρ≥0.74), moderate for variability (ρ=0.38-.054), and low to moderate for other measures (ρ=0.03-0.41). Intercorrelations among the emotion dynamics showed high convergence across EMAs and EOD diaries, and moderate convergence between the DRM and EMAs as well as EOD diaries. Emotion dynamics from all 3 ILA methods produced very similar patterns of relationships with health outcomes. CONCLUSIONS: EMAs and EOD diaries provide corresponding information about individual differences in various emotion dynamics, whereas the DRM provides corresponding information about emotion levels and (to a lesser extent) variability, but not about more complex emotion dynamics. Our results caution researchers against viewing these ILA methods as universally interchangeable.
This study examined the syntactic impairments of Chinese Alzheimer’s disease patients with a dependency network approach. The dependency treebanks and dependency networks are constructed from the discourses of both the patient group and its healthy peers. By analysing the contrasts in the dependency networks of the two groups, we found that 1) the mean dependency distance (MDD) of the AD group is shorter than that of the HP group; furthermore, the MDDs of both AD and HP groups are far below the standard Chinese MDD; 2) the content words like remember, forget, know, etc. and the negative forms of the verbs like don’t know, can’t remember, can’t say, etc. show highly repetitive uncertain and negative expressions that are typical of the predicates of the clauses of AD patients; 3) the function word vertices in the AD dependency network have distinctive network parameters such as higher ‘betweenness’ centrality, closeness centrality, and clustering coefficients, etc., indicating that the syntax of AD is impaired and features more simplified stereotypes. These results indicate that the syntax of the AD group has been impaired from parts of speech to the whole syntactic structure.
One of the biggest attractions in the tourism industry in Bandung is nature tourism. There is still such a constraint related to get information about nature tourism in Bandung because new attractions in Bandung always appear every year. This is felt particularly for foreign tourists outside of Bandung. Tourists are still confused to find new and popular tourist attractions, which are places that are worth visiting or not. By implementing Cyber-Physical-Social System (CPSS) with a new approach that is emphasized on social aspect in smart tourism based on Service Oriented Architecture (SOA) as methodology can influence other travelers to visit tourist attractions in Bandung. The main results are tourists will get information such as location, route, images, rating, captions of tourist attractions, and the most important thing is to be able to exchange information with others. Smart tourism is more flexible because it is web based and does not depend on the operating system used, does not require database storage, does not take up storage space, and is free. Tourists can access smart tourism anytime and anywhere.
UDon2 is an open-source library for manipulating dependency trees represented in the CoNLL-U format. The library is compatible with the Universal Dependencies. UDon2 is aimed at developers of downstream Natural Language Processing applications that require manipulating dependency trees on the sentence level (in addition to other available tools geared towards working with treebanks).
This article presents a theory of the initiation and incrementation mechanisms whereby individual phonetic innovations become community-wide sound changes. The theory asserts that language learners are community-oriented and momentum-sensitive: they are community-oriented in that they acquire and obey a mental representation of the collective linguistic norm of their speech community, rejecting individual idiosyncrasies; they are momentum-sensitive in that their mental representation of the community norm includes an age vector encoding linguistic differences between age groups. The theory is shown to fulfil four critical desiderata: (i) it accounts for the sporadic and localized occurrence of community-wide sound change, (ii) it incorporates Ohala’s prediction of a lawful relationship between the strength of the phonetic biases driving individual innovation and the typological frequency of the corresponding sound changes, (iii) it explains how community-wide sound change advances by intergenerational incrementation producing adolescent peaks in apparent time, and (iv) it reliably generates monotonic—including sigmoid—diachronic trajectories. Moreover, the hypotheses of community orientation and sensitivity to momentum, combined with the mechanical effects of density of contact, suffice to explain several macroscopic phenomena in the propagation of sound change, including class stratification, the curvilinear pattern in change from below, and the existence of change reversals. During propagation, linguistic variants do acquire indexical value, and so social meaning, but this produces only small-scale attitudinal effects; it is not the force that drives the intergenerational incrementation of sound change.
In natural vision, noisy and distorted visual inputs often change our perceptual strategy in scene perception. However, it is unclear the extent to which the affective meaning embedded in the degraded natural scenes modulates our scene understanding and associated eye movements. In this eye-tracking experiment by presenting natural scene images with different categories and levels of emotional valence (high-positive, medium-positive, neutral/low-positive, medium-negative, and high-negative), we systematically investigated human participants' perceptual sensitivity (image valence categorization and arousal rating) and image-viewing gaze behaviour to the changes of image resolution. Our analysis revealed that reducing image resolution led to decreased valence recognition and arousal rating, decreased number of fixations in image-viewing but increased individual fixation duration, and stronger central fixation bias. Furthermore, these distortion effects were modulated by the scene valence with less deterioration impact on the valence categorization of negatively valenced scenes and on the gaze behaviour in viewing of high emotionally charged (high-positive and high-negative) scenes. It seems that our visual system shows a valence-modulated susceptibility to the image distortions in scene perception.
We describe an approach to statistical parsing with Tree-Wrapping Grammars (TWG). TWG is a tree-rewriting formalism which includes the tree-combination operations of substitution, sisteradjunction and tree-wrapping substitution. TWGs can be extracted from constituency treebanks and aim at representing long distance dependencies (LDDs) in a linguistically adequate way. We present a parsing algorithm for TWGs based on neural supertagging and A * parsing. We extract a TWG for English from the treebanks for Role and Reference Grammar and discuss first parsing results with this grammar.
Parsing sentences into syntax trees can benefit downstream applications in NLP. Transition-based parsers build trees by executing actions in a state transition system. They are computationally efficient, and can leverage machine learning to predict actions based on partial trees. However, existing transition-based parsers are predominantly based on the shift-reduce transition system, which does not align with how humans are known to parse sentences. Psycholinguistic research suggests that human parsing is strongly incremental: humans grow a single parse tree by adding exactly one token at each step. In this paper, we propose a novel transition system called attach-juxtapose. It is strongly incremental; it represents a partial sentence using a single tree; each action adds exactly one token into the partial tree. Based on our transition system, we develop a strongly incremental parser. At each step, it encodes the partial tree using a graph neural network and predicts an action. We evaluate our parser on Penn Treebank (PTB) and Chinese Treebank (CTB). On PTB, it outperforms existing parsers trained with only constituency trees; and it performs on par with state-of-the-art parsers that use dependency trees as additional training data. On CTB, our parser establishes a new state of the art. Code is available at this https URL.
In this paper, we first open on important issues regarding the Penn Korean Universal Treebank (PKT-UD) and address these issues by revising the entire corpus manually with the aim of producing cleaner UD annotations that are more faithful to Korean grammar. For compatibility to the rest of UD corpora, we follow the UDv2 guidelines, and extensively revise the part-of-speech tags and the dependency relations to reflect morphological features and flexible word-order aspects in Korean. The original and the revised versions of PKT-UD are experimented with transformer-based parsing models using biaffine attention. The parsing model trained on the revised corpus shows a significant improvement of 3.0% in labeled attachment score over the model trained on the previous corpus. Our error analysis demonstrates that this revision allows the parsing model to learn relations more robustly, reducing several critical errors that used to be made by the previous model.
This paper discusses the theoretical bases as well as the pragmatic implementation of the lemmatization of the Late Latin Charter Treebanks (LLCT). LLCT is a set of three dependency treebanks (LLCT1, LLCT2, LLCT3) of Early Medieval Latin documentary texts (charters) written in Italy between AD 714 and 1000 (c. 594,000 tokens). The original model for the lemmatization of LLCT was the Latin Dependency Treebank (LDT), which is mainly Classical standard Latin and based on the entries of Lewis and Short’s Latin Dictionary. Since LLCT reflects later linguistic developments of Latin and contains a plethora of non-standard proper names, particular attention is paid to how non-standard lexemes are lemmatized systematically to make the lemmatization maximally usable. The theoretical underpinnings to manage the lemmatization boil down to two principles: the evolutionary principle and the parsimony principle.
For sequence models with large word-level vocabularies, a majority of network parameters lie in the input and output layers. In this work, we describe a new method, DeFINE, for learning deep word-level representations efficiently. Our architecture uses a hierarchical structure with novel skip-connections which allows for the use of low dimensional input and output layers, reducing total parameters and training time while delivering similar or better performance versus existing methods. DeFINE can be incorporated easily in new or existing sequence models. Compared to state-of-the-art methods including adaptive input representations, this technique results in a 6% to 20% drop in perplexity. On WikiText-103, DeFINE reduces total parameters of Transformer-XL by half with minimal impact on performance. On the Penn Treebank, DeFINE improves AWD-LSTM by 4 points with a 17% reduction in parameters, achieving comparable performance to state-of-the-art methods with fewer parameters. For machine translation, DeFINE improves a Transformer model by 2% while simultaneously reducing total parameters by 26%
The importance of affect processing to human behavior has long driven researchers to pursue its measurement. In this study, we compared the relative fidelity of measurements of neural activation and physiology (i.e., heart rate change) in detecting affective valence induction across a broad continuum of conveyed affective valence. We combined intra-subject neural activation based multivariate predictions of affective valence with measures of heart rate (HR) deceleration to predict predefined normative affect rating scores for stimuli drawn from the International Affective Picture System (IAPS) in a population (n = 50) of healthy adults. In sum, we found that patterns of neural activation and HR deceleration significantly, and uniquely, explain the variance in normative valent scores associated with IAPS stimuli; however, we also found that patterns of neural activation explain a significantly greater proportion of that variance. These traits persisted across a range of stimulus sets, differing by the polar-extremity of their positively and negatively valent subsets, which represent the positively and negatively valent polar-extremity of stimulus sets reported in the literature. Overall, these findings support the acquisition of heart rate deceleration concurrently with fMRI to provide convergent validation of induced affect processing in the dimension of affective valence.
We propose a novel constituency parsing model that casts the parsing problem into a series of pointing tasks. Specifically, our model estimates the likelihood of a span being a legitimate tree constituent via the pointing score corresponding to the boundary words of the span. Our parsing model supports efficient top-down decoding and our learning objective is able to enforce structural consistency without resorting to the expensive CKY inference. The experiments on the standard English Penn Treebank parsing task show that our method achieves 92.78 F1 without using pre-trained models, which is higher than all the existing methods with similar time complexity. Using pre-trained BERT, our model achieves 95.48 F1, which is competitive with the state-of-theart while being faster. Our approach also establishes new state-of-the-art in Basque and Swedish in the SPMRL shared tasks on multilingual constituency parsing.
Traditional approaches to set goals in second language (L2) vocabulary acquisition relied either on word lists that were obtained from large L1 corpora or on collective knowledge and experience of L2 experts, teachers, and examiners. Both approaches are known to offer some advantages, but also to have some limitations. In this paper, we try to combine both sources of information, namely the official reference level description for French language and the FLElex lexical database. Our aim is to train a statistical model on the French RLD that would be able to turn the distributional information from FLElex into one of the six levels of the Common European Framework of Reference for languages (CEFR). We show that such approach yields a gain of 29\\% in accuracy compared to the method currently used in the CEFRLex project. Besides, our experiments also offer deeper insights into the advantages and shortcomings of the two traditional sources of information (frequency vs. expert knowledge).
Emotion research typically searches for consistency and specificity in physiological activity across instances of an emotion category, such as anger or fear, yet studies to date have observed more variation than expected. In the present study, we adopt an alternative approach, searching inductively for structure within variation, both within and across participants. Following a novel, physiologically-triggered experience sampling procedure, participants’ self-reports and peripheral physiological activity were recorded when substantial changes in cardiac activity occurred in the absence of movement. Unsupervised clustering analyses revealed variability in the number and nature of patterns of physiological activity that recurred within individuals, as well as in the affect ratings and emotion labels associated with each pattern. There were also broad patterns that recurred across individuals. These findings support a constructionist account of emotion which, drawing on Darwin, proposes that emotion categories are populations of variable instances tied to situation-specific needs.
(NAPS; Marchewka et al., 2014). We collected ratings of perceived valence and arousal for both material groups and recorded eye movements of 42 participants during reading and picture viewing. Linear mixed-effects models were performed to analyze effects of valence (i.e., valence category, valence rating) and stimulus domain (i.e., textual, pictorial) on ratings of perceived valence and arousal, eye movements in reading, and eye movements in picture viewing. Results supported the success of our experimental manipulation: emotionally positive stimuli (i.e., vignettes, pictures) were perceived more positively and less arousing than emotionally negative ones. The cross-domain comparison indicated that vignettes are able to induce stronger valence effects than their pictorial counterparts, no differences between vignettes and pictures regarding effects on perceived arousal were found. Analyses of eye movements in reading replicated results from experiments using isolated words and sentences: perceived positive text valence attracted shorter reading times than perceived negative valence at both the supralexical and lexical level. In line with previous findings, no emotion effects on eye movements in picture viewing were found. This is the first eye tracking study reporting superior valence effects for vignettes compared to pictures and valence-specific effects on eye movements in reading at the supralexical level.
Many educational institutions in North America have declared a commitment to enhancing the diversity of their students, and to providing a learning environment free of discrimination. This diversity unequivocally includes sexual orientation as well as gender identity and expression, and we teachers are expected to play a role in fulfilling this commitment. Nevertheless, there are few opportunities for us to learn about the diversity of gender and sexuality in pre-service training or in-service professional development for Japanese-language education. This paper addresses issues that may create challenges for LGBTQ learners of Japanese, paying special attention to heteronormativity in Japanese language teaching materials and linguistic norms and ideology regarding gendered expression in Japanese, and suggests ways teachers might deal with these issues in order to create an inclusive learning environment for all students regardless of their gender and sexuality.
AIMS: The aim of this study was to investigate the effects of alcohol hangover on emotion regulation. METHODS: Forty-five non-smoking, healthy participants aged between 18 and 30 years completed a lab-based emotion regulation task assessing cognitive reappraisal and an emotion regulation questionnaire (State-Difficulties in Emotion Regulation Scale [S-DERS]) when hungover (morning following a night of heavy drinking) and under a no-hangover condition in a naturalistic, within-subjects design study. RESULTS: Participants reported poorer emotion regulation overall (P < 0.001, d = 0.75), and for the subscales 'Non-Acceptance', 'Modulation' and 'Clarity' (Ps ≤ 0.001, ds ≥ 0.62), but not 'Awareness' on the S-DERS, in the hangover versus the no-hangover condition. Hangover did not impair emotion regulation ability as assessed using the lab-based task (Ps ≥ 0.21, ds ≤ 0.40), but there was a general negative shift in valence ratings (i.e. all images were rated more negatively) in the hangover relative to the no-hangover condition (P < 0.001, d = 1.16). CONCLUSION: These results suggest that emotion regulation in everyday life and emotional reactivity may be adversely affected by alcohol hangover, but some emotion regulation strategies (e.g. deliberate cognitive reappraisal) may be unaffected.
The carbon footprint of natural language processing research has been increasing in recent years due to its reliance on large and inefficient neural network implementations. Distillation is a network compression technique which attempts to impart knowledge from a large model to a smaller one. We use teacher-student distillation to improve the efficiency of the Biaffine dependency parser which obtains state-of-the-art performance with respect to accuracy and parsing speed (Dozat and Manning, 2017). When distilling to 20\% of the original model's trainable parameters, we only observe an average decrease of $\sim$1 point for both UAS and LAS across a number of diverse Universal Dependency treebanks while being 2.30x (1.19x) faster than the baseline model on CPU (GPU) at inference time. We also observe a small increase in performance when compressing to 80\% for some treebanks. Finally, through distillation we attain a parser which is not only faster but also more accurate than the fastest modern parser on the Penn Treebank.
Color has demonstrated to have an influence on picture naming tasks. Objects with high color diagnosticity are recalled faster than objects with low value. That is why the Argentinean Psycholinguistic Picture Naming Test in color (PAPDIC in Spanish) was designed. The items and semantic cues were built considering local psycholinguistic norms. A series of psychometric analyses were performed on a sample of patients with focal brain damage with (n = 11) and without (n = 14) aphasia, a sample of patients with degenerative disease (n = 46) and two samples of healthy participants (young n = 27, old n = 50). Evidence of convergent validity was obtained through the correlation with the brief Boston Naming Test (r = 0.871; p <.001); of criteria validity by means of contrasted groups analysis (t = 4.059, p <.001), and through the ROC curve analysis (AUC = 0.993). Scores’ reliability was explored by means of an internal consistency analysis (KR20 = 0.905). These results indicate that the PAPDIC is a promising color naming test which can be applied in the field of clinical neuropsychology to identify anomia. This test has several advantages in comparison with the available naming tests in Argentina.
The high memory consumption and computational costs of Recurrent neural network language models (RNNLMs) limit their wider application on resource constrained devices. In recent years, neural network quantization techniques that are capable of producing extremely low-bit compression, for example, binarized RNNLMs, are gaining increasing research interests. Directly training of quantized neural networks is difficult. By formulating quantized RNNLMs training as an optimization problem, this paper presents a novel method to train quantized RNNLMs from scratch using alternating direction methods of multipliers (ADMM). This method can also flexibly adjust the trade-off between the compression rate and model performance using tied low-bit quantization tables. Experiments on two tasks: Penn Treebank (PTB), and Switchboard (SWBD) suggest the proposed ADMM quantization achieved a model size compression factor of up to 31 times over the full precision baseline RNNLMs. Faster convergence of 5 times in model training over the baseline binarized RNNLM quantization was also obtained.
Even though Automatic Speech Recognition (ASR) systems significantly improved over the last decade, they still introduce a lot of errors when they transcribe voice to text. One of the most common reasons for these errors is phonetic confusion between similar-sounding expressions. As a result, ASR transcriptions often contain "quasi-oronyms", i.e., words or phrases that sound similar to the source ones, but that have completely different semantics (e.g., "win" instead of "when" or "accessible on defecting" instead of "accessible and affecting"). These errors significantly affect the performance of downstream Natural Language Understanding (NLU) models (e.g., intent classification, slot filling, etc.) and impair user experience. To make NLU models more robust to such errors, we propose novel phonetic-aware text representations. Specifically, we represent ASR transcriptions at the phoneme level, aiming to capture pronunciation similarities, which are typically neglected in word-level representations (e.g., word embeddings). To train and evaluate our phoneme representations, we generate noisy ASR transcriptions of four existing datasets - Stanford Sentiment Treebank, SQuAD, TREC Question Classification and Subjectivity Analysis - and show that common neural network architectures exploiting the proposed phoneme representations can effectively handle noisy transcriptions and significantly outperform state-of-the-art baselines. Finally, we confirm these results by testing our models on real utterances spoken to the Alexa virtual assistant.
The Dark Triad of personality is a cluster of three socially aversive personality traits: Machiavellianism, narcissism and psychopathy. These traits are associated with a selfish, aggressive and exploitative interpersonal strategy. The objective of the current study was to establish relationships between the Dark Triad traits (and their dimensions) and momentary affect. Machiavellianism, grandiose narcissism, vulnerable narcissism and the dimensions of the Triarchic model of psychopathy (namely, boldness, meanness and disinhibition) were examined. We used the Day Reconstruction Method, which is based on reconstructing affective states experienced during the previous day. The final sample consisted of 270 university students providing affective ratings of 3047 diary episodes. Analyses using multilevel modelling showed that only boldness had a positive association with positive affective states and affect balance, and a negative association with negative affective states. Grandiose narcissism and its sub-dimensions had no relationship with momentary affect. The other dark traits were related to negative momentary affect and/or inversely related to positive momentary affect and affect balance. As a whole, our results empirically demonstrated distinctiveness of the Dark Triad traits in their relationship to everyday affective states. These findings are not congruent with the notion that people with the Dark Triad traits, who have a dispositional tendency to manipulate and exploit others, are generally cold and invulnerable to negative feelings. The associations between the Dark Triad and momentary affect were discussed in the contexts of evolutionary and positive psychology, in relation to the role and adaptive value of positive and negative emotions experienced by individuals higher in Machiavellianism, narcissism and psychopathy.
Extensive couples? literature shows that how couples feel after a conflict is predicted by certain emotional aspects of that conversation. Understanding the emotions of couples leads to a better understanding of partners? mental well-being and consequently their relationships. Hence, automatic emotion recognition among couples could potentially guide interventions to help couples improve their emotional well-being and their relationships. It has been shown that people's global emotional judgment after an experience is strongly influenced by the emotional extremes and ending of that experience, known as the peak-end rule. In this work, we leveraged this theory and used machine learning to investigate, which audio segments can be used to best predict the end-of-conversation emotions of couples. We used speech data collected from 101 Dutch-speaking couples in Belgium who engaged in 10-minute long conversations in the lab. We extracted acoustic features from (1) the audio segments with the most extreme positive and negative ratings, and (2) the ending of the audio. We used transfer learning in which we extracted these acoustic features with a pre-trained convolutional neural network (YAMNet). We then used these features to train machine learning models - support vector machines - to predict the end-of-conversation valence ratings (positive vs negative) of each partner. The results of this work could inform how to best recognize the emotions of couples after conversation-sessions and eventually, lead to a better understanding of couples? relationships either in therapy or in everyday life.
The French TreeBank developed at the University Paris 7 is the main source of morphosyntactic and syntactic annotations for French. However, it does not include explicit information related to named entities, which are among the most useful information for several natural language processing tasks and applications. Moreover, no large-scale French corpus with named entity annotations contain referential information, which complement the type and the span of each mention with an indication of the entity it refers to. We have manually annotated the French TreeBank with such information, after an automatic pre-annotation step. We sketch the underlying annotation guidelines and we provide a few figures about the resulting annotations.
Abstract Immersive virtual reality (VR) enables naturalistic neuroscientific studies while maintaining experimental control, but dynamic and interactive stimuli pose methodological challenges. We here probed the link between emotional arousal, a fundamental property of affective experience, and parieto-occipital alpha power under naturalistic stimulation: 37 young healthy adults completed an immersive VR experience, which included rollercoaster rides, while their EEG was recorded. They then continuously rated their subjective emotional arousal while viewing a replay of their experience. The association between emotional arousal and parieto-occipital alpha power was tested and confirmed by (1) decomposing the continuous EEG signal while maximizing the comodulation between alpha power and arousal ratings and by (2) decoding periods of high and low arousal with discriminative common spatial patterns and a Long Short-Term Memory recurrent neural network. We successfully combine EEG and a naturalistic immersive VR experience to extend previous findings on the neurophysiology of emotional arousal towards real-world neuroscience.
Universal Dependencies is a project that seeks to develop cross-linguistically consistent treebank annotation for many languages, with the goal of facilitating multilingual parser development, cross-lingual learning, and parsing research from a language typology perspective. The annotation scheme is based on (universal) Stanford dependencies (de Marneffe et al., 2006, 2008, 2014), Google universal part-of-speech tags (Petrov et al., 2012), and the Interset interlingua for morphosyntactic tagsets (Zeman, 2008).
Abstract One view of language change sees changes as originating in erroneous deviations from the linguistic norm and diffusing through social transmission. An alternative is to see changes as originating in speakers’ problem-solving activities and spreading in response to system pressures that reflect speakers’ recurrent communicative needs and shared resources. It is argued here that the latter may well be the dominant scenario.
Pain is evolutionarily hardwired to signal potential danger and threat. It has been proposed that altered pain-related associative learning processes, i.e., emotional or fear conditioning, might contribute to the development and maintenance of chronic pain. Pain in or near the face plays a special role in pain perception and processing, especially with regard to increased pain-related fear and unpleasantness. However, differences in pain-related learning mechanisms between the face and other body parts have not yet been investigated. Here, we examined body-site specific differences in associative emotional conditioning using electrical stimuli applied to the face and the hand. Acquisition, extinction, and reinstatement of cue-pain associations were assessed in a 2-day emotional conditioning paradigm using a within-subject design. Data of 34 healthy subjects revealed higher fear of face pain as compared to hand pain. During acquisition, face pain (as compared to hand pain) led to a steeper increase in pain-related negative emotions in response to conditioned stimuli (CS) as assessed using valence ratings. While no significant differences between both conditions were observed during the extinction phase, a reinstatement effect for face but not for hand pain was revealed on the descriptive level and contingency awareness was higher for face pain compared to hand pain. Our results indicate a stronger propensity to acquire cue-pain-associations for face compared to hand pain, which might also be reinstated more easily. These differences in learning and resultant pain-related emotions might play an important role in the chronification and high prevalence of chronic facial pain and stress the evolutionary significance of pain in the head and face.
The purpose of this study was to experimentally investigate the relationship between positive affect elicitation (using a short video clip) prior to exercise and affect during acute aerobic exercise. A counterbalanced, within-subject experimental design was used. We conducted three related experiments. In Experiment 1, 30 adults aged 18–40 years participated in a positive affect-elicitation condition (“affective priming”) and a control condition. Participation involved watching a five-minute video clip, as well as walking on a treadmill at a (self-selected) brisk pace for ten minutes. We compared affective ratings at baseline and intra-exercise for both conditions using a 2 (condition; priming versus no priming) × 2 (time; pre- versus mid-exercise) repeated measures ANOVA. In the follow-up experiments, we re-examined the relationship between affective priming and intra-exercise affect, addressing some limitations noted with Experiment 1. In Experiment 2, we compared the affect-elicitation properties of self-selected and imposed video clips. In Experiment 3, we re-investigated the potential affective benefits of priming, while including a neutral (neither positive nor negative) video during the control condition to attenuate potential demand characteristics, and a positive video-only condition to investigate possible carryover effects. Self-selected and imposed film clips showed similar affect-elicitation properties. Comparing the priming and control conditions, there were notable differences in the mean intra-exercise affective valence ratings (p = 0.07 Experiment 1, p = 0.01 Experiment 3). The mean affective activation ratings were not significantly different (p = 0.07 Experiment 1, p = 0.86 Experiment 3). Priming the affective state prior to exercise may be beneficial for enhancing intra-exercise affect.
Gender can be considered an embodied social concept encompassing biological as well as cultural components. In this paper, we explored whether the concept of gender varies as a function of different cultural and linguistic norms by comparing communities that vary in their social treatment of gender-related issues and linguistic encoding of gender. In Study 1, Italian, Dutch, and English speaking participants completed a free-listing task which showed Italians and Dutch were the most distinct in their conceptualization of gender: Italian participants focused more on sociocultural features (e.g., discrimination, politics, power), whereas Dutch participants focused more on the corporeal sphere (e.g., hormones, breasts, genitals). Study 2 replicated this finding focusing on Italian and Dutch and using a typicality rating task: sociocultural and abstract features were considered as more typical of “gender” by Italian than Dutch participants. Study 3 addressed Italian and Dutch participants’ explicit beliefs about gender with a questionnaire measuring essentialism and constructivism, and consolidated results from Study 1 and 2 showing that Dutch participants endorsed more essentialist beliefs about gender compared to Italian participants. Our results provide evidence that gender is conceptualized differently by diverse groups more in line with sociocultural constructivist accounts and is adapted to specific cultural and linguistic environments.
We tackle implicit discourse relation classification, a task of automatically determining semantic relationships between arguments. The attention-worthy words in arguments are crucial clues for classifying the discourse relations. Attention mechanisms have been proven effective in highlighting the attention-worthy words during encoding. However, our survey shows that some inessential words are unintentionally misjudged as the attention-worthy words and, therefore, assigned heavier attention weights than should be. We propose a penalty-based loss re-estimation method to regulate the attention learning process, integrating penalty coefficients into the computation of loss by means of overstability of attention weight distributions. We conduct experiments on the Penn Discourse TreeBank (PDTB) corpus. The test results show that our loss re-estimation method leads to substantial improvements for a variety of attention mechanisms, and it obtains highly competitive performance compared to the state-of-the-art methods.
abstract Reaction times for a translation recognition study are reported where novice to expert English–ASL bilinguals rejected English translation distractors for ASL signs that were related to the correct translations through phonology, semantics, or both form and meaning (diagrammatic iconicity). Imageability ratings of concepts impacted performance in all conditions; when imageability was high, participants showed interference for phonologically related distractors, and when imageability was low participants showed interference for semantically related distractors, regardless of proficiency. For diagrammatically related distractors high imageability caused interference in experts, but low imageability caused interference in novices. These patterns suggest that imageability and diagrammaticity interact with proficiency – experts process diagrammatic related distractors phonologically, but novices process them semantically. This implies that motivated signs are dependent on the entrenchment of language systematicity; rather than decreasing their impact on language processing as proficiency grows, they build on the original benefit conferred by iconic mappings.
Alexithymia is a personality trait characterized by difficulties identifying and describing feelings (DIF and DDF) and an externally-oriented thinking style (EOT). The primary aim of the present study was to investigate links between alexithymia and the evaluation of emotional scenes. We also investigated whether viewers’ evaluations of emotional scenes were better predicted by specific alexithymic traits or by individual differences in sensory processing sensitivity (SPS). Participants (N = 106) completed measures of alexithymia and SPS along with a task requiring speeded judgments of the pleasantness of 120 moderately arousing scenes. We did not replicate laterality effects previously described with the scene perception task. Compared to those with weak alexithymic traits, individuals with moderate-to-strong alexithymic traits were less likely to classify positively valenced scenes as pleasant, and less likely to classify scenes with (vs. without) implied motion in a way that was consistent with normative scene valence ratings. In addition, regression analyses confirmed that reporting strong EOT and a tendency to be easily overwhelmed by busy sensory environments negatively predicted classification accuracy for positive scenes, and that both DDF and EOT negatively predicted classification accuracy for scenes depicting implied motion. These findings highlight the importance of accounting for stimulus characteristics and individual differences in specific traits associated with alexithymia and SPS when investigating the processing of emotional stimuli. Learning more about the links between these individual difference variables may have significant clinical implications, given that alexithymia is an important, transdiagnostic risk factor for a wide range of psychopathologies.
In this study we evaluate the convergent validity of a new graphical self-report tool (the EmojiGrid) for the affective appraisal of perceived touch events. The EmojiGrid is a square grid labeled with facial icons (emoji) showing different levels of valence and arousal. The EmojiGrid is language independent and efficient (a single click suffices to report both valence and arousal), making it a practical instrument for studies on affective appraisal. We previously showed that participants can intuitively and reliably report their affective appraisal (valence and arousal) of visual, auditory and olfactory stimuli using the EmojiGrid, even without additional (verbal) instructions. However, because touch events can be bidirectional and dynamic, these previous results cannot be generalized to the touch domain. In this study, participants reported their affective appraisal of video clips showing different interpersonal (social) and object-based touch events, using either the validated 9-point SAM (Self-Assessment Mannikin) scale or the EmojiGrid. The valence ratings obtained with the EmojiGrid and the SAM are in excellent agreement. The arousal ratings show good agreement for object-based touch and moderate agreement for social touch. For social touch and at more extreme levels of valence, the EmojiGrid appears more sensitive to arousal than the SAM. We conclude that the EmojiGrid can also serve as a valid and efficient graphical self-report instrument to measure human affective response to a wide range of (possibly mediated) tactile signals.
Deep neural networks (DNNs) have become the gold standard for solving challenging classification problems, especially given complex sensor inputs (e.g., images and video). While DNNs are powerful, they are also brittle, and their inner workings are not fully understood by humans, leading to their use as "black-box" models. DNNs often generalize poorly when provided new data sampled from slightly shifted distributions; DNNs are easily manipulated by adversarial examples; and the decision-making process of DNNs can be difficult for humans to interpret. To address these challenges, we propose integrating DNNs with external sources of semantic knowledge. Large quantities of meaningful, formalized knowledge are available in knowledge graphs and other databases, many of which are publicly obtainable. But at present, these sources are inaccessible to deep neural methods, which can only exploit patterns in the signals they are given to classify. In this work, we conduct experiments on the ADE20K dataset, using scene classification as an example task where combining DNNs with external knowledge graphs can result in more robust and explainable models. We align the atomic concepts present in ADE20K (i.e., objects) to WordNet, a hierarchically-organized lexical database. Using this knowledge graph, we expand the concept categories which can be identified in ADE20K and relate these concepts in a hierarchical manner. The neural architecture we present performs scene classification using these concepts, illuminating a path toward DNNs which can efficiently exploit high-level knowledge in place of excessive quantities of direct sensory input. We hypothesize and experimentally validate that incorporating background knowledge via an external knowledge graph into a deep learning-based model should improve the explainability and robustness of the model.
OBJECTIVES: The objective of this paper was to examine the implementation and effectiveness of a community-based intervention for hoarding disorder (HD) using Cognitive Rehabilitation and Exposure/Sorting Therapy (CREST). DESIGN: This was a mixed-method, pre-post quasi-experimental study informed by the Practical, Robust Implementation and Sustainability Model for implementation science. SETTING: Program activities took place in San Diego County, mainly within clients' homes or community, with some activities in-office. PARTICIPANTS: Participants were aged 60 years or older, met eligibility for Medi-Cal or were uninsured, and met criteria for HD. INTERVENTION: A manualized, mobile protocol that incorporated CREST was utilized. MEASUREMENTS: The Clutter Image Rating and Hoarding Rating Scale were used as effectiveness outcomes. An investigator-created staff questionnaire was used to evaluate implementation. RESULTS: Thirty-seven clients were reached and enrolled in treatment and 15 completed treatment during the initial 2 years of the program. There were significant changes in hoarding severity and clutter volume. Based on the initial 2 years of the program, funding was provided for expansion to cover additional San Diego County regions and hire more staff clinicians in year three. CONCLUSION: Preliminary data suggest that the CREST intervention can be successfully implemented in a community setting with positive results for older adults with HD.
BACKGROUND: Traumatic experiences are associated with neurofunctional dysregulations in key regions of the emotion regulation circuits. In particular, amygdala responsivity to negative stimuli is exaggerated while engagement of prefrontal regulatory control regions is attenuated. Successful application of emotion regulation (ER) strategies may counteract this disbalance, however, application of learned strategies in daily life is hampered in individuals afflicted by posttraumatic stress disorder (PTSD). We hypothesized that a single session of real-time fMRI (rtfMRI) guided upregulation of prefrontal regions during an emotion regulation task enhances self-control during exposure to negative stimuli and facilitates transfer of the learned ER skills to daily life. METHODS: In a cross-over design, individuals with a PTSD diagnosis after a single traumatic event (n = 20) according to DSM-IV-TR criteria and individuals without a formal psychiatric diagnosis (n = 21) underwent a cognitive reappraisal training. In randomized order, all participants completed two rtfMRI neurofeedback (NF) runs targeting the left lateral prefrontal cortex (lPFC) and two control runs without NF (NoNF) while using cognitive reappraisal to reduce their emotional response to negative scenes. During the NoNF runs, two %%-signs were displayed instead of the two-digit feedback (FB) to achieve a comparable visual stimulation. The project aimed at defining the clinical potential of the training according to three success markers: (1) NF induced changes in left lateral prefrontal cortex and bilateral amygdala activity during the regulation of aversive scenes compared to cognitive reappraisal alone (primary registered outcome), (2) associated changes on the symptomatic and behavioral level such as indicated by PTSD symptom severity and affect ratings, (3) clinical utility such as indicated by perceived efficacy, acceptance, and transfer to daily life measured four weeks after the training. RESULTS: In comparison to the reappraisal without feedback, a neurofeedback-specific decrease in the left lateral PFC (d = 0.54) alongside an attenuation of amygdala responses (d = 0.33) emerged. Reduced amygdala responses during NF were associated with symptom improvement (r = -0.42) and less negative affect (r = -0.63) at follow-up. The difference in symptom scores exceeds requirements for a minimal clinically important difference and corresponds to a medium effect size (d = 0.64). Importantly, 75% of individuals with PTSD used the strategies in daily life during a one-month follow-up period and perceived the training as efficient. CONCLUSION: Our findings suggest beneficial effects of the NF training indicated by reduced amygdala responses that were associated with improved symptom severity and affective state four weeks after the NF training as well as patient-centered perceived control during the training, helpfulness and application of strategies in daily life. However, reduced prefrontal involvement was unexpected. The study suggests good tolerability of the training protocol and potential for clinical use in the treatment of PTSD.