Papers reviewed and determined not to be word norm studies. Use the flag icon to report errors or suggest re-inclusion.
16504 papers
We propose a model of Tibetan syntactic parsing which is based on Tibetan syl lables instead of Tibetan words, change Tibetan syntactic Treebank use algorith m of labeling.
Limbic encephalitis (LE) is an autoimmune-mediated disorder that affects structures of the limbic system, in particular, the amygdala. The amygdala constitutes a brain area substantial for processing of emotional, especially fear-related signals. The amygdala is also involved in neuroendocrine and autonomic functions, including skin conductance responses (SCRs) to emotionally arousing stimuli. This study investigates behavioral and autonomic responses to discrete emotion evoking and neutral film clips in a patient suffering from LE associated with contactin-associated protein-2 (CASPR2) antibodies as compared to a healthy control group. Results show a lack of SCRs in the patient while watching the film clips, with significant differences compared to healthy controls in the case of fear-inducing videos. There was no comparable impairment in behavioral data (emotion report, valence, and arousal ratings). The results point to a defective modulation of sympathetic responses during emotional stimulation in patients with LE, probably due to impaired functioning of the amygdala.
In this work, we present a novel way of using neural network for graph-based dependency parsing, which fits the neural network into a simple probabilistic model and can be furthermore generalized to high-order parsing. Instead of the sparse features used in traditional methods, we utilize distributed dense feature representations for neural network, which give better feature representations. The proposed parsers are evaluated on English and Chinese Penn Treebanks. Compared to existing work, our parsers give competitive performance with much more efficient inference.
Categorial grammars are attractive because they have a clear account of unbounded dependencies. This accounting is especially important in Mandarin Chinese which makes extensive usage of unbounded dependencies. However, parsers trained on existing categorial grammar annotations (Tse and Curran, 2010) extracted from the Penn Chinese Treebank This work reannotates the Penn Chinese Treebank into a generalized categorial grammar which uses a larger rule set and a substantially smaller category set while retaining the capacity to model unbounded dependencies. Experimental results show a statistically significant improvement in parsing accuracy with this categorial grammar.
Dependency parsing has become an important line of research in natural language processing in recent years. This is due to its usefulness in a wide variety of real world applications. This paper presents the improvement of Vietnamese dependency parsing using distributed word representations. Our parser achieves an accuracy of 76.29% of unlabelled attachment score or 69.25% of labelled attachment score. This is the most accurate dependency parser for the Vietnamese language in comparison to others which are trained and tested on the same dependency treebank. The distributed word representations are produced by two recent unsupervised learning models, the Skip-gram model and the GloVe model. We also show that distributed representations produced by the GloVe model are better than those produced by the Skip-gram model when being used in dependency parsing. Our dependency parsing system, including software, corpus and distributed word representations, is released as an open source project, freely available for research purpose.
Following a comparison of the different views on lexical meaning conveyed by the Latin WordNet and by a treebank-based valency lexicon for Latin, the paper evaluates the degree of overlapping between a number of homogeneous lexical subsets extracted from the two resources.
The emotional habituation plays an important role in individuals' adaptation to the environment. The present study explored the brain's emotional habituation to positive and negative pictures of diverse emotional intensities. Event-related potentials (ERPs) were recorded in two different experimental sessions, for highly positive (HP), mildly positive (MP) and neutral picture and for highly negative (HN), mildly negative (MN) and neutral picture. Subjects were asked to perform a standard/deviant categorization task, irrespective of emotionality of the deviants. The behavior results showed that the arousal ratings for HP stimuli decreased significantly with stimulus repetition. In addition, the ERP results displayed earlier N1 peak latencies with stimulus repetition in the positive session. Furthermore, the size of the emotion effect, which was computed by the emotion-neutral differences, decreased significantly for HP and MP stimuli with stimulus repetition in P3 amplitudes. Conversely, the current study failed to observe an emotional habituation effect to negative stimuli in any behavioral or ERP indexes. These results suggest that the humans' emotional reactions to positive stimuli, irrespective of the emotional intensity, are susceptible to habituation, irrespective of information processing stage. However, the humans' emotional reactions to negative stimuli are resistant to habituation, irrespective of the emotional intensities of the stimuli and the information processing stage. This valence-specific habituation effect is independent of the emotional intensity of the stimuli.
Lexical semantic information plays an important role in supervised dependency parsing. In this paper, we add lexical semantic features to the feature set of a parser, obtaining improvements on the Penn Chinese Treebank. We extract semantic categories of words from HowNet, and use them as semantic information of words. Moreover, we investigate the method to compute semantic similarity between Chinese compound words, and obtain semantic information of words which did not record in HowNet. Our experiments show that unlabeled attachment scores can increase by 1.29%.
In this paper, we propose a baseline messagelevel sentiment classification method, as developed for SemEval-2015 Task 10, Subtask B. This system leverages both hand-crafted features and message-level embedding features, and uses an SVM classifier for messagelevel sentiment classification. In pre-training the embedding features, we use one million randomly-selected tweets. We present results over SemEval-2015 Task 10, Subtask B, as well as the Stanford Sentiment Treebank. Our experiments show the effectiveness of our method over both datasets.
Abstract This article measures the productivity index of the Old English suffixes -cund, -ful, and -isc as well as the prefix ful- and checks the results against the diachronic evolution of the affixes. The frameworks brought to the discussion include Type frequency measurement, as well as productivity indexes proposed by Baayen (1992, 1993, 2009) and Trips (2009). The sources are both textual (The Dictionary of Old English Corpus) and lexicographical (the lexical database of Old English Nerthus). The conclusion drawn is that Baayen's (1992, 1993, 2002) index of Global Productivity provides the most consistent results with the diachronic evolution of the affixes.
This paper presents a novel technique for empty category (EC) detection using distributed word representations. A joint model is learned from the labeled data to map both the distributed representations of the contexts of ECs and EC types to a low dimensional space. In the testing phase, the context of possible EC positions will be projected into the same space for empty category detection. Experiments on Chinese Treebank prove the effectiveness of the proposed method. We improve the precision by about 6 points on a subset of Chinese Treebank, which is a new state-ofthe-art performance on CTB.
News websites give their users the opportunity to participate in discussions about published articles, by writing comments. Typically, these comments are unstructured making it hard to understand the flow of user discussions. Thus, there is a need for organizing comments to help users to (1) gain more insights about news topics, and (2) have an easy access to comments that trigger their interests. In this work, we address the above problem by organizing comments around the entities and the aspects they discuss. More specifically, we propose an approach for entity and aspect extraction from user comments through the following contributions. First, we extend traditional Named-Entity Recognition approaches, using coreference resolution and external knowledge bases, to detect more occurrences of entities in comments. Second, we exploit part-of-speech tag, dependency tag, and lexical databases to extract explicit and implicit aspects around discussed entities. Third, we evaluate our entity and aspect extraction approach, on manually annotated data, showing that it highly increases precision and recall compared to baseline approaches.
We propose and evaluate the use of an affective-semantic model to expand the affective lexica of German, Greek, English, Spanish and Portuguese. Motivated by the assumption that semantic similarity implies affective similarity, we use word level semantic similarity scores as semantic features to estimate their corresponding affective scores. Various context-based semantic similarity metrics are investigated using contextual features that include both words and character n-grams. The model produces continuous affective ratings in three dimensions (valence, arousal and dominance) for all five languages, achieving consistent performance. We achieve classification accuracy (valence polarity task) between 85% and 91% for all five languages. For morphologically rich languages the proposed use of character n-grams is shown to improve performance.
Songs heard between the ages of 15 and 24 should be remembered better and have a stronger relationship to autobiographical memories when compared with music from other phases of life (“reminiscence bump effect”). Additionally, the proportion of music-evoked autobiographical memories (MEAMs) is at a maximum in these years of early adolescence and then declines up to the age of 60. In our study we tried both to replicate these important findings based on a German sample and to further investigate the influence of the affective characteristics of the songs on the frequency of participants’ autobiographical memories. In Experiment 1 a group of adults ( N = 48, M age = 67.1 years) listened to excerpts from 80, number-one, popular music hits from 1930 to 2010 and gave written self-reports on MEAMs. In Experiment 2 the affective characteristics were rated by another group of adults ( N = 22, M age = 66 years) and were used to predict the frequency of MEAMs. As a main result of Experiment 1, we confirmed the reminiscence bump and decline effect with a small effect size for the ratings of feelings evoked by the song and with a medium effect size for the song recognition performance of those songs released during the participants’ age range of 15 to 24 years. The total number of MEAMs was only marginally influenced by a memory bump and decline effect, and participants showed a significant proportion of MEAMs up to the fifth decade. Experiment 2 revealed that the affective ratings of the songs were unequally distributed over the two-dimensional emotion space unlike the average rate of MEAMs which was nearly equally distributed. In contrast to previous research, we therefore conclude that popular songs can be associated with autobiographical memory over five decades of life – independent of the affective character of the music.
The purpose of this paper is to present an approach to create semi-automatically ontology from Arabic texts. The whole process is supervised by a linguistic expert. Our involvement in this project focused on a lexical ontology, taking as model the WordNet ontology, and as input source, the “Arabic verbs” of a contemporary monolingual dictionary () /mζjm Alγny/ in the form a lexical database. The verb, pivot of a sentence, is our goal in creating concepts, by adopting the synset as our meaning representation model. The Markov clustering algorithm of a graph, generated by the defining verbs, obtained from the transitive closure, allowed us to detect similar verbs and to identify as well, for a given verbal entry, all of its synonyms. A tool has been implemented, and experiments have been carried out to evaluate and show efficiency of the proposed approach.
In the present study, we raised the question of whether valence information of natural emotional sounds can be extracted rapidly and unintentionally. In a first experiment, we collected explicit valence ratings of brief natural sound segments. Results showed that sound segments of 400 and 600 ms duration-and with some limitation even sound segments as short as 200 ms-are evaluated reliably. In a second experiment, we introduced an auditory version of the affective Simon task to assess automatic (i.e. unintentional and fast) evaluations of sound valence. The pattern of results indicates that affective information of natural emotional sounds can be extracted rapidly (i.e. after a few hundred ms long exposure) and in an unintentional fashion.
The purpose of this study was to reveal the effects of Westernized arrangements of traditional Korean folk music on music familiarity and preference. Two separate labs in one intact class were assigned to one of two treatment groups of either listening to traditional Korean folk songs ( n = 18) or listening to Western arrangements of the same Korean folk songs ( n = 22); a second intact class served as a control group with no listening ( n = 20). Before and after the listening treatment session, pre- and posttests were administered that included 12 music excerpts of current popular, Western classical, and traditional Korean music. Results showed that participants who listened to traditional folk songs demonstrated significant increases in both familiarity and preference ratings; however, those who listened to Westernized folk songs showed increases only in familiarity ratings but not preference ratings for the same Korean songs in traditional versions. An analysis of participants’ open-ended responses showed that affective–positive responses were used most frequently when explaining preference for traditional versions of Korean folk songs (28.1%) among the traditional Korean listening group; structural–negative reasons (47.8%) were the most frequent among the Westernized listening group.
Using functional near-infrared spectroscopy, the present study investigated how listening to differently valenced music is associated with changes in hemoglobin concentrations in the prefrontal cortex area, indicating changes in neural activity. Thirty healthy people (15 men; M age = 24.8 yr., SD = 2.4; 15 women; M age = 25.2 yr., SD = 3.1) participated. Prefrontal cortex activation, emotional responses (heart rate variability), and self-reported affective ratings were measured while listening to calm and motivational music. The songs were presented in a random counterbalanced order and separated by periods of white noise. Mixed-model repeated-measures analysis of variance (ANOVA) evaluated the relationships for main effects and interactions. The results showed that music was associated with increased activation of the prefrontal cortex area. For both sexes, listening to the motivational song was associated with higher vagal withdrawal (lower HR) than the calm song. As expected, participants rated the motivational song with greater affective valence and higher arousal. Effects persisted longer in men than in women. These findings suggest that both the characteristics of music and sex differences may significantly affect the results of emotional neuroimaging in samples of young adults.
Prior research with four-part analogies suggests that people can detect that a novel word pair (e.g., "beaver:dam") maps analogically onto a pair in memory (e.g., "robin:nest") despite being unable to retrieve the pair from memory that is driving that detection. The present study demonstrates that the same type of detection during retrieval failure can occur when a story is used at test to illustrate a common aphorism that failed to be retrieved from an earlier list (e.g., "The squeaky wheel gets the grease" or "A watched pot never boils"). Given that prior research has suggested that analogy is related to insight, the present study also examined if such analogical detection during retrieval failure is related to the sense of presque vu, which is a term used to describe the subjective sense of an impending insight or discovery. Reports of presque vu during retrieval failure were associated with higher familiarity ratings. Participants were most likely to report presque vu after failing to identify the aphorism on the first attempt but before succeeding on the second attempt (relative to succeeding on the first attempt or failing altogether on both attempts). Additionally, instances of successful identification on the second attempt after a failed first attempt were more likely when presque vu was reported than when it was not. These patterns suggest that reports of presque vu may indicate impending retrieval of as yet unretrieved relevant information. However, instances of successful second attempt identification after initial failure occurred too infrequently to fully examine whether analogical resemblance to an unretrieved studied aphorism interacted with these patterns.
Sleep supports the consolidation of declarative memory in children and adults. However, it is unclear whether sleep improves odor memory in children as well as adults. Thirty healthy children (mean age of 10.6, ranging from 8-12 yrs.) and 30 healthy adults (mean age of 25.4, ranging from 20-30 yrs.) participated in an incidental odor recognition paradigm. While learning of 10 target odorants took place in the evening and retrieval (10 target and 10 distractor odorants) the next morning in the sleep groups (adults: n = 15, children: n = 15), the time schedule was vice versa in the wake groups (n = 15 each). During encoding, adults rated odors as being more familiar. After the retention interval, adult participants of the sleep group recognized odors better than adults in the wake group. While children in the wake group showed memory performance comparable to the adult wake group, the children sleep group performed worse than adult and children wake groups. Correlations between memory performance and familiarity ratings during encoding indicate that pre-experiences might be critical in determining whether sleep improves or worsens memory consolidation.
We introduce interpolation of trained MSTParser models as a resource combination method for multi-source delexicalized parser transfer. We present both an unweighted method, as well as a variant in which each source model is weighted by the similarity of the source language to the target language. Evaluation on the HamleDT treebank collection shows that the weighted model interpolation performs comparably to weighted parse tree combination method, while being computationally much less demanding.
We examined the potential cost of practicing suppression of negative thoughts on subsequent performance in an unrelated task. Cues for previously suppressed and unsuppressed (baseline) responses in a think/no-think procedure were displayed as irrelevant flankers for neutral words to be judged for emotional valence. These critical flankers were homographs with one negative meaning denoted by their paired response during learning. Responses to the targets were delayed when suppression cues (compared with baseline cues and new negative homographs) were used as flankers, but only following direct-suppression instructions and not when benign substitutes had been provided to aid suppression. On a final recall test, suppression-induced forgetting following direct suppression and the flanker task was positively correlated with the flanker effect. Experiment 2 replicated these findings. Finally, valence ratings of neutral targets were influenced by the valence of the flankers but not by the prior role of the negative flankers.
With a dependency grammar, this study provides a unified method for calculating the syntactic complexity in linear and hierarchical dimensions. Two metrics, mean dependency distance (MDD) and mean hierarchical distance (MHD), one for each dimension, are adopted. Some results from the Czech-English dependency treebank are revealed: (1) Positive asymmetries in the distributions of the two metrics are observed in English and Czech, which indicates both languages prefer the minimalization of structural complexity in each dimension. (2) There are significantly positive correlations between sentence length (SL), MDD, and MHD. For longer sentences, English prefers to increase the MDD, while Czech tends to enhance the MHD. (3) A trade-off relationship of syntactic complexity in two dimensions is shown between the two languages. English tends to reduce the complexity of production in the hierarchical dimension, whereas Czech prefers to lessen the processing load in the linear dimension. (4) The threshold of the MDD2 and MHD2 in English and
Abstract This work presents the development and evaluation of an extended Urdu parser. It further focuses on issues related to this parser and describes the changes made in the Earley algorithm to get accurate and relevant results from the Urdu parser. The parser makes use of a morphologically rich context free grammar extracted from a linguistically-rich Urdu treebank. This grammar with sufficient encoded information is comparable with the state-of-the-art parsing requirements for the morphologically rich Urdu language. The extended parsing model and the linguistically rich extracted-grammar both provide us better evaluation results in Urdu/Hindi parsing domain. The parser gives 87% of f-score, which outperforms the existing parsing work of Urdu/Hindi based on the tree-banking approach.
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). This is the second release of UD Treebanks, Version 1.1.
This paper proposes neural networks for integrating compositional and non-compositional sentiment in the process of sentiment composition, a type of semantic composition that optimizes a sentiment objective. We enable individual composition operations in a recursive process to possess the capability of choosing and merging information from these two types of sources. We propose our models in neural network frameworks with structures, in which the merging parameters can be learned in a principled way to optimize a well-defined objective. We conduct experiments on the Stanford Sentiment Treebank and show that the proposed models achieve better results over the model that lacks this ability.
Text-based sentiment analysis is a growing research field in affective computing, driven by both commercial applications and academic interest. Continuous dimensional representations, such as valence-arousal (VA) space, can represent the affective state more precisely than discrete effective representations. In building dimensional sentiment applications, affective lexicons with valence-arousal ratings are useful resources but are still very rare. Therefore, recent studies have investigated the automatic development of VA lexicons using linear regression techniques. One of the major limitations of linear regression is the under-fitting problem which can cause a poor fit between the algorithm and the training data. To tackle this problem, this study proposes the use of a locally weighted linear regression (LWLR) model to predict the valence-arousal ratings of affective words. The locally weighted method performs a regression around the point of interest using only training data that are "local" to that point, and thus can reduce the impact of noise from unrelated training data. Experimental results show that the proposed method achieved better performance for VA word prediction.
The goal of this work is to bring semantics into the tasks of text recognition and retrieval in natural images. Although text recognition and retrieval have received a lot of attention in recent years, previous works have focused on recognizing or retrieving exactly the same word used as a query, without taking the semantics into consideration. In this paper, we ask the following question: can we predict semantic concepts directly from a word image, without explicitly trying to transcribe the word image or its characters at any point? For this goal we propose a convolutional neural network (CNN) with a weighted ranking loss objective that ensures that the concepts relevant to the query image are ranked ahead of those that are not relevant. This can also be interpreted as learning a Euclidean space where word images and concepts are jointly embedded. This model is learned in an end-to-end manner, from image pixels to semantic concepts, using a dataset of synthetically generated word images and concepts mined from a lexical database (WordNet). Our results show that, despite the complexity of the task, word images and concepts can indeed be associated with a high degree of accuracy.
This paper describes the submitted discourse parsing system of the natural language group of Soochow University (SoNLP-DP) to the CoNLL 2015 shared task. Our System classifies discourse relations into explicit and non-explicit relations and uses a pipeline platform to conduct every subtask to form an end-toend shallow discourse parser in the Penn Discourse Treebank (PDTB). Our system is evaluated on the CoNLL-2015 Shared Task closed track and achieves the 18.51% in F1-measure on the official blind test set.
We show that Combinatory Categorial Grammar (CCG) supertags can improve Telugu dependency parsing. In this process, we first extract a CCG lexicon from the dependency treebank. Using both the CCG lexicon and the dependency treebank, we create a CCG treebank using a chart parser. Exploring different morphological features of Telugu, we develop a supertagger using maximum entropy models. We provide CCG supertags as features to the Telugu dependency parser (MST parser). We get an improvement of 1.8% in the unlabelled attachment score and 2.2% in the labelled attachment score. Our results show that CCG supertags improve the MST parser, especially on verbal arguments for which it has weak rates of recovery.
Abstract: We present the multiplicative recurrent neural network as a general model for compositional meaning in language, and evaluate it on the task of fine-grained sentiment analysis. We establish a connection to the previously investigated matrix-space models for compositionality, and show they are special cases of the multiplicative recurrent net. Our experiments show that these models perform comparably or better than Elman-type additive recurrent neural networks and outperform matrix-space models on a standard fine-grained sentiment analysis corpus. Furthermore, they yield comparable results to structural deep models on the recently published Stanford Sentiment Treebank without the need for generating parse trees.
We introduce an approach to train lexicalized parsers using bilingual corpora\nobtained by merging harmonized treebanks of different languages, producing\nparsers that can analyze sentences in either of the learned languages, or even\nsentences that mix both. We test the approach on the Universal Dependency\nTreebanks, training with MaltParser and MaltOptimizer. The results show that\nthese bilingual parsers are more than competitive, as most combinations not\nonly preserve accuracy, but some even achieve significant improvements over the\ncorresponding monolingual parsers. Preliminary experiments also show the\napproach to be promising on texts with code-switching and when more languages\nare added.\n
Context: Sentences are rich in redundancy, and therefore, their identification is often facilitated by the context. The use of phrases introduces limited contextual cues into the process of identification and facilitates the evocation of words. Thus, there is a need to develop phrase recognition test to assess identification abilities. Aims: To develop and validate phrase recognition test in Kannada language for assessing speech recognition in noise. Settings and Design: Normative research design was utilized. Subjects and Methods: A total of 70 phrases in Kannada language were constructed and 67 of them were selected based on familiarity rating. Ten participants each in two groups were involved for the list equivalency and validation. Statistical Analysis Used: Repeated measure of analysis of variance was utilized for the lists equivalency and standardization. Results: Sixty-seven phrases were shortlisted from 70 phrases through familiarity rating. These phrases were embedded in different 5 signal to noise ratios (SNRs) (−9 dB SNR to −1 dB SNR in steps of 2 dB). Analysis of results showed 50% recognition score at ~−5 dB SNR. In addition, the phrases that were too easy and too difficult were eliminated. From the remaining phrases, five lists of 10 phrases each were constructed and compared for their equal intelligibility in noise. The results revealed no significant differences across the phrase lists. Conclusions: The homogenous five lists of the Kannada phrase recognition test will be useful to assess identification ability of the listeners and hearing aid benefit.
The main subject of this article is the linguistic norm and its (in)variants with regard to evaluation of lingual expressions. The article tries to show various German and Polish conceptions of linguistic norms and to point to their reference to lingual errors/mistakes and interference. Functional norm, usus, feel for language and their importance for language standardization are also discussed in the article, together with the relativity of linguistic norm.
Dutch is a pluricentric language: in Europe, it is spoken in two different countries (the Netherlands in the north and Flanders, Belgium, in the south) with differing linguistic norms. Vismans investigates what happens when the northern and southern Dutch address systems meet. His data come from in-depth radio interviews between Dutch journalists and Flemish academics. In a qualitative analysis, he tracks the development of the relationship between the two speakers and their use of address forms, as well as other markers of (in)formality. The analysis also takes into account other possible factors affecting the interaction (age, gender, residence in the other country) and pays special attention to speakers’ commentary on the variation between familiar and formal second-person pronouns.
Eating slowly is associated with a lower body mass index. However, the underlying mechanism is poorly understood. Here, our objective was to determine whether eating a meal at a slow rate improves episodic memory for the meal and promotes satiety. Participants (N=40) consumed a 400ml portion of tomato soup at either a fast (1.97ml/s) or a slow (0.50ml/s) rate. Appetite ratings were elicited at baseline and at the end of the meal (satiation). Satiety was assessed using; i) an ad libitum biscuit 'taste test' (3h after the meal) and ii) appetite ratings (collected 2h after the meal and after the ad libitum snack). Finally, to evaluate episodic memory for the meal, participants self-served the volume of soup that they believed they had consumed earlier (portion size memory) and completed a rating of memory 'vividness'. Participants who consumed the soup slowly reported a greater increase in fullness, both at the end of the meal and during the inter-meal interval. However, we found little effect of eating rate on subsequent ad libitum snack intake. Importantly, after 3h, participants who ate the soup slowly remembered eating a larger portion. These findings show that eating slowly promotes self-reported satiation and satiety. For the first time, they also suggest that eating rate influences portion size memory. However, eating slowly did not affect ratings of memory vividness and we found little evidence for a relationship between episodic memory and satiety. Therefore, we are unable to conclude that episodic memory mediates effects of eating rate on satiety.
Studies demonstrating a mnemonic benefit for encoding words in a survival scenario have revived interest in how human memory is shaped by evolutionary pressures. Prior work on the survival-processing advantage has largely examined cognitive factors as potential proximate mechanisms. The current study, by contrast, focused on the role of perceived threat. Guided by the idea that a survival scenario implies threat, we combined measures of heart rate (HR) with affective ratings to probe the potential presence of fear bradycardia as a marker of freezing--a parasympathetically dominated HR deceleration that reflects the initial stage of the defensive engagement. We replicated the mnemonic advantage in behavior and found that the survival scenario was rated higher in perceived negative arousal than a commonly used control scenario. Critically, words encountered in the survival scenario were associated with more extensive HR deceleration, and this effect was directly related to subsequent recall performance. Our findings point to a role for the involvement of neurobiological fear responses in producing the survival processing advantage, as well as potential links between autonomic changes and cognitive processing in adaptive memory.
Implementation intention (IMP) has recently been highlighted as an effective emotion regulatory strategy. Most studies examining the effectiveness of IMPs to regulate emotion have relied on self-report measures of emotional change. In two studies we employed electrodermal activity (EDA) and heart rate (HR) in addition to arousal ratings (AR) to assess the impact of an IMP on emotional responses. In Study 1, 60 participants viewed neutral and two types of negative pictures (weapon vs. non-weapon) under the IMP "If I see a weapon, then I will stay calm and relaxed!" or no self-regulatory instructions (Control). In Study 2, additionally to the Control and IMP conditions, participants completed the picture rating task either under goal intention (GI) to stay calm and relaxed or warning instructions highlighting that some pictures contain weapons. In both studies, participants showed lower EDA, reduced HR deceleration and lower AR to the weapon pictures compared to the non-weapon pictures. In Study 2, the IMP was associated with lower EDA compared to the GI condition for the weapon pictures, but not compared to the weapon pictures in the Warning condition. ARs were lower for IMP compared to GI and Warning conditions for the weapon pictures.
Earlier studies have demonstrated emotional overreactions to affective visual stimuli in patients with borderline personality disorder (BPD). However, contradictory findings regarding hyper- versus hyporeactivity have been reported for peripheral physiological measures. In order to extend previous results, the authors investigated emotional reactivity and long-term habituation in the acoustic modality. Twenty-two female BPD patients and 19 female nonclinical controls listened to emotionally negative, neutral, and positive sounds in two identical sessions. Heart rate, skin conductance, zygomaticus/corrugator muscle, and self-reported valence/arousal responses were measured. BPD patients showed weaker skin conductance responses to negative sounds than controls. The elevated zygomaticus activity in response to positive sounds observed in controls was absent in BPD patients, and BPD patients assigned lower valence ratings to positive sounds than controls. In Session 2, patients recognized fewer positive sounds than controls. Across both groups, physiological measures habituated between sessions. These findings add to growing evidence toward partial affective hyporeactivity in BPD.
Various websites are dedicated to rating physicians. The goals of this study were to: (1) evaluate the prevalence of orthopedic surgeon ratings on physician rating websites in the United States and (2) evaluate factors that may affect ratings, such as sex, practice sector (academic or private), years of practice, and geographic location. A total of 557 orthopedic surgeons selected from the 30 most populated US cities were enrolled. The study period was June 1 to July 31, 2013. Practice type (academic vs private), sex, geographic location, and years since completion of training were evaluated. For each orthopedic surgeon, numeric ratings from 7 physician rating websites were collected. The ratings were standardized on a scale of 0 to 100. Written reviews were also collected and categorized as positive or negative. Of the 557 orthopedic surgeons, 525 (94.3%) were rated at least once on 1 of the physician rating websites. The average rating was 71.4. The study included 39 female physicians (7.4%) and 486 male physicians (92.6%). There were 204 (38.9%) physicians in academic practice and 321 (61.1%) in private practice. The greatest number of physicians, 281 (50.4%), practiced in the South and Southeast, whereas 276 (49.6%) practiced in the West, Midwest, and Northeast. Those in academic practice had significantly higher ratings (74.4 vs 71.1; P<.007). No significant difference based on sex (72.5 male physicians vs 70.2 female physicians; P=.17) or geographic location (P=.11) were noted. Most comments (64.6%) were positive or extremely positive. Physicians who were in practice for 6 to 10 years had significantly higher ratings (76.9, P<.01) than those in practice for 0 to 5 years (70.5) or for 21 or more years (70.7).
Because of their superior ability to preserve sequence information over time,\nLong Short-Term Memory (LSTM) networks, a type of recurrent neural network with\na more complex computational unit, have obtained strong results on a variety of\nsequence modeling tasks. The only underlying LSTM structure that has been\nexplored so far is a linear chain. However, natural language exhibits syntactic\nproperties that would naturally combine words to phrases. We introduce the\nTree-LSTM, a generalization of LSTMs to tree-structured network topologies.\nTree-LSTMs outperform all existing systems and strong LSTM baselines on two\ntasks: predicting the semantic relatedness of two sentences (SemEval 2014, Task\n1) and sentiment classification (Stanford Sentiment Treebank).\n