Automatic Vigilance Research Articles

Emotion in language: Emotion word type and valence interactively predicted Chinese emotional word processing in emotion categorization task

AbstractThe present study examined the predicted effects of emotion word type and valence on emotional word recognition and explored their influence on emotion effects through an emotion categorization task. The results showed the influence of emotion word type and valence on emotional words recognition. Emotion‐label words exhibited faster response and higher accuracy rates (ACC) than emotion‐laden words, and positive words evoked faster response and lower ACC than negative words. Positive emotion‐label words and negative emotion‐laden words evoked higher ACC than negative emotion‐label words and positive emotion‐laden words. Besides, the findings revealed the modulation of emotion word type and valence on the emotion effects. Emotion‐label words and positive words displayed larger emotion effects than emotion‐laden words and negative words in reaction time, and only emotion‐laden words and positive emotion‐label words showed emotion effects in ACC. These findings endorsed the mediated emotion concept account, density hypothesis, and automatic vigilance hypothesis. Based on the findings, we proposed a valence cognition account to explain the interactive results.

Vigilance estimation using truncated l1 distance kernel-based sparse representation regression with physiological signals

BackgroundWith a large number of accidents caused by the decline in the vigilance of operators, finding effective automatic vigilance monitoring methods is a work of great significance in recent years. Based on physiological signals and machine learning algorithms, researchers have opened up a path for objective vigilance estimation. MethodsSparse representation (SR)-based recognition algorithms with excellent performance and simple models are very promising approaches in this field. This paper aims to study the adaptability and performance improvement of truncated l1 distance (TL1) kernel on SR-based algorithm in the context of physiological signal vigilance estimation. Compared with the traditional radial basis function (RBF), the TL1 kernel has good adaptiveness to nonlinearity and is suitable for the discrimination of complex physiological signals. A recognition framework based on TL1 and SR theory is proposed. Firstly, the inseparable physiological features are mapped to the reproducing kernel Kreĭn space through the infinite-dimensional projection of the TL1 kernel. Then the obtained kernel matrix is converted into the symmetric positive definite matrix according to the eigenspectrum approaches. Finally, the final prediction result is obtained through the sparse representation regression process. ResultsWe verified the performance of the proposed framework on the popular SEED-VIG dataset containing physiological signals (electroencephalogram and electrooculogram) associated with vigilance. In the experimental results, the TL1 kernel is superior to the RBF kernel in both performance and kernel parameter stability. ConclusionsThis demonstrates the effectiveness of the TL1 kernel in distinguishing physiological signals and the excellent vigilance estimation capability of the proposed framework. Moreover, the contribution of our research motivates the development of physiological signal recognition based on kernel methods.

Crossmodal negativity bias in semantic processing.

Negative events have greater influence on cognitive processing compared with positive events, a phenomenon referred to as the negativity bias. Previous studies have shown that reaction times (RTs) to negatively valenced items are slower in semantic tasks. According to the automatic vigilance hypothesis, these effects are caused by preferential attention to negative stimuli or features diverting cognitive resources away from semantic processing. However, it is still unclear whether the negativity bias can be modulated by affective context in a crossmodal setting and how that occurs. Experiment 1 examined individually presented pictures and words and established that participants were slower to judge negatively valenced picture and word targets in a semantic task. Experiments 2 and 3 probed the crossmodal influences of valence on subsequent semantic processing by using short music clips as primes and valenced pictures or words as targets. Both experiments demonstrated that priming negative versus positive music slowed RTs in a semantic task, irrespective of target valence. Hierarchical Bayesian drift diffusion model analyses suggest that the slow-down effects for negative conditions are mainly attributed to reduced drift rates. Together, these experiments demonstrate that negative auditory valence can impair subsequent semantic processing of visual targets in an additive fashion. These results provide further support for the automatic vigilance hypothesis. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The Effect of Emotional Valence on Auditory Word Recognition Memory in English as a Foreign Language.

The present study investigated the effect of emotional valence on auditory word recognition memory in English as a foreign language. Participants included 48 native Spanish speakers whose foreign language was English. They viewed four emotionally negative, four positive, and four neutral videos that, in total, contained 48 emotionally valenced target words. After watching the videos, participants completed an auditory word recognition memory task where target words, and the same number of fillers, were presented. The results showed a statistically significant main effect of valence on both reaction times and accuracy. Positive words were recognised more accurately and faster than neutral and negative words, but no difference between neutral and negative stimuli was found. These findings fit in well within the gradient model of automatic vigilance, which implies that emotional valence has a monotonic effect on processing latencies during auditory recognition memory in a foreign language.

Machine vision for natural gas methane emissions detection using an infrared camera

In a climate-constrained world, it is crucial to reduce natural gas methane emissions, which can potentially offset the climate benefits of replacing coal with gas. Optical gas imaging (OGI) is a widely-used method to detect methane leaks, but is labor-intensive and cannot provide leak detection results without operators’ judgment. In this paper, we develop a computer vision approach for OGI-based leak detection using convolutional neural networks (CNN) trained on methane leak images to enable automatic detection. First, we collect ∼1 M frames of labeled videos of methane leaks from different leaking equipment, covering a wide range of leak sizes (5.3–2051.6 g CH4/h) and imaging distances (4.6–15.6 m). Second, we examine different background subtraction methods to extract the methane plume in the foreground. Third, we then test three CNN model variants, collectively called GasNet, to detect plumes in videos. We assess the ability of GasNet to perform leak detection by comparing it to a baseline method that uses an optical-flow based change detection algorithm. We explore the sensitivity of results to the CNN structure, with a moderate-complexity variant performing best across distances. The generated detection probability curves show that the detection accuracy (fraction of leak and non-leak images correctly identified by the algorithm) can reach as high as 99%, the overall detection accuracy can exceed 95% across all leak sizes and imaging distances. Binary detection accuracy exceeds 97% for large leaks (∼710 g CH4/h) imaged closely (∼5–7 m). The GasNet-based computer vision approach could be deployed in OGI surveys for automatic vigilance of methane leak detection with high accuracy in the real world.

Emotion in Chinese Words Could Not Be Extracted in Continuous Flash Suppression.

Previous studies have demonstrated the automatic vigilance effect for faces and pictures and have attributed it to the brain’s prioritized unconscious evaluation of early evolutionary stimuli that are critical to survival. Whether this effect exists for evolutionarily more recent stimuli, such as written words, has become the center of much debate. Apparently contradicting results have been reported in different languages, such as Hebrew, English, and Traditional Chinese (TC), with regard to the unconscious processing of emotional words in breaking continuous flash suppression (b-CFS). Our current study used two experiments (with two-character words or single-character words) to verify whether the emotional valence or the length of Simplified Chinese (SC) words would modulate conscious access in b-CFS. We failed to replicate the findings reported in Yang and Yeh (2011) using TC, but found that complex high-level emotional information could not be extracted from interocularly suppressed words regardless of their length. Our findings comply with the distinction between subliminal and preconscious states in Global Neuronal Workspace Theory and support the current notion that preconsciousness or partial awareness may be indispensable for high-level cognitive tasks such as reading comprehension.

Evaluation of an Automatic Vigilance System for Detecting Adverse Drug Reactions from Electronic Medical Records

Evaluation of an Automatic Vigilance System for Detecting Adverse Drug Reactions from Electronic Medical Records

回避型依恋者对消极情绪信息防御性加工特征

准确加工情绪信息在人际关系管理及一般社会功能上起重要作用。不安全依恋风格影响个体的情绪加工偏向。回避型依恋者抑制消极刺激的加工，并且被看做拥有早期无意识的焦虑自我模型以及晚期控制的积极自我模型。本文主要探讨回避型依恋者加工消极情绪信息的防御性排斥特征，具体表现为一种早期警觉和晚期抑制的双重加工模型。行为证据证明了回避型依恋者在早期自动化加工阶段表现出同依恋焦虑者类似的对威胁相关信息的警觉探测。ERP成分也体现了回避者早期警觉的特征，表现在N1、P2等注意警觉相关成分以及C1、P1等基于粗略视觉特征的视觉情绪刺激加工相关的早成分更为强烈的波幅上。在晚期加工阶段这类个体则表现出对消极情绪的回避抑制加工，在表情模仿任务中出现“反转的微笑”以及stroop任务中更少的干扰效应证明其在晚期加工阶段的抑制加工特征。神经层面表现为回避型个体在认知控制脑区如背外侧前额叶、眶额皮层等脑区的激活。回避者对负性情绪的高度警觉是成功回避的前提，而这一策略在个体认知资源不足时出现失效。未来研究可进一步探讨回避型依恋与警觉-抑制注意模式的因果基础；关注防御性情绪信息加工对个体健康以及关系维持的影响及后果；深入考察去激活策略功能的有效性及其神经机制；深化回避防御在临床治疗上的应用，进一步为治疗依恋混乱相关的临床策略提供新的启示。 Accurate processing of emotional information plays an important role in the management of in-terpersonal relationship and social function. Insecure attachment style effects the process of threaten related information. Individuals with avoidant pattern of attachment are assumed to repress threaten-related signals. They are assumed to have one internal working model associated with anxiety, operating out of awareness at early, automatic stages of information processing, and another positive model operating at later, cognitively controlled stages of processing. This paper discussed the defensive characteristics of negative emotion processing of avoidant attachment. Automatic vigilance followed by effortful inhibition as a cognitive defense for avoidant attachment to processing the threaten-related signals, which is expressed in a specific dual-process model of avoidant defenses included vigilance in early automatic process and avoidance in later controlled process. The evidences from various behavioral experimental paradigms included emoticon mimicry task, morph task, perceptual threshold task, as well as emotional cueing task provided support for the point of view that attachment avoidant individuals have the capacity to detect the threaten related information in certain circumstance in the early stage of information processing. This characteristic was also proved by the evidences from event-related potentials (ERP) components, which are mainly reflected in more strong amplitude in attention vigilance related components (N1, P2) and early components related to visual features-based emotion processing when presented negative emotion information in attachment avoidant individuals. However, individuals with avoidant attachment style processed negative emotion with attention inhibition at the cognitively controlled level of processing. The avoidant individual’ tendency to “smiling” in response to the angry face at the controlled level was interpreted as a repression of their earlier, automatically evoked negative emotional reaction. Coincidentally, consistent with the proposal that avoidant individuals limit attention to potentially distressing information, attachment avoidance was associated with reductions in emotional Stroop (ES) interference for threatening related stimulus. These individuals exhibited limited cognitive resources which were devoted in this phase. The evidences from functional magnetic resonance imaging showed that during the processing of negative emotion, these individuals with attachment avoidant exhibited more strong activities in brain region in charge of advanced cognitive and control function, such as the right hemisphere superior frontal gyms (rSFG), the dorsolateral prefrontal cortex (DLPFC), the orbital frontal cortex (OFC) and so on. It can be explained that effectively avoiding processing threaten related information is based on the heightened state of vigilance for attachment avoidance. The defense strategy of attachment avoidance was considered as a secondary strategy with cognitive effort to sustain in later stage of emotion information processing. This characteristic of cognitive resource consumption made the strategy ineffective when the cognitive resource deficit. Future studies can further explore the causal basis for the relationship between avoidant attachment and vigilance-inhibi- tion attention pattern, pay attention to the impact of defensive emotional processing on individual health and the maintain of intimate relationship, explore the neural mechanism and effectiveness of the deactivation strategy and further study the application of avoid defense in clinical treatment to provide new inspiration for the treatment of attachment disorder and related clinical strategies.

APPLICATION OF S-TRANSFORM FOR AUTOMATED DETECTION OF VIGILANCE LEVEL USING EEG SIGNALS

This paper presents an S-transform-based Electroencephalogram channel optimization and feature extraction methodology for monitoring mental vigilance level of humans. Vigilance level detection methodology consists of four steps. In the first stage, two types of Electroencephalogram signals (alert and drowsy) are acquired from 30 healthy subjects and decomposed into sub-bands using the S-transform. In the second stage, permutation entropy of the S-transform coefficients is calculated and Electroencephalogram channel optimization is performed. S-transform-based statistical features are computed from the optimized Electroencephalogram channels, in the third stage. In the fourth stage, artificial intelligence techniques such as Least Square-Support Vector Machine, Artificial Neural Network and Naive Bayes Classifier are used for the classification of Electroencephalogram signals using extracted features. The performance of the feature extraction methodology is tested on the Electroencephalogram data of 30 healthy subjects. Experimental results ensured the effectiveness of proposed methodology for the estimation of mental vigilance level by using Electroencephalogram signals. It is observed that the Artificial Neural Network classifier is a good candidate for pre-emptive automatic vigilance level detection system for Brain-Computer Interface applications.

Enhanced Memory for both Threat and Neutral Information Under Conditions of Intergroup Threat.

Few studies have examined the effect of intergroup threat on cognitive outcomes such as memory. Different theoretical perspectives can inform how intergroup threat should affect memory for threat-relevant and neutral information, such as the mood-congruency approach, Yerkes–Dodson law, Easterbrook’s theory, and also evolutionary perspectives. To test among these, we conducted two experiments to examine how exposure to intergroup threats affected memory compared to control conditions. In study 1, we manipulated symbolic threat and examined participants’ memory for threat and neutral words. In study 2, memory performance was assessed following the induction of realistic threat. Across the studies, in the control condition participants showed better memory for threat-related than neutral information. However, participants under threat remembered neutral information as well as threat-related information. In addition, participants in the threat condition remembered threat-related information as well as participants in the control condition. The findings are discussed in terms of automatic vigilance processes but also the effects of threat on arousal and its effect on information processing. This latter perspective, suggests paradoxically, that under some circumstances involving an outgroup threat, non-threatening information about outgroups can be extensively processed.

Automatic processing of facial affects in patients with borderline personality disorder: associations with symptomatology and comorbid disorders.

BackgroundInstability of affects and interpersonal relations are important features of borderline personality disorder (BPD). Interpersonal problems of individuals suffering from BPD might develop based on abnormalities in the processing of facial affects and high sensitivity to negative affective expressions. The aims of the present study were to examine automatic evaluative shifts and latencies as a function of masked facial affects in patients with BPD compared to healthy individuals. As BPD comorbidity rates for mental and personality disorders are high, we investigated also the relationships of affective processing characteristics with specific borderline symptoms and comorbidity.MethodsTwenty-nine women with BPD and 38 healthy women participated in the study. The majority of patients suffered from additional Axis I disorders and/or additional personality disorders. In the priming experiment, angry, happy, neutral, or no facial expression was briefly presented (for 33 ms) and masked by neutral faces that had to be evaluated. Evaluative decisions and response latencies were registered. Borderline-typical symptomatology was assessed with the Borderline Symptom List.ResultsIn the total sample, valence-congruent evaluative shifts and delays of evaluative decision due to facial affect were observed. No between-group differences were obtained for evaluative decisions and latencies. The presence of comorbid anxiety disorders was found to be positively correlated with evaluative shifting owing to masked happy primes, regardless of baseline—neutral or no facial expression condition. The presence of comorbid depressive disorder, paranoid personality disorder, and symptoms of social isolation and self-aggression were significantly correlated with response delay due to masked angry faces, regardless of baseline.ConclusionsIn the present affective priming study, no abnormalities in the automatic recognition and processing of facial affects were observed in BPD patients compared to healthy individuals. The presence of comorbid anxiety disorders could make patients more susceptible to the influence of a happy expression on judgment processes at an automatic processing level. Comorbid depressive disorder, paranoid personality disorder, and symptoms of social isolation and self-aggression may enhance automatic attention allocation to threatening facial expressions in BPD. Increased automatic vigilance for social threat stimuli might contribute to affective instability and interpersonal problems in specific patients with BPD.

Channel optimization and nonlinear feature extraction for Electroencephalogram signals classification

In present work, a methodology for automatic vigilance level detection of human brain using nonlinear features of Electroencephalogram (EEG) signals is presented. Vigilance level detection methodology consists of three steps, EEG channels selection, feature extraction and classification. EEG signals obtained from 64 channels are sub-divided into four frequency sub-bands i.e. alpha, beta, delta and theta. Channel selection criteria Maximum Energy to Shannon Entropy ratio is applied on each frequency band to select appropriate EEG channels. EEG signals obtained from selected channels are further divided into frequency sub-bands i.e. alpha, beta and alpha–beta bands. Three nonlinear features such as Higuchi fractal dimension, Petrosian fractal dimension and Detrended Fluctuation Analysis are calculated to prepare three feature vectors respective to each frequency sub-bands. Three machine learning techniques are used for vigilance level detection such as Support Vector Machine, Least Square-Support Vector Machine and Artificial Neural Network.

Testing a dual-process model of avoidant defenses

Research suggests that the defensive strategy used by people who score high on attachment-related avoidance when they encounter interpersonal threats involves two stages: automatic vigilance followed by effortful avoidance. In two studies we varied stimulus exposure times and cognitive load to determine how attention is regulated by avoidant individuals. Results indicate that avoidant individuals were vigilant toward contempt faces when the faces were presented for 100ms but quickly disengaged from them when the faces were presented for 750ms. Moreover, avoidant participants could disengage from contempt faces presented for 750ms while rehearsing a simple 1-digit number, but not when rehearsing a 7-digit number, suggesting that avoidant disengagement is a cognitively effortful process. Theoretical and practical implications are discussed.

Emotion and language: valence and arousal affect word recognition.

Emotion influences most aspects of cognition and behavior, but emotional factors are conspicuously absent from current models of word recognition. The influence of emotion on word recognition has mostly been reported in prior studies on the automatic vigilance for negative stimuli, but the precise nature of this relationship is unclear. Various models of automatic vigilance have claimed that the effect of valence on response times is categorical, an inverted U, or interactive with arousal. In the present study, we used a sample of 12,658 words and included many lexical and semantic control factors to determine the precise nature of the effects of arousal and valence on word recognition. Converging empirical patterns observed in word-level and trial-level data from lexical decision and naming indicate that valence and arousal exert independent monotonic effects: Negative words are recognized more slowly than positive words, and arousing words are recognized more slowly than calming words. Valence explained about 2% of the variance in word recognition latencies, whereas the effect of arousal was smaller. Valence and arousal do not interact, but both interact with word frequency, such that valence and arousal exert larger effects among low-frequency words than among high-frequency words. These results necessitate a new model of affective word processing whereby the degree of negativity monotonically and independently predicts the speed of responding. This research also demonstrates that incorporating emotional factors, especially valence, improves the performance of models of word recognition.

Attention interference for emotional stimuli in cardiac interoceptive awareness

Previous research has shown that individuals with good perception of cardiac signals, referred to as cardiac interoceptive awareness, experience emotions more intensely. To investigate if emotional experience in high cardiac interoceptive awareness arises from biases in attention, we compared the performance of participants with high versus regular cardiac interoceptive awareness in an emotional Stroop task. Participants with high cardiac interoceptive awareness showed attention interference for negative words, whereas participants with regular cardiac interoceptive awareness showed attention facilitation for positive words. Our data provide further evidence for the emotion information process associated with cardiac interoceptive awareness. The results support the notion that better access to cardiac changes accompanying negative emotional conditions results in automatic vigilance for negative information and may interfere with cognitive processing.

Do Emotion Words Interfere with Processing Emotion Faces? Stroop-Like Interference versus Automatic Vigilance for Negative Information

Algom, Chajut, and Lev (2004) reported that the Emotion-Word Stroop task does not generate the cognitive processes involved in the Color-Word Stroop paradigm. An Emotion Face-Word naming task that satisfies the criteria of the Stroop paradigm is explored. Two alternative hypotheses were tested: that valence can produce Stroop-like interference or that negative information captures attention, i.e., automatic vigilance. This naming task produced facilitation and interference when the emotion words were congruent or incongruent, respectively. Vigilance effects for negative faces and negative words were also found, such that naming the faces was significantly slower when they were negative or when the superimposed words were negative.

Emotion words affect eye fixations during reading.

Emotion words are generally characterized as possessing high arousal and extreme valence and have typically been investigated in paradigms in which they are presented and measured as single words. This study examined whether a word's emotional qualities influenced the time spent viewing that word in the context of normal reading. Eye movements were monitored as participants read sentences containing an emotionally positive (e.g., lucky), negative (e.g., angry), or neutral (e.g., plain) word. Target word frequency (high or low) was additionally varied to help determine the temporal locus of emotion effects, with interactive results suggesting an early lexical locus of emotion processing. In general, measures of target fixation time demonstrated significant effects of emotion and frequency as well as an interaction. The interaction arose from differential effects with negative words that were dependent on word frequency. Fixation times on emotion words (positive or negative) were consistently faster than those on neutral words with one exception-high-frequency negative words were read no faster than their neutral counterparts. These effects emerged in the earliest eye movement measures, namely, first and single fixation duration, suggesting that emotionality, as defined by arousal and valence, modulates lexical processing. Possible mechanisms involved in processing emotion words are discussed, including automatic vigilance and desensitization, both of which imply a key role for word frequency. Finally, it is important that early lexical effects of emotion processing can be established within the ecologically valid context of fluent reading.

The automatic activation and perception of emotion in word processing: Evidence from a modified dot probe paradigm

Öhman (1993) suggested that participants more readily pay attention to negative information than neutral information. Theoretically, this “automatic vigilance hypothesis” appears to be evolutionarily derived to ensure the proper detection of threatening stimuli thereby increasing the probability of survival. The current study was designed to examine the processing of both positive and negative emotion words in a nonclinical sample using a modified version of the attentional dot probe paradigm. In Experiment 1 the words were presented without a mask; however, in Experiment 2 the words were presented quickly, then masked. Regardless of the method of presentation, the key findings from Experiments 1 and 2 were identical. Specifically, participants responded faster to the probe when it appeared in the same location as the negative words than the neutral words. Findings are discussed with regards to the automatic processing of emotion words and their influence on the perception of emotional versus nonemotional stimuli.

The effect of music‐induced mood on attentional networks

Attention network theory suggests that there are three separate neural networks that execute the discrete functions of alerting, orienting, and executive attention. Previous research on the influence of mood on attention has shown subtle and inconsistent results. The attention network theory may aid in clarifying the influence of mood on attention. The present study investigated the influence of mood on attentional networks in a normal population. Participants performed the Attention Network Test (ANT), which provides functional measures of alerting, orienting, and executive attention. Positive or negative mood was induced by listening to music with a positive or negative valence, respectively; neutral mood was induced by reading a collection of basic facts about China. The results revealed that negative mood led to a significantly higher alerting efficiency relative to other moods, while there were no significant mood effects on orienting or executive attention efficiency. According to the algorithm underlying the ANT, the higher alerting efficiency in the negative mood condition can be attributed to relatively greater benefits of cueing effects. The findings are discussed in the context of the noradrenergic system and of evolutionary significance. Specifically, the increase in the alerting function during negative mood states may be due to the modulation effect of negative mood on the noradrenergic system, and/or to the survival benefit resulting from an increase in automatic vigilance towards negative information. The current results suggest that as the influence of negative mood on attention appears to specifically consist in an enhanced alerting function, it may not be found in studies where the three attentional networks are not dissociated.

A machine learning approach to classify vigilance states in rats

Identifying mammalian vigilance states has recently become an important topic in biological science research. The vigilance states are usually categorized in at least three states, including slow wave sleep (SWS), rapid eye movement sleep (REM), and awakening. To identify different vigilance states, even a well-trained expert must spend a lot of time analyzing a mass of physiological recording data. This study proposes an automatic vigilance stages classification method for analyzing EEG signals in rats. The EEG signals were transferred by fast Fourier transform before extracting features. These extracted features were then used as training patterns to construct the proposed classification system. The proposed classification system contains two functional units. The first unit is principle component analysis (PCA) method, which is used to project the high dimensional features into the lower dimensional subspace. The second unit is the k-nearest neighbor ( k-NN) method, which identifies the physiological state in each EEG signal epoch. Based on the results of analyzing 810 epochs of EEG signal, the proposed classification method achieves satisfactory classification accuracy for vigilance states. Based on machine-learning algorithms, the classifier learns to approach the configuration that best fits the categorization task. Therefore, additional training in searching best parameters and thresholds can be avoided. Moreover, the PCA algorithm projects data instances into a 3-D space, making it possible to visualize state-changing dynamics. Experimental results show that the proposed machine-learning based classifier performs better than conventional vigilance state classification algorithms. The results also suggest that it is possible to identify the vigilance states with only EEG signals using the proposed pattern recognition technique.