Emotion, cognition, and behavior

Toward understanding nurses' decisions whether to miss care: A discrete choice experiment

BACKGROUND

Studies of missed nursing care suggest that it results from ward-level, patient-related, and task-type factors, while nurses' decision-making style was scarcely studied. Studying the effect of nurses' decision-preference structures, namely a pattern of joint ward and patient factors, on missed care may also contribute to understanding the phenomenon.

OBJECTIVES

To examine the relationships between decision-preference structures and missed care and the moderating effects of decision-making styles and task type in these links.

DESIGN

A discrete choice experiment with a between- and within-participants design.

PARTICIPANTS

A sample of 387 registered nurses working in acute medical surgical wards in Israel.

METHODS

Based on the protocol for discrete choice experiments, a survey was developed to assess the decision-preference structure, considering five factors: overload, presence of head nurse, clinical complexity, difficult patient, and presence of relatives. Participants were randomly assigned to four task-type conditions and completed a survey regarding their task. Decision-making style was assessed using a validated questionnaire.

RESULTS

Extensive workload (b = -0.46; p = 0.001), difficult patient (b = -0.20; p = 0.001), and patient clinical complexity (b = -0.10; p = 0.006) were negatively linked to the probability of missed care. The interaction between workload and task type (b = 0.252; p = 0.017) indicated that the probability of missed care under extensive compared with regular workload was lowest for developing a discharge plan and highest for providing emotional support. The interaction of patient complexity and task type (b = 0.230; p = 0.013) indicated that the probability of missed care in developing a discharge plan and medication administration was lower for patients having high compared with low clinical complexity. The interaction between difficult patient and task type (b = -0.219; p = 0.044) indicated that the probability of missed care in emotional support, developing a discharge plan, and patient's mobility was lower for difficult than for non-difficult patients. Finally, the interaction between workload and decision-making style (b = -0.48; p = 0.001) indicated that the probability of missed care under heavy compared with regular workloads was lower for the dual-preference or the dominantly intuitive styles.

CONCLUSIONS

This design enabled examining the prioritizing processes nurses use when deciding about whether to miss care. The likelihood of missing more in structured tasks is lower under a heavy overload and when patients appear difficult or clinically complex. Dual-preference styles or dominantly intuitive styles are more suitable for the routine high workload.

Automatic and controlled attentional orienting toward emotional faces in patients with Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative motor disorder that can associate with deficits in cognitive and emotional processing. In particular, PD has been reported to be mainly associated with defects in executive control and orienting attentional systems. The deficit in emotional processing mainly emerged in facial expression recognition. It is possible that the defects in emotional processing in PD may be secondary to other cognitive impairments, such as attentional deficits. This study was designed to systematically investigate the different weight of automatic and controlled attentional orienting mechanisms implied in emotional selective attention in PD. To address our purpose, we assessed drug-naïve PD patients and age-matched healthy controls with two dot-probe tasks that differed for stimuli duration. Automatic and controlled attentions were evaluated with stimuli lasting 100 ms and 500 ms, respectively. Furthermore, we introduced an emotion recognition task to investigate the performance in explicit emotion classification. The stimuli used in both the tasks dot-probe and emotion recognition were expressive faces displaying neutral, disgusted, fearful, and happy expressions.Our results showed that in PD patients, compared with healthy controls, there was 1) an alteration of automatic and controlled attentional orienting toward emotional faces in both the dot-probe tasks (with short and long durations), and 2) no difference in the emotion recognition task. These findings suggest that, from the early stages of the disease, PD can yield specific deficits in implicit emotion processing task (i.e., dot-probe task) despite a normal performance in explicit tasks that demand overt emotion recognition.

Trait anxiety is related to an impaired attention model for controllable threat cues: Evidence from ERPs

Attentional bias to threat cues is maladaptive for individuals with high trait anxiety (HTA), but may become adaptive when the dangers signaled by these cues can be controlled by timely actions. However, it remains unclear how HTA individuals allocate attention to controllable threat cues. The current study examined whether trait anxiety is associated with an impaired attention model for controllable threat cues and explored the related underlying neural mechanisms. A sample of 21 participants with low trait anxiety (LTA) and 21 with HTA completed a modified cued anticipation task which allowed participants to control the appearance of threatening pictures associated with controllable threat cues. Results revealed that HTA individuals had no difference in N1 amplitude among controllable threat cues, uncontrollable threat cues, and neutral cues, while LTA individuals showed the greatest N1 amplitude on controllable cues. HTA individuals also exhibited lower N2 amplitude than LTA individuals. The current study provides electrophysiological evidence showing that HTA individuals have impaired attention for processing controllable threat cues and weak inhibitory control. Deficient attention to controllable threat cues may be crucial in the mechanisms underlying trait anxiety.

The Association of Short-Video Problematic Use, Learning Engagement, and Perceived Learning Ineffectiveness among Chinese Vocational Students

Short videos are very popular among students, but the immersive nature of the software makes them prone to problematic use and even addiction. Internet addiction, including short-video problematic use, has been a hot topic in recent years due to the COVID-19 epidemic. However, there are few empirical studies that have explored the effects of short-video problematic use on students. Thus, vocational colleges in China were recruited to participate in this study. There were 1089 effective participants, with a valid recall rate of 90.8%. This included 466 male students (42.8%) and 623 female students (57.2%), with a mean age of 19.19 years (standard deviation of 1.07 years). Five hypotheses were proposed and validated by structural equation modeling within the framework of ecological systems theory and engagement theory to explore the association of short-video problematic use, three types of learning engagement, and perceived learning ineffectiveness. Research findings showed that: (1) short-video problematic use has a negative effect on behavioral engagement; (2) behavioral engagement has a positive effect on both emotional and cognitive engagement; and (3) emotional and cognitive engagement have a negative effect on perceived learning ineffectiveness. According to the results, it can be seen that short-video problematic use has a detrimental effect on students' learning experiences, so teachers and parents need to pay attention to the negative effects of excessive use among students.

Using Social Media & Sentiment Analysis to Make Investment Decisions

Making investment decisions by utilizing sentiment data from social media (SM) is starting to become a more tangible concept. There has been a broad investigation into this field of study over the last decade, and many of the findings have promising results. However, there is still an opportunity for continued research, firstly, in finding the most effective way to obtain relevant sentiment data from SM, then building a system to measure the sentiment, and finally visualizing it to help users make investment decisions. Furthermore, much of the existing work fails to factor SM metrics into the sentiment score effectively. This paper presents a novel prototype as a contribution to the field of study. In our work, a detailed overview of the topic is given in the form of a literature and technical review. Next, a prototype is designed and developed using the findings from the previous analysis. On top of that, a novel approach to factor SM metrics into the sentiment score is presented, with the goal of measuring the collective sentiment of the data effectively. To test the proposed approach, we only used popular stocks from the S&P500 to ensure large volumes of SM sentiment was available, adding further insight into findings, which we then discuss in our evaluation.

Too Tired and in Too Good of a Mood to Worry About Privacy: Explaining the Privacy Paradox Through the Lens of Effort Level in Information Processing

Policy-oriented Abstract

Data privacy is one of the most pressing issues today. The world is thirsty for novel, effective, and efficient policies to strike an appropriate balance between protecting individuals’ privacy and creating economic value from their personal information. Whereas governmental efforts, such as the enaction of General Data Protection Regulation, California Consumer Privacy Act, and other privacy regulations, have been pushing boundaries to strike this balance, the effects of these types of initiatives on individuals’ privacy awareness and behavior are uncertain, likely to be nuanced, and will take time to sort out. In this paper, we explain the privacy paradox, a phenomenon with important implications that apply to policymakers, industry professionals, and individuals. The privacy paradox refers to a mismatch between individuals’ stated privacy concerns and their actual disclosure behaviors. In three behavioral experiments, we show how the paradox is revealed when individuals are cognitively tired especially when they are in a good mood. These findings do not indicate that individuals do not care about privacy because they do when they are not cognitively tired especially when they are in a bad mood. By explaining the privacy paradox, we inform existing and future privacy policies to strike that balance we all strive for.

Controllability of Functional Brain Networks and Its Clinical Significance in First-Episode Schizophrenia

BACKGROUND AND HYPOTHESIS

Disrupted control of brain state transitions may contribute to the diverse dysfunctions of cognition, emotion, and behavior that are fundamental to schizophrenia. Control theory provides the rationale for evaluating brain state transitions from a controllability perspective, which may help reveal the brain mechanism for clinical features such as cognitive control deficits associated with schizophrenia. We hypothesized that brain controllability would be altered in patients with schizophrenia, and that controllability of brain networks would be related to clinical symptomatology.

STUDY DESIGN

Controllability measurements of functional brain networks, including average controllability and modal controllability, were calculated and compared between 125 first-episode never-treated patients with schizophrenia and 133 healthy controls (HCs). Associations between controllability metrics and clinical symptoms were evaluated using sparse canonical correlation analysis.

STUDY RESULTS

Compared to HCs, patients showed significantly increased average controllability (PFDR = .023) and decreased modal controllability (PFDR = .023) in dorsal anterior cingulate cortex (dACC). General psychopathology symptoms and positive symptoms were positively correlated with average controllability in regions of default mode network and negatively associated with average controllability in regions of sensorimotor, dorsal attention, and frontoparietal networks.

CONCLUSIONS

Our findings suggest that altered controllability of functional activity in dACC may play a critical role in the pathophysiology of schizophrenia, consistent with the importance of this region in cognitive and brain state control operations. The demonstration of associations of functional controllability with psychosis symptoms suggests that the identified alterations in average controllability of brain function may contribute to the severity of acute psychotic illness in schizophrenia.

Cities and neuroscience research: A systematic literature review

Background

Cities are becoming the socio-economic hubs for most of the world's population. Understanding how our surroundings can mentally affect everyday life has become crucial to integrate environmental sustainability into urban development. The present review aims to explore the empirical studies investigating neural mechanisms underlying cognitive and emotional processes elicited by the exposure to different urban built and natural spaces. It also tries to identify new research questions and to leverage neurourbanism as a framework to achieve healthier and sustainable cities.

Methods

By following the PRISMA framework, we conducted a structured search on PubMed, ProQuest, Web of Science, and Scopus databases. Only articles related to how urban environment-built or natural-affects brain activity through objective measurement (with either imaging or electrophysiological techniques) were considered. Further inclusion criteria were studies on human adult populations, peer-reviewed, and in English language.

Results

Sixty-two articles met the inclusion criteria. They were qualitatively assessed and analyzed to determine the main findings and emerging concepts. Overall, the results suggest that urban built exposure (when compared to natural spaces) elicit activations in brain regions or networks strongly related to perceptual, attentional, and (spatial) cognitive demands. The city's-built environment also triggers neural circuits linked to stress and negative affect. Convergence of these findings was observed across neuroscience techniques, and for both laboratory and real-life settings. Additionally, evidence also showed associations between neural social stress processing with urban upbringing or current city living-suggesting a mechanistic link to certain mood and anxiety disorders. Finally, environmental diversity was found to be critical for positive affect and individual well-being.

Conclusion

Contemporary human-environment interactions and planetary challenges imply greater understanding of the neurological underpinnings on how the urban space affects cognition and emotion. This review provides scientific evidence that could be applied for policy making on improved urban mental health. Several studies showed that high-quality green or blue spaces, and bio-diverse urban areas, are important allies for positive neural, cognitive, and emotional processes. Nonetheless, the spatial perception in social contexts (e.g., city overcrowding) deserves further attention by urban planners and scientists. The implications of these observations for some theories in environmental psychology and research are discussed. Future work should take advantage of technological advancements to better characterize behavior, brain physiology, and environmental factors and apply them to the remaining complexity of contemporary cities.

Responses of functional brain networks in micro-expressions: An EEG study

Micro-expressions (MEs) can reflect an individual’s subjective emotions and true mental state, and they are widely used in the fields of mental health, justice, law enforcement, intelligence, and security. However, one of the major challenges of working with MEs is that their neural mechanism is not entirely understood. To the best of our knowledge, the present study is the first to use electroencephalography (EEG) to investigate the reorganizations of functional brain networks involved in MEs. We aimed to reveal the underlying neural mechanisms that can provide electrophysiological indicators for ME recognition. A real-time supervision and emotional expression suppression experimental paradigm was designed to collect video and EEG data of MEs and no expressions (NEs) of 70 participants expressing positive emotions. Based on the graph theory, we analyzed the efficiency of functional brain network at the scalp level on both macro and micro scales. The results revealed that in the presence of MEs compared with NEs, the participants exhibited higher global efficiency and nodal efficiency in the frontal, occipital, and temporal regions. Additionally, using the random forest algorithm to select a subset of functional connectivity features as input, the support vector machine classifier achieved a classification accuracy for MEs and NEs of 0.81, with an area under the curve of 0.85. This finding demonstrates the possibility of using EEG to recognize MEs, with a wide range of application scenarios, such as persons wearing face masks or patients with expression disorders.

Aversive memory formation in humans involves an amygdala-hippocampus phase code

Memory for aversive events is central to survival but can become maladaptive in psychiatric disorders. Memory enhancement for emotional events is thought to depend on amygdala modulation of hippocampal activity. However, the neural dynamics of amygdala-hippocampal communication during emotional memory encoding remain unknown. Using simultaneous intracranial recordings from both structures in human patients, here we show that successful emotional memory encoding depends on the amygdala theta phase to which hippocampal gamma activity and neuronal firing couple. The phase difference between subsequently remembered vs. not-remembered emotional stimuli translates to a time period that enables lagged coherence between amygdala and downstream hippocampal gamma. These results reveal a mechanism whereby amygdala theta phase coordinates transient amygdala -hippocampal gamma coherence to facilitate aversive memory encoding. Pacing of lagged gamma coherence via amygdala theta phase may represent a general mechanism through which the amygdala relays emotional content to distant brain regions to modulate other aspects of cognition, such as attention and decision-making.

Do we enjoy what we sense and perceive? A dissociation between aesthetic appreciation and basic perception of environmental objects or events

This integrative review rearticulates the notion of human aesthetics by critically appraising the conventional definitions, offerring a new, more comprehensive definition, and identifying the fundamental components associated with it. It intends to advance holistic understanding of the notion by differentiating aesthetic perception from basic perceptual recognition, and by characterizing these concepts from the perspective of information processing in both visual and nonvisual modalities. To this end, we analyze the dissociative nature of information processing in the brain, introducing a novel local-global integrative model that differentiates aesthetic processing from basic perceptual processing. This model builds on the current state of the art in visual aesthetics as well as newer propositions about nonvisual aesthetics. This model comprises two analytic channels: aesthetics-only channel and perception-to-aesthetics channel. The aesthetics-only channel primarily involves restricted local processing for quality or richness (e.g., attractiveness, beauty/prettiness, elegance, sublimeness, catchiness, hedonic value) analysis, whereas the perception-to-aesthetics channel involves global/extended local processing for basic feature analysis, followed by restricted local processing for quality or richness analysis. We contend that aesthetic processing operates independently of basic perceptual processing, but not independently of cognitive processing. We further conjecture that there might be a common faculty, labeled as aesthetic cognition faculty, in the human brain for all sensory aesthetics albeit other parts of the brain can also be activated because of basic sensory processing prior to aesthetic processing, particularly during the operation of the second channel. This generalized model can account not only for simple and pure aesthetic experiences but for partial and complex aesthetic experiences as well.

Law Enforcement Officers’ Ability to Recognize Emotions: The Role of Personality Traits and Basic Needs’ Satisfaction

Background: This study intended to explore the role of personality traits and basic psychological needs in law enforcement officers’ ability to recognize emotions: anger, joy, sadness, fear, surprise, disgust, and neutral. It was significant to analyze law enforcement officers’ emotion recognition and the contributing factors, as this field has been under-researched despite increased excessive force use by officers in many countries. Methods: This study applied the Big Five–2 (BFI-2), the Basic Psychological Needs Satisfaction and Frustration Scale (BPNSFS), and the Karolinska Directed Emotional Faces set of stimuli (KDEF). The data was gathered using an online questionnaire provided directly to law enforcement agencies. A total of 154 law enforcement officers participated in the study, 50.65% were females, and 49.35% were males. The mean age was 41.2 (age range = 22–61). In order to analyze the data, SEM and multiple linear regression methods were used. Results: This study analyzed variables of motion recognition, personality traits, and needs satisfaction and confirmed that law enforcement officers’ personality traits play a significant role in emotion recognition. Respondents’ agreeableness significantly predicted increased overall emotion recognition; conscientiousness predicted increased anger recognition; joy recognition was significantly predicted by extraversion, neuroticism, and agreeableness. This study also confirmed that law enforcement officers’ basic psychological needs satisfaction/frustration play a significant role in emotion recognition. Respondents’ relatedness satisfaction significantly predicted increased overall emotion recognition, fear recognition, joy recognition, and sadness recognition. Relatedness frustration significantly predicted decreased anger recognition, surprise recognition, and neutral face recognition. Furthermore, this study confirmed links between law enforcement officers’ personality traits, satisfaction/frustration of basic psychological needs, and emotion recognition, χ2 = 57.924; df = 41; p = 0.042; TLI = 0.929; CFI = 0.956; RMSEA = 0.042 [0.009–0.065]. Discussion: The findings suggested that agreeableness, conscientiousness, extraversion, and neuroticism play an essential role in satisfaction and frustration of relatedness needs, which, subsequently, link to emotion recognition. Due to the relatively small sample size, the issues of validity/reliability of some instruments, and other limitations, the results of this study should preferably be regarded with concern.

Insular cortical circuits as an executive gateway to decipher threat or extinction memory via distinct subcortical pathways

Threat and extinction memories are crucial for organisms’ survival in changing environments. These memories are believed to be encoded by separate ensembles of neurons in the brain, but their whereabouts remain elusive. Using an auditory fear-conditioning and extinction paradigm in male mice, here we discovered that two distinct projection neuron subpopulations in physical proximity within the insular cortex (IC), targeting the central amygdala (CeA) and nucleus accumbens (NAc), respectively, to encode fear and extinction memories. Reciprocal intracortical inhibition of these two IC subpopulations gates the emergence of either fear or extinction memory. Using rabies-virus-assisted tracing, we found IC-NAc projection neurons to be preferentially innervated by intercortical inputs from the orbitofrontal cortex (OFC), specifically enhancing extinction to override fear memory. These results demonstrate that IC serves as an operation node harboring distinct projection neurons that decipher fear or extinction memory under the top-down executive control from OFC.

Ensembles of fear and extinction memories compete and interact to drive opposing behaviors. Here the authors identified insular cortical circuits as an executive gateway that decipher between fear and extinction memories via distinct subcortical pathways.

Preverbal infants tune manual choices on subliminal affective information

Human behaviour is often shaped by unconscious emotional cues. From early on, infants are able to process emotional signals even if presented subliminally; however, whether subliminal emotional expressions are capable to affect infants' behaviour remains unknown. The current study aimed to fill this gap, recording 8-10-month-old infants' looking time and manual choice toward two objects previously associated to subliminal emotional faces. Results demonstrated that infants' manual choice, but not looking time, was guided by the previously presented subliminal emotional signal, as infants preferred to choose the object associated to the happy face. Overall, our findings show that preverbal infants tune their behaviour based on affective information, which drives them towards or away from previous encounters, even outside conscious awareness.

The relationship between self-reported ability emotional intelligence and risky driving behaviour: Consequences for accident and traffic ticket rate

Road safety represents one of the main public health issues worldwide, and risky driving behaviour is one of the most predominant factors in traffic road accidents. The primary objective of this research was to clarify the relationship between emotional intelligence (EI) abilities and the probability of engaging in risky behaviour during driving. Previous literature linking these constructs is limited, and research has yielded mixed findings. In the present study, 555 drivers from a Spanish community sample (M_age = 39.34, ranging from 18 to 79 years old; 49.19% women) were assessed on risky driving behaviour using the Dula Dangerous Driving Index while self-reported ability EI was measured using the Wong and Law Emotional Intelligence Scale. Gender, age, and driving experience were controlled. The results of this study revealed that a higher self-reported ability EI, particularly the ability to regulate emotions, was related to a lower tendency to engage in risky driving behaviours. In turn, self-reported ability EI was negatively and indirectly related to the number of road accidents and traffic tickets through the mediating effect of risky driving. The regulation of emotions (via direct and indirect effect) and the appraisal of the emotions of others (via direct effect) were the EI abilities that better predicted the number of accidents and traffic tickets. We discuss the practical implications of these findings, along with suggested future lines of research.

Simultaneously exploring multi-scale and asymmetric EEG features for emotion recognition

In recent years, emotion recognition based on electroencephalography (EEG) has received growing interests in the brain-computer interaction (BCI) field. The neuroscience researches indicate that the left and right brain hemispheres demonstrate activity differences under different emotional activities, which could be an important principle for designing deep learning (DL) model for emotion recognition. Besides, owing to the nonstationarity of EEG signals, using convolution kernels of a single size may not sufficiently extract the abundant features for EEG classification tasks. Based on these two angles, we proposed a model termed Multi-Scales Bi-hemispheric Asymmetric Model (MSBAM) based on convolutional neural network (CNN) structure. Evaluated on the public DEAP and DREAMER datasets, MSBAM achieved over 99% accuracy for the two-class classification of low-level and high-level states in each of four emotional dimensions, i.e., arousal, valence, dominance and liking, respectively. This study further demonstrated the promising potential to design the DL model from the multi-scale characteristics of the EEG data and the neural mechanisms of the emotion cognition.

Evidence for dynamic attentional bias toward positive emotion-laden words: A behavioral and electrophysiological study

There has been no consensus on the neural dissociation between emotion-label and emotion-laden words, which remains one of the major concerns in affective neurolinguistics. The current study adopted dot-probe tasks to investigate the valence effect on attentional bias toward Chinese emotion-label and emotion-laden words. Behavioral data showed that emotional word type and valence interacted in attentional bias scores with an attentional bias toward positive emotion-laden words rather than positive emotion-label words and that this bias was derived from the disengagement difficulty in positive emotion-laden words. In addition, an attentional bias toward negative emotion-label words relative to positive emotion-label words was observed. The event-related potential (ERP) data demonstrated an interaction between emotional word type, valence, and hemisphere. A significant hemisphere effect was observed during the processing of positive emotion-laden word pairs rather than positive emotion-label, negative emotion-label, and negative emotion-laden word pairs, with positive emotion-laden word pairs eliciting an enhanced P1 in the right hemisphere as compared to the left hemisphere. Our results found a dynamic attentional bias toward positive emotion-laden words; individuals allocated more attention to positive emotion-laden words in the early processing stage and had difficulty disengaging attention from them in the late processing stage.

Grounding Context in Embodied Cognitive Robotics

Biological agents are context-dependent systems that exhibit behavioral flexibility. The internal and external information agents process, their actions, and emotions are all grounded in the context within which they are situated. However, in the field of cognitive robotics, the concept of context is far from being clear with most studies making little to no reference to it. The aim of this paper is to provide an interpretation of the notion of context and its core elements based on different studies in natural agents, and how these core contextual elements have been modeled in cognitive robotics, to introduce a new hypothesis about the interactions between these contextual elements. Here, global context is categorized as agent-related, environmental, and task-related context. The interaction of their core elements, allows agents to first select self-relevant tasks depending on their current needs, or for learning and mastering their environment through exploration. Second, to perform a task and continuously monitor its performance. Third, to abandon a task in case its execution is not going as expected. Here, the monitoring of prediction error, the difference between sensorimotor predictions and incoming sensory information, is at the core of behavioral flexibility during situated action cycles. Additionally, monitoring prediction error dynamics and its comparison with the expected reduction rate should indicate the agent its overall performance on executing the task. Sensitivity to performance evokes emotions that function as the driving element for autonomous behavior which, at the same time, depends on the processing of the interacting core elements. Taking all these into account, an interactionist model of contexts and their core elements is proposed. The model is embodied, affective, and situated, by means of the processing of the agent-related and environmental core contextual elements. Additionally, it is grounded in the processing of the task-related context and the associated situated action cycles during task execution. Finally, the model proposed here aims to guide how artificial agents should process the core contextual elements of the agent-related and environmental context to give rise to the task-related context, allowing agents to autonomously select a task, its planning, execution, and monitoring for behavioral flexibility.

Examining implicit positive affect toward suicide among suicidal and nonsuicidal adults and adolescents

OBJECTIVE

The purpose of this study was to examine implicit affect toward suicide (i.e., how good/bad suicide is perceived). Some people might be more likely to think about/choose suicide because they perceive it as a good option (to gain relief) relative to available alternatives.

METHOD

Implicit affect toward suicide among adults (N = 72) and adolescents (N = 174) with and without suicidal thoughts was examined using first-person (FP) perspective suicide pictures in the affect misattribution procedure (AMP).

RESULTS

Suicidal adults' implicit positive affect toward suicide was associated with STB variables, such as explicit valence (r = 0.34) and arousal (r = 0.44) ratings of suicide pictures, and implicit affect differentiated groups above and beyond explicit valence ratings. Contrary to our hypothesis, suicidal participants did not display higher implicit positive affect toward suicide than nonsuicidal participants. However, suicidal participants displayed consistent implicit affect toward different suicide pictures, whereas nonsuicidal participants evaluated some pictures as more pleasant than others (ORs = 1.92-2.27).

CONCLUSIONS

Implicit affect toward suicide may relate to STB, but stimuli characteristics (e.g., color) likely influence the accuracy of assessment with the AMP and should be a focus of future research involving this and other implicit measures.

The Impact of Chronic Unpredictable Mild Stress-Induced Depression on Spatial, Recognition and Reference Memory Tasks in Mice: Behavioral and Histological Study

Depression-induced cognitive impairment has recently been given more attention in research. However, the relationship between depression and different types of memory is still not clear. Chronic unpredictable mild stress (CUMS) is a commonly used animal model of depression in which animals are exposed to chronic unpredictable environmental and psychological stressors, which mimics daily human life stressors. This study investigated the impact of different durations of CUMS on various types of memory (short- and long-term spatial memory and recognition memory) and investigated CUMS’ impact on the ultrastructural level by histological assessment of the hippocampus and prefrontal cortex. Twenty male C57BL/J6 mice (6 weeks old, 21.8 ± 2 g) were randomly divided into two groups (n = 10): control and CUMS (8 weeks). A series of behavioral tasks were conducted twice at weeks 5–6 (early CUMS) and weeks 7–8 (late CUMS). A tail-suspension test (TST), forced swimming test (FST), elevated zero maze (EZM), elevated plus maze (EPM), open field test (OFT), and sucrose-preference test (SPT) were used to assess anxiety and depressive symptoms. The cognitive function was assessed by the novel object recognition test (NORT; for recognition memory), Y-maze (for short-term spatial memory), and Morris water maze (MWM: for long-term spatial memory) with a probe test (for reference memory). Our data showed that 8 weeks of CUMS increased the anxiety level, reported by a significant increase in anxiety index in both EPM and EZM and a significant decrease in central preference in OFT, and depression was reported by a significant increase in immobility in the TST and FST and sucrose preference in the SPT. Investigating the impact of CUMS on various types of memory, we found that reference memory is the first memory to be affected in early CUMS. In late CUMS, all types of memory were impaired, and this was consistent with the abnormal histological features of the memory-related areas in the brain (hippocampus and prefrontal cortex).

Unravelling the impact of frontal lobe impairment for social dysfunction in myotonic dystrophy type 1

Myotonic dystrophy type 1 is an autosomal dominant multisystemic disorder affecting muscular and extra muscular systems, including the central nervous system. Cerebral involvement in myotonic dystrophy type 1 is associated with subtle cognitive and behavioural disorders, of major impact on socio-professional adaptation. The social dysfunction and its potential relation to frontal lobe neuropsychology remain under-evaluated in this pathology. The neuroanatomical network underpinning that disorder is yet to disentangle. Twenty-eight myotonic dystrophy type 1 adult patients (mean age: 46 years old) and 18 age and sex-matched healthy controls were included in the study. All patients performed an exhaustive neuropsychological assessment with a specific focus on frontal lobe neuropsychology (motivation, social cognition and executive functions). Among them, 18 myotonic dystrophy type 1 patients and 18 healthy controls had a brain MRI with T1 and T2 Flair sequences. Grey matter segmentation, Voxel-based morphometry and cortical thickness estimation were performed with Statistical Parametric Mapping Software SPM12 and Freesurfer software. Furthermore, T2 white matter lesions and subcortical structures were segmented with Automated Volumetry Software. Most patients showed significant impairment in executive frontal functions (auditory working memory, inhibition, contextualization and mental flexibility). Patients showed only minor difficulties in social cognition tests mostly in cognitive Theory of Mind, but with relative sparing of affective Theory of Mind and emotion recognition. Neuroimaging analysis revealed atrophy mostly in the parahippocampal and hippocampal regions and to a lesser extent in basal ganglia, regions involved in social navigation and mental flexibility, respectively. Social cognition scores were correlated with right parahippocampal gyrus atrophy. Social dysfunction in myotonic dystrophy type 1 might be a consequence of cognitive impairment regarding mental flexibility and social contextualization rather than a specific social cognition deficit such as emotion recognition. We suggest that both white matter lesions and grey matter disease could account for this social dysfunction, involving, in particular, the frontal-subcortical network and the hippocampal/arahippocampal regions, brain regions known, respectively, to integrate contextualization and social navigation.

Association between resting-state EEG oscillation and psychometric properties in perimenopausal women

Background

The perimenopausal period is associated with a higher risk of various mood disorders. Similarly, although resting-state electroencephalogram (rsEEG) brain oscillatory activity has been associated with various neuropsychological disorders and behaviours, these issues have not been assessed in perimenopausal women. This study aimed to evaluate quantitative relationships between psychometric properties and rsEEG rhythms (delta, theta, alpha, beta and gamma powers) in perimenopausal women.

Methods

A cross-sectional correlational descriptive study was conducted to quantitatively analyze the correlations between rsEEG low-to-high band activities (delta, theta, alpha, beta, and gamma powers) and psychometric properties in 14 perimenopausal women. Participants completed a psychological inventory comprising the State Anxiety Inventory (SAI), Depression Inventory (DI), Behavioural Inhibition Scale (BIS) and short-form UPPS Impulsive Behaviour Scale (IS) before EEG recording.

Results

Results showed that impulsivity was positively related to the beta power, symmetrical at most channels (frontal, temporal, central, parietal and occipital regions; p < .05); but did not related to the delta, theta, alpha and gamma powers. The brainwave low-to-high bands, delta, theta, alpha, beta and gamma power were not associated with DI, SAI or BIS scores.

Conclusions

This study’s findings propose that significantly enhanced resting-state beta activity is a trait of impulsivity in perimenopausal women. Therefore, results have potential implications for the preclinical or clinical evaluation of these issues in perimenopausal women.

Multi-Model Adaptation Learning With Possibilistic Clustering Assumption for EEG-Based Emotion Recognition

In machine learning community, graph-based semi-supervised learning (GSSL) approaches have attracted more extensive research due to their elegant mathematical formulation and good performance. However, one of the reasons affecting the performance of the GSSL method is that the training data and test data need to be independently identically distributed (IID); any individual user may show a completely different encephalogram (EEG) data in the same situation. The EEG data may be non-IID. In addition, noise/outlier sensitiveness still exist in GSSL approaches. To these ends, we propose in this paper a novel clustering method based on structure risk minimization model, called multi-model adaptation learning with possibilistic clustering assumption for EEG-based emotion recognition (MA-PCA). It can effectively minimize the influence from the noise/outlier samples based on different EEG-based data distribution in some reproduced kernel Hilbert space. Our main ideas are as follows: (1) reducing the negative impact of noise/outlier patterns through fuzzy entropy regularization, (2) considering the training data and test data are IID and non-IID to obtain a better performance by multi-model adaptation learning, and (3) the algorithm implementation and convergence theorem are also given. A large number of experiments and deep analysis on real DEAP datasets and SEED datasets was carried out. The results show that the MA-PCA method has superior or comparable robustness and generalization performance to EEG-based emotion recognition.

Robust Latent Multi-Source Adaptation for Encephalogram-Based Emotion Recognition

In practical encephalogram (EEG)-based machine learning, different subjects can be represented by many different EEG patterns, which would, in some extent, degrade the performance of extant subject-independent classifiers obtained from cross-subjects datasets. To this end, in this paper, we present a robust Latent Multi-source Adaptation (LMA) framework for cross-subject/dataset emotion recognition with EEG signals by uncovering multiple domain-invariant latent subspaces. Specifically, by jointly aligning the statistical and semantic distribution discrepancies between each source and target pair, multiple domain-invariant classifiers can be trained collaboratively in a unified framework. This framework can fully utilize the correlated knowledge among multiple sources with a novel low-rank regularization term. Comprehensive experiments on DEAP and SEED datasets demonstrate the superior or comparable performance of LMA with the state of the art in the EEG-based emotion recognition.

Knowledge of Animal Welfare and Consumers’ Behavioral Intentions in China: A Moderated Mediation Model of Product Cognition and Empathy

Simple Summary

In this study, consumer perceptions of animal welfare have been assessed. The results can strongly support the development of policies and legislation regarding animal-friendly production. In China, the demand for animal-friendly products is increasing, but so far, the research on the relationship between the knowledge of animal welfare and animal-friendly consuming intentions is limited. The objective of this study was to examine the impact of the knowledge of animal welfare on consumers’ behavioral intentions and its mechanism. The survey covered 1499 food consumers in Guangdong province, China. Our empirical results suggest that increasing knowledge of animal welfare is significantly positive for the intention of animal-friendly products consumption. Furthermore, empathy moderates the indirect effect between animal-friendly product cognition and the behavioral intention both to purchase and recommend.

Abstract

As purchase power and consumption knowledge increase, consumers gradually demand safer and healthier products. Animal welfare is expected to be an important attribute of high-end food in the future and a major concern for the high-quality development of the livestock industry. The objective was to shed new light on our understanding of consumers’ perceptions and behavioral intentions toward animal-friendly food. Using sample data of 1499 food consumers in Guangdong province, China, this study explored the role of product cognition and empathy in the relationship between consumers’ knowledge and behavioral intentions. Results indicate that knowledge of animal welfare significantly influences consumers’ behavioral intentions, and there is a mediating effect on cognition. Meanwhile, empathy moderates the relationship between product cognition and consumers’ intentions to purchase or recommend animal-friendly products. Improving consumers’ knowledge of animal welfare and cognitive levels of animal-friendly products may contribute to promoting animal-friendly product consumption and sustainable development of the livestock industry.

Attention and regulation during emotional word comprehension in older adults: Evidence from event-related potentials and brain oscillations

Older adults often show a positivity bias effect during picture processing, focusing more on positive than negative information. It is unclear whether this positivity bias effect generalizes to language and whether arousal matters. The present study investigated how age affects emotional word comprehension with varied valence (positive, negative) and arousal (high, low). We recorded older and younger participants' brainwaves (EEG) while they read positive/negative and high/low-arousing words and pseudowords, and made word/non-word judgments. Older adults showed increased N400s and left frontal alpha decreases (300-450 ms) for low-arousing positive as compared to low-arousing negative words, suggesting an arousal-dependent positivity bias during lexical retrieval. Both age groups showed similar LPPs to negative words. Older adults further showed a larger mid-frontal theta increase (500-700 ms) than younger adults for low-arousing negative words, possibly indicating down-regulation of negative meanings of low-arousing words. Altogether, our data supported the strength and vulnerability integration model of aging.

A Study of Subliminal Emotion Classification Based on Entropy Features

Electroencephalogram (EEG) has been widely utilized in emotion recognition. Psychologists have found that emotions can be divided into conscious emotion and unconscious emotion. In this article, we explore to classify subliminal emotions (happiness and anger) with EEG signals elicited by subliminal face stimulation, that is to select appropriate features to classify subliminal emotions. First, multi-scale sample entropy (MSpEn), wavelet packet energy (E_i), and wavelet packet entropy (WpEn) of EEG signals are extracted. Then, these features are fed into the decision tree and improved random forest, respectively. The classification accuracy with E_i and WpEn is higher than MSpEn, which shows that E_i and WpEn can be used as effective features to classify subliminal emotions. We compared the classification results of different features combined with the decision tree algorithm and the improved random forest algorithm. The experimental results indicate that the improved random forest algorithm attains the best classification accuracy for subliminal emotions. Finally, subliminal emotions and physiological proof of subliminal affective priming effect are discussed.

Closed-Loop Tracking and Regulation of Emotional Valence State From Facial Electromyogram Measurements

Affective studies provide essential insights to address emotion recognition and tracking. In traditional open-loop structures, a lack of knowledge about the internal emotional state makes the system incapable of adjusting stimuli parameters and automatically responding to changes in the brain. To address this issue, we propose to use facial electromyogram measurements as biomarkers to infer the internal hidden brain state as feedback to close the loop. In this research, we develop a systematic way to track and control emotional valence, which codes emotions as being pleasant or obstructive. Hence, we conduct a simulation study by modeling and tracking the subject's emotional valence dynamics using state-space approaches. We employ Bayesian filtering to estimate the person-specific model parameters along with the hidden valence state, using continuous and binary features extracted from experimental electromyogram measurements. Moreover, we utilize a mixed-filter estimator to infer the secluded brain state in a real-time simulation environment. We close the loop with a fuzzy logic controller in two categories of regulation: inhibition and excitation. By designing a control action, we aim to automatically reflect any required adjustments within the simulation and reach the desired emotional state levels. Final results demonstrate that, by making use of physiological data, the proposed controller could effectively regulate the estimated valence state. Ultimately, we envision future outcomes of this research to support alternative forms of self-therapy by using wearable machine interface architectures capable of mitigating periods of pervasive emotions and maintaining daily well-being and welfare.

Depression Assessment Method: An EEG Emotion Recognition Framework Based on Spatiotemporal Neural Network

The main characteristic of depression is emotional dysfunction, manifested by increased levels of negative emotions and decreased levels of positive emotions. Therefore, accurate emotion recognition is an effective way to assess depression. Among the various signals used for emotion recognition, electroencephalogram (EEG) signal has attracted widespread attention due to its multiple advantages, such as rich spatiotemporal information in multi-channel EEG signals. First, we use filtering and Euclidean alignment for data preprocessing. In the feature extraction, we use short-time Fourier transform and Hilbert-Huang transform to extract time-frequency features, and convolutional neural networks to extract spatial features. Finally, bi-directional long short-term memory explored the timing relationship. Before performing the convolution operation, according to the unique topology of the EEG channel, the EEG features are converted into 3D tensors. This study has achieved good results on two emotion databases: SEED and Emotional BCI of 2020 WORLD ROBOT COMPETITION. We applied this method to the recognition of depression based on EEG and achieved a recognition rate of more than 70% under the five-fold cross-validation. In addition, the subject-independent protocol on SEED data has achieved a state-of-the-art recognition rate, which exceeds the existing research methods. We propose a novel EEG emotion recognition framework for depression detection, which provides a robust algorithm for real-time clinical depression detection based on EEG.

Altered regional brain activity and functional connectivity patterns in major depressive disorder: A function of childhood trauma or diagnosis?

Childhood trauma (CT) is a non-specific risk factor for major depressive disorder (MDD). However, the neurobiological mechanisms of MDD with CT remain unclear. In the present study, we sought to determine the specific brain regions associated with CT and MDD etiology. Fractional amplitude of low-frequency fluctuation (fALFF) and functional connectivity (FC) analyses were performed to assess alterations of intrinsic brain activity in MDD with CT, MDD without CT, healthy controls with CT, and healthy controls without CT. Two-by-two factorial analyses were performed to examine the effects of the factors "MDD" and "CT" on fALFF and FC. Moderator analysis was used to explore whether the severity of depression moderated the relationship between CT and aberrant fALFF. We found that the etiological effects of MDD and CT exhibited negative impacts on brain dysfunction including altered fALFF in the left postcentral gyrus, left lingual gyrus, left paracentral lobule (PCL), and left cuneus. Decreased FC was observed in the following regions: (i) the left lingual gyrus seed and the left fusiform gyrus as well as the right calcarine cortex; (ii) the left PCL seed and the left supplementary motor area, left calcarine cortex, left precentral gyrus, and right cuneus; (iii) the left postcentral gyrus seed and left superior parietal lobule, right postcentral gyrus, and left precentral gyrus. Furthermore, the severity of depression acted as a moderator in the relationship between CT and aberrant fALFF in the left PCL. These data indicate that MDD patients with and without trauma exposure are clinically and neurobiologically distinct.

Evidence on the Relationship Between Emotional Intelligence and Risk Behavior: A Systematic and Meta-Analytic Review

The aim of the present study was to carry out a qualitative and quantitative synthesis of the existing literature studying the relationship between emotional intelligence and risk behavior. We conducted a systematic review and meta-analysis of the scientific evidence available relating both constructs. Particular attention was paid to identifying possible differences in this relationship as a function of the different conceptualizations of EI and the risk domain. The study was conducted following the Cochrane and PRISMA guidelines. Our results revealed a significant negative relationship between EI and health-related risk behaviors. However, this relationship was not observed in other risk domains such as finance and gambling. The relationship between EI and risk behavior differed according to the risk domain studied, which supports the notion that risk is a domain-specific construct. The results associated with the health-related risk behaviors are consistent with existing literature about the positive impact of emotional abilities on the health domain. A more complete understanding of the emotional mechanisms that underlie risk behavior could help to establish action guidelines and improve programmes to prevent and reduce the negative effects of risk behavior on our society.

Dynamics of Functional Network Organization Through Graph Mixture Learning

Understanding the organizational principles of human brain activity at the systems level remains a major challenge in network neuroscience. Here, we introduce a fully data-driven approach based on graph learning to extract meaningful repeating network patterns from regionally-averaged timecourses. We use the Graph Laplacian Mixture Model (GLMM), a generative model that treats functional data as a collection of signals expressed on multiple underlying graphs. By exploiting covariance between activity of brain regions, these graphs can be learned without resorting to structural information. To validate the proposed technique, we first apply it to task fMRI with a known experimental paradigm. The probability of each graph to occur at each time-point is found to be consistent with the task timing, while the spatial patterns associated to each epoch of the task are in line with previously established activation patterns using classical regression analysis. We further on apply the technique to resting state data, which leads to extracted graphs that correspond to well-known brain functional activation patterns. The GLMM allows to learn graphs entirely from the functional activity that, in practice, turn out to reveal high degrees of similarity to the structural connectome. The Default Mode Network (DMN) is always captured by the algorithm in the different tasks and resting state data. Therefore, we compare the states corresponding to this network within themselves and with structure. Overall, this method allows us to infer relevant functional brain networks without the need of structural connectome information. Moreover, we overcome the limitations of windowing the time sequences by feeding the GLMM with the whole functional signal and neglecting the focus on sub-portions of the signals.

The effect of unexpected rewards on decision making in cuttlefish

Despite numerous studies demonstrating the cognitive ability of cephalopods, there is currently no study showing an emotion-like behavior in this group of animals. To examine whether cuttlefish have different internal states, we developed a behavioral paradigm to assess if prior surprised events are able to alter the choice made by cuttlefish. By presenting unexpected food rewards to cuttlefish before the test, we investigated whether the reaction time of choosing between two shrimps, an intuitive response toward the prey without previous learning, at three different levels of discriminative tests (easy, difficult, and ambiguous), are different compared to the one without an unexpected reward. This behavioral paradigm serves to demonstrate whether cuttlefish are aware of ambiguous situations, and their choice outcome and reaction time are dependent of their internal states. The results show that the response latency was significantly shortened in the difficult and ambiguous tests when choosing from two shrimps that are either moderately different in size or similar sizes, respectively, when cuttlefish have received unexpected rewards before the test. These results were compared with tests during which the cuttlefish did not receive any reward in advance. Furthermore, this shortening of latency did not result in a difference in choice outcome during the difficult and ambiguous tests. Interestingly, even when cuttlefish have obtained the expected food rewards or simply made tentacular strike without prey capture each time before test, these prior experiences were sufficient to shorten the response latency in the difficult and ambiguous tests. However, different from the result of unexpected rewards, food consumption alone or prey capture failure did affect the choice outcome during the simple and difficult tests. Taken together, our findings suggest that pre-test treatments of unexpected and expected rewards or simply unsuccessful visual attack seem to induce cuttlefish to adopt different foraging behaviors. This context dependent decision making suggests that cuttlefish’s foraging strategies are influenced by the previously surprised event and their internal states. It also shows a speed-accuracy tradeoff in difficult and ambiguous situations when foraging for prey. This observation may lead to a future investigation of the presence of emotional state in cephalopods.

The Relationship Between Attentional Bias, Anxiety Sensitivity, and Depression and Anxiety Symptoms: Evidence From the COVID-19 Pandemic in China

Background

The COVID-19 pandemic has led to observed increases in reported mental health issues, such as depression and anxiety symptoms. There is evidence attentional bias is associated with depression and anxiety, and it has been further suggested that anxiety sensitivity has a role in both the development and maintenance of depression and anxiety symptoms. Understanding these relationships may help inform preventative interventions for those at risk of mental health concerns. The present study explores the role of anxiety sensitivity, specifically physical and cognitive concerns, as a potential mediator of the relationship between attentional bias with depression and anxiety symptoms.

Method

Participants (n = 460) were recruited from the general population in China, and completed an online survey between February and March, 2020 which included the Attention to Positive and Negative Information Scale (APNI), Anxiety Sensitivity Index-3 (ASI-3) and Depression, Anxiety and Stress Scale (DASS-21). After exploring the correlations between the measures, mediation analysis was performed to explore the role of anxiety sensitivity (physical and cognitive subscales) in the relationship between attentional bias and depression and anxiety (as measured by the DASS-21).

Results

The results indicated that negative attention bias was significantly positively correlated with physical and cognitive concerns, physical and cognitive concerns were significantly positively correlated with depression and anxiety, and negative attention bias was significantly positively correlated with depression and anxiety (all ps < 0.001). Physical and cognitive anxiety sensitivity mediated the relationship between negative attention bias and both anxiety and depression symptoms.

Conclusion

Negative bias was associated with levels of anxiety and depression, and physical and cognitive anxiety sensitivity mediated associations between negative bias and anxiety and depression symptoms. The study provides theoretical support for intervention and guidance on individual mental health during the pandemic, and helps individuals increase their concern to negative emotions.

Temporal dynamics of affect in the brain: Evidence from human imaging and animal models

Emotions are time-varying internal states that promote survival in the face of dynamic environments and shifting homeostatic needs. Research in non-human organisms has recently afforded specific insights into the neural mechanisms that support the emergence, persistence, and decay of affective states. Concurrently, a separate affective neuroscience literature has begun to dissect the neural bases of affective dynamics in humans. However, the circuit-level mechanisms identified in animals lack a clear mapping to the human neuroscience literature. As a result, critical questions pertaining to the neural bases of affective dynamics in humans remain unanswered. To address these shortcomings, the present review integrates findings from humans and non-human organisms to highlight the neural mechanisms that govern the temporal features of emotional states. Using the theory of affective chronometry as an organizing framework, we describe the specific neural mechanisms and modulatory factors that arbitrate the rise-time, intensity, and duration of emotional states.

How cognitive and emotional empathy relate to rational thinking: empirical evidence and meta-analysis

Empathy is frequently described in opposition to rationality. Yet in two studies, we demonstrate that the relationship between rationality and empathy is nuanced and likely context dependent. Study 1 reports correlational data from two American samples and Study 2 presents a meta-analysis of existing literature (k = 22). We demonstrate that various types of cognitive empathy (perspective-taking, emotion recognition, and fantasy) are positively correlated with self-reported rationality, but unrelated to rational performance. In contrast, types of emotional empathy (empathic concern, personal distress, and emotion contagion) are generally negatively correlated with performance measures of rationality, but their relationships with self-reported rationality are divergent. Although these results do not settle the debate on empathy and rationality, they challenge the opposing domains hypothesis and provide tentative support for a dual-process model of empathy. Overall, these results indicate that the relationship between rationality and empathy differs depending upon how rationality and empathy are measured.

The Mediating Effect of Perceived Injustice and Pain Catastrophizing in the Relationship of Pain on Fatigue and Sleep in Breast Cancer Survivors: A Cross-Sectional Study

OBJECTIVE

Multidimensional aspects of pain have raised awareness about cognitive appraisals, such as perceived injustice (PI) and pain catastrophizing (PC). It has been demonstrated that they play an important role in patients' pain experience. However, the mediating effect of these appraisals has not been investigated in breast cancer survivors (BCS), nor have they been related to fatigue and sleep.

METHODS

Cross-sectional data from 128 BCS were analysed by structural path analysis with the aim to examine the mediating effect of PI and PC in the relationship of pain on fatigue and sleep.

RESULTS

The indirect mediating effects of PI on fatigue (CSI*PI = 0.21; P < 0.01 and VAS*PI = 1.19; P < 0.01) and sleep (CSI*PI = 0.31; P < 0.01 and VAS*PI = 1.74; P < 0.01) were found significant for both pain measures (Central Sensitization Inventory (CSI) and Visual Analogue Scale (VAS)). PC, on the other hand, only mediated the relationship between pain measured by VAS and fatigue (VAS*PC = 0.80; P = 0.03). Positive associations were found, indicating that higher pain levels are positively correlated with PI and PC, which go hand in hand with higher levels of fatigue and sleep problems.

CONCLUSION

PI is an important mediator in the relationship of pain on fatigue and sleep, while PC is a mediator on fatigue after cancer treatment. These findings highlight that both appraisals are understudied and open new perspectives regarding treatment strategies in BCS.

4D attention-based neural network for EEG emotion recognition

Electroencephalograph (EEG) emotion recognition is a significant task in the brain-computer interface field. Although many deep learning methods are proposed recently, it is still challenging to make full use of the information contained in different domains of EEG signals. In this paper, we present a novel method, called four-dimensional attention-based neural network (4D-aNN) for EEG emotion recognition. First, raw EEG signals are transformed into 4D spatial-spectral-temporal representations. Then, the proposed 4D-aNN adopts spectral and spatial attention mechanisms to adaptively assign the weights of different brain regions and frequency bands, and a convolutional neural network (CNN) is utilized to deal with the spectral and spatial information of the 4D representations. Moreover, a temporal attention mechanism is integrated into a bidirectional Long Short-Term Memory (LSTM) to explore temporal dependencies of the 4D representations. Our model achieves state-of-the-art performances on both DEAP, SEED and SEED-IV datasets under intra-subject splitting. The experimental results have shown the effectiveness of the attention mechanisms in different domains for EEG emotion recognition.

A functional magnetic resonance imaging study of frontal networks in obsessive-compulsive disorder during cognitive reappraisal

Background

Patients with obsessive-compulsive disorder (OCD) present difficulties in the cognitive regulation of emotions, possibly because of inefficient recruitment of distributed patterns of frontal cortex regions. The aim of the present study is to characterize the brain networks, and their dysfunctions, related to emotion regulation alterations observed during cognitive reappraisal in OCD.

Methods

Adult patients with OCD (n = 31) and healthy controls (HC; n = 30) were compared during performance of a functional magnetic resonance imaging cognitive reappraisal protocol. We used a free independent component analysis approach to analyze network-level alterations during emotional experience and regulation. Correlations with behavioral scores were also explored.

Results

Analyses were focused on six networks encompassing the frontal cortex. OCD patients showed decreased activation of the frontotemporal network in comparison with HC (F(1,58) = 7.81, p = 0.007) during cognitive reappraisal. A similar trend was observed in the left frontoparietal network.

Conclusions

The present study demonstrates that patients with OCD show decreased activation of specific networks implicating the frontal cortex during cognitive reappraisal. These outcomes should help to better characterize the psychological processes modulating fear, anxiety, and other core symptoms of patients with OCD, as well as the associated neurobiological alterations, from a system-level perspective.

Titanium oxide-based optoelectronic synapses with visual memory synergistically adjusted by internal emotions and ambient illumination

Brain-inspired neuromorphic computing has become one of the critical technologies to overcome the bottleneck of von Neumann architecture. It is a vital step to construct a brain-like neuromorphic computing system at the hardware level by utilizing artificial synaptic devices. Compared with electronic synaptic devices, optoelectronic synaptic devices have the advantages of low power consumption, low crosstalk, and high bandwidth. Artificial optoelectronic synapses, analogous to retinal structure, can directly respond to and process light signal information to mimic the neuromorphic visual system. As high-level nerve impulses, both generated and regulated, emotions affect the strength and persistence of memory. Ambient illumination can provide visual perception to distinguish the size, color, and other characteristics of objects as well as affect the nonvisual functions of individuals, such as emotional states, thereby affecting learning and memory function. Herein, an artificial optoelectronic synapse composed of ITO/TiO_2−x/p-Si was proposed. A variety of biologically dependent synaptic plasticity relating to learning and memory, including short-term synaptic plasticity, long-term synaptic plasticity, and learning-forgetting-relearning multifunctional advanced synaptic activity, was successfully simulated. A 3 × 3 artificial optoelectronic synapse array based on 9 devices was constructed to mimic the functions of visual learning and memory affected by internal emotion and ambient illumination. The proposed artificial optoelectronic synapse will exhibit great potential in visual and image information perception and memory.

A variety of biologically dependent synaptic plasticity activities were simulated on artificial optoelectronic synapse devices. An array was constructed to mimic the functions of visual learning and memory affected by internal emotion and ambient illumination.

MS-MDA: Multisource Marginal Distribution Adaptation for Cross-Subject and Cross-Session EEG Emotion Recognition

As an essential element for the diagnosis and rehabilitation of psychiatric disorders, the electroencephalogram (EEG) based emotion recognition has achieved significant progress due to its high precision and reliability. However, one obstacle to practicality lies in the variability between subjects and sessions. Although several studies have adopted domain adaptation (DA) approaches to tackle this problem, most of them treat multiple EEG data from different subjects and sessions together as a single source domain for transfer, which either fails to satisfy the assumption of domain adaptation that the source has a certain marginal distribution, or increases the difficulty of adaptation. We therefore propose the multi-source marginal distribution adaptation (MS-MDA) for EEG emotion recognition, which takes both domain-invariant and domain-specific features into consideration. First, we assume that different EEG data share the same low-level features, then we construct independent branches for multiple EEG data source domains to adopt one-to-one domain adaptation and extract domain-specific features. Finally, the inference is made by multiple branches. We evaluate our method on SEED and SEED-IV for recognizing three and four emotions, respectively. Experimental results show that the MS-MDA outperforms the comparison methods and state-of-the-art models in cross-session and cross-subject transfer scenarios in our settings. Codes at https://github.com/VoiceBeer/MS-MDA.

Identifying knowledge important to teach about the nervous system in the context of secondary biology and science education-A Delphi study

Teaching about the nervous system has become a challenging task in secondary biology and science education because of the fast development in the field of neuroscience. A major challenge is to determine what content to teach. Curricula goals are often too general to guide instruction, and information about the nervous system has become overwhelming and diverse with ubiquitous relevance in society. In addition, several misconceptions and myths are circulating in educational communities causing world-wide confusion as to what content is correct. To help teachers, textbook authors, and curricula developers in this challenging landscape of knowledge, the aim of the present study is to identify the expert view on what knowledge is important for understanding the nervous system in the context of secondary biology and science education. To accomplish this, we have conducted a thematic content analysis of textbooks followed by a Delphi study of 15 experts in diverse but relevant fields. The results demonstrate six curriculum themes including gross anatomy and function, cell types and functional units, the nerve signal, connections between neurons, when nerve signals travel through networks of neurons, and plasticity in the nervous system, as well as 26 content principles organized in a coherent curriculum progression from general content to more specific content. Whereas some of the principles clarify and elaborate on traditional school biology knowledge, others add new knowledge to the curriculum. Importantly, the new framework for teaching about the nervous system presented here, meets the needs of society, as expressed by recent international policy frameworks of OECD and WHO, and it addresses common misconceptions about the brain. The study suggests an update of the biology and science curriculum.

Housing and personality effects on judgement and attention biases in dairy cows

Affective states can be inferred from responses to ambiguous and threatening stimuli, using Judgement Bias Tasks (JBTs) and Attention Bias Tasks (ABTs). We investigated the separate and interactive effects of personality and housing conditions on dairy cattle affective states. We assessed personality in 48 heifers using Open-Field, Novel-Object and Runway tests. Personality effects on responses to the JBT and to the ABT were examined when heifers were housed under reference conditions. Heifers were subsequently housed under positive or negative conditions, and housing effects on animal responses in both tasks were investigated while controlling for personality. A Principal Component Analysis revealed three personality traits labelled Activity, Fearfulness and Sociability. Under reference conditions, personality influenced heifers' responses to the JBT and to the ABT, therefore questioning the tasks' generalizability across individuals. Against expectations, housing did not influence responses to the  JBT and heifers in the negative conditions looked at the threat later than heifers in the positive or reference conditions. More research is warranted to confirm the validity and the repeatability of the JBT and of the ABT as appropriate measures of affective states in dairy cows.