Emotion, cognition, and behavior

Modulatory effects of goal relevance on emotional attention reveal that fear has a distinct value

Threat-related stimuli can capture attention. However, it remains debated whether this capture is automatic or not. To address this question, we compared attentional biases to emotional faces using a dot-probe task (DPT) where emotion was never goal-relevant (Experiment 1) or made directly task-relevant by means of induction trials (Experiments 2-3). Moreover, the contingency between the DPT and induction trials was either partial (Experiment 2) or full (Experiment 3). Eye-tracking was used to ascertain that the emotional cue and the subsequent target were processed with peripheral vision. Experiments 1 and 2 both showed that negative faces captured attention, with faster target processing when it appeared on the same side as the preceding fearful face (i.e. fear-valid trials) compared to the opposite side where the neutral face was shown (i.e. fear-invalid trials), but also when it appeared on the side of the preceding neutral face (i.e. happy-invalid trials) compared to the happy face (i.e. happy-valid trials). Importantly, this preferential spatial orienting to negative emotion was not observed in Experiment 3, where the goal relevance of emotion was high. However, in that experiment, fearful faces produced a specific attentional bias during the DPT, which was mostly driven by the induction trials themselves.

A line attractor encoding a persistent internal state requires neuropeptide signaling

Internal states drive survival behaviors, but their neural implementation is poorly understood. Recently, we identified a line attractor in the ventromedial hypothalamus (VMH) that represents a state of aggressiveness. Line attractors can be implemented by recurrent connectivity or neuromodulatory signaling, but evidence for the latter is scant. Here, we demonstrate that neuropeptidergic signaling is necessary for line attractor dynamics in this system by using cell-type-specific CRISPR-Cas9-based gene editing combined with single-cell calcium imaging. Co-disruption of receptors for oxytocin and vasopressin in adult VMH Esr1+ neurons that control aggression diminished attack, reduced persistent neural activity, and eliminated line attractor dynamics while only slightly reducing overall neural activity and sex- or behavior-specific tuning. These data identify a requisite role for neuropeptidergic signaling in implementing a behaviorally relevant line attractor in mammals. Our approach should facilitate mechanistic studies in neuroscience that bridge different levels of biological function and abstraction.

Linked patterns of symptoms and cognitive covariation with functional brain controllability in major depressive disorder

BACKGROUND

Controllability analysis is an approach developed for evaluating the ability of a brain region to modulate function in other regions, which has been found to be altered in major depressive disorder (MDD). Both depressive symptoms and cognitive impairments are prominent features of MDD, but the case-control differences of controllability between MDD and controls can not fully interpret the contribution of both clinical symptoms and cognition to brain controllability and linked patterns among them in MDD.

METHODS

Sparse canonical correlation analysis was used to investigate the associations between resting-state functional brain controllability at the network level and clinical symptoms and cognition in 99 first-episode medication-naïve patients with MDD.

FINDINGS

Average controllability was significantly correlated with clinical features. The average controllability of the dorsal attention network (DAN) and visual network had the highest correlations with clinical variables. Among clinical variables, depressed mood, suicidal ideation and behaviour, impaired work and activities, and gastrointestinal symptoms were significantly negatively associated with average controllability, and reduced cognitive flexibility was associated with reduced average controllability.

INTERPRETATION

These findings highlight the importance of brain regions in modulating activity across brain networks in MDD, given their associations with symptoms and cognitive impairments observed in our study. Disrupted control of brain reconfiguration of DAN and visual network during their state transitions may represent a core brain mechanism for the behavioural impairments observed in MDD.

FUNDING

National Natural Science Foundation of China (82001795 and 82027808), National Key R&D Program (2022YFC2009900), and Sichuan Science and Technology Program (2024NSFSC0653).

Asynchronous Involvement of VLPFC and DLPFC During Negative Emotion Processing: An Online Transcranial Magnetic Stimulation Study

The ventrolateral prefrontal cortex (VLPFC) and dorsolateral prefrontal cortex (DLPFC) have been found to play important roles in negative emotion processing. However, the specific time window of their involvement remains unknown. This study addressed this issue in three experiments using single-pulse transcranial magnetic stimulation (TMS). We found that TMS applied over the VLPFC at 400 ms after negative emotional exposure significantly enhanced negative feelings compared to the vertex condition. Furthermore, TMS applied over the DLPFC at both 0 ms and 600 ms after negative emotional exposure also resulted in deteriorated negative feelings. These findings provide potential evidence for the VLPFC-dependent semantic processing (∼400 ms) and the DLPFC-dependent attentional and cognitive control (∼0/600 ms) in negative emotion processing. The asynchronous involvement of these frontal cortices not only deepens our understanding of the neural mechanisms underlying negative emotion processing but also provides valuable temporal parameters for neurostimulation therapy targeting patients with mood disorders.

Comparing an In-Person and Online Continuing Education Intervention to Improve Professional Decision-Making: A Mixed Methods Study

Purpose: This paper compares two iterations (in-person and online) of a multi-stage continuing education program for improving high-risk decision-making among mental health workers. Methods: The mixed-methods study analyzed the following: (1) physiological and psychological arousal during simulated patient interviews; (2) physiological and psychological arousal recorded during real-time decision-making over four months; and (3) thoughts on the process and outcomes of the intervention raised in reflective interviews. Findings: Quantitatively, there were no statistical differences in stress measures between in-person and online simulated interviews or decision-making logs, suggesting they were effective in eliciting reactions commonly found in challenging clinical situations. Qualitatively, participants in both iterations indicated that the intervention caused them to reflect on practice, consider a wider range of factors related to the decisions, and enact approaches to improve decision-making. Conclusions: A carefully constructed online continuing education experience can result in outcomes for experienced social workers that are equivalent to an in-person iteration.

Positive and negative facial valence perception are modulated differently by eccentricity in the parafovea: Replication from KDEF to NimStim

While perceiving the emotional state of others may be crucial for our behavior even when this information is present outside of central vision, emotion perception studies typically focus on central visual field. We have recently investigated emotional valence (pleasantness) perception across the parafovea (≤ 4°) and found that for briefly presented (200 ms) emotional face images (from the established KDEF image-set), positive (happy) valence was the least affected by eccentricity (distance from the central visual field) and negative (fearful) valence the most. Furthermore, we found that performance at 2° predicted performance at 4°. Here we tested (n = 37) whether these effects replicate with face stimuli of different identities from a different well-established image-set (NimStim). All our prior findings replicated and eccentricity-based modulation magnitude was smaller with NimStim (~ 16.6% accuracy reduction at 4°) than with KDEF stimuli (~ 27.3% reduction). Our current investigations support our earlier findings that for briefly presented parafoveal stimuli, positive and negative valence perception are differently affected by eccentricity and may be dissociated. Furthermore, our results highlight the importance of investigating emotions beyond central vision and demonstrate commonalities and differences across different image sets in the parafovea, emphasizing the contribution of replication studies to substantiate our knowledge about perceptual mechanisms.

Clouded judgments? The role of virtual weather in word valence evaluations

Exploring the dynamic interface of environmental psychology and psycholinguistics, this investigation delves into how simulated weather conditions - sunny versus rainy - affect emotional perceptions of linguistic stimuli within a Virtual Reality (VR) framework. Participants assessed words' emotional valence being exposed to these distinct environmental simulations. Contrary to expectations, we found that while rainy conditions modestly prolonged response times, they did not significantly alter the emotional valence attributed to words. Our study shows that weather can affect emotional cognition, but intrinsic emotional word properties are resilient to environmental influences. This highlights the complex interplay between environmental factors and linguistic processing and reaffirms the importance of environmental contexts in cognitive and emotional evaluations, especially in the face of climate change. By integrating VR technology with environmental psychology and linguistics, our research offers novel insights into the subtle yet significant ways in which virtual and real-world environments converge to shape human emotional and cognitive responses.

Electrophysiological Correlates of Amplified Emotion-Related Cognitive Processing Evoked by Self-Administered Disgust Images

In the processing of emotions, the brain prepares and reacts in distinctive manners depending upon the negative or positive nuance of the emotion elicitors. Previous investigations showed that negative elicitors generally evoke more intense neural activities than positive and neutral ones, as reflected in the augmented amplitude of all sub-components of the event-related potentials (ERP) late posterior positivity (LPP) complex, while less is known about the emotion of disgust. The present study aimed to examine whether the LPP complex during the processing of disgust stimuli showed greater amplitude than other emotion elicitors with negative or positive valences, thus confirming it as a neural marker of disgust-related negativity bias at earlier or later stages. Thus, in the present study, we leveraged the ERP technique during the execution of an affective self-administered visual stimuli task to disentangle the neural contributions associated with images of positive, negative, disgust, or neutral emotions. Crucially, we showed that handling with disgust elicitors prompted the greatest neural activity and the highest delay during self-administration. Overall, we demonstrated progressive neural activities associated with the unpleasantness of the emotion elicitors and peculiar processing for disgust compared with all other emotions.

Attentional Differences Between Males and Females for Emojis During the Emotional Stroop Task

Introduction Cognitive load can be intensified by emotional components such as emotive words or facial expressions. Sex differences influence both emotional and cognitive functions for emotional facial expressions. Emojis, in contrast to human faces, serve as digital cues conveying emotional nuances in communication. The present study aimed to compare attentional differences prompted by emojis. Methods This study aimed to compare attentional differences in males and females elicited by emojis in 100 healthy adults (50 males and 50 females) within the age group of 18 to 40 years (mean ± SD: 27.87 ± 5.37 years) while performing the emotional Stroop task (EST). The EST comprised emojis depicting four emotions (happy, fear, sad, and angry) and emotionally charged words conveying similar emotions. An independent sample t-test was used to compare the reaction times among males and females. Results Results showed males had significantly longer reaction times than females across all task conditions. Both genders exhibited significant differences in reaction times across task conditions, except inhibition. Overall, reaction times increased notably from neutral to incongruent conditions for both genders. This suggests that response times increased significantly from neutral to incongruent conditions. Conclusion Emojis introduced in the EST revealed gender-related differences in attentional processing. This study showed the greater proficiency of females in emotional processing during EST compared to males. These findings contribute to our understanding of how gender is associated with cognitive responses to emotional stimuli in digital communication contexts.

Understanding the structure of autobiographical memories: A study of trauma memories from the 1994 Rwandan genocide

How do we remember traumatic events, and are these memories different in individuals who experience post-traumatic stress? Some evidence suggests that traumatic events are mnemonically enhanced, or include more episodic detail, relative to other types of memories. Simultaneously, individuals with post-traumatic stress disorder (PTSD) have more non-episodic details in all of their memories, a pattern hypothesized to result from impairment in executive function. Here, we explore these questions in a unique population that experienced severely traumatic events more than 20 years ago - individuals who lived through the 1994 genocide in Rwanda. Participants recalled events from the genocide, negative events unrelated to the genocide, neutral events, and positive events. We used the Autobiographical Interview method to label memory details as episodic or non-episodic. We found that memories from the genocide showed robust mnemonic enhancement, with more episodic than non-episodic details, and contained more details overall than any other memory type. This pattern was not impacted by post-traumatic stress. Overall, this study provides evidence that traumatic events create vivid long-lasting episodic memories, in this case even more than 20 years later.

Exploring the relationship among soccer-related knowledge, attitude, practice, and self-health in Chinese campus soccer education

China has promoted campus soccer for over a decade due to its potential health benefits. The study aimed to explore soccer knowledge (SK), soccer attitude (SA), soccer practice (SP), and health status among Chinese freshmen and sophomore undergraduates who had received campus soccer education. Of the 7419 participants, 1,069 were valid and included in the analysis. Structural equation modeling (SEM) results indicated SK is positively associated with SA (p < 0.001), but negatively with SP (p < 0.01). SA was positively linked to SP (p < 0.001). SK indirectly affected SP through SA (Z = 13.677). Random forest-tree-structured Parzen estimators (RF-TPE) with SHAP indicated SP holds primary importance with a strong negative impact on health. Additionally, differences in rankings for SK, SA, and SP were observed among gender and urban-rural groups. These results reveal current campus soccer education is suboptimal to health promotion.

MSLTE: multiple self-supervised learning tasks for enhancing EEG emotion recognition

Objective. The instability of the EEG acquisition devices may lead to information loss in the channels or frequency bands of the collected EEG. This phenomenon may be ignored in available models, which leads to the overfitting and low generalization of the model.Approach. Multiple self-supervised learning tasks are introduced in the proposed model to enhance the generalization of EEG emotion recognition and reduce the overfitting problem to some extent. Firstly, channel masking and frequency masking are introduced to simulate the information loss in certain channels and frequency bands resulting from the instability of EEG, and two self-supervised learning-based feature reconstruction tasks combining masked graph autoencoders (GAE) are constructed to enhance the generalization of the shared encoder. Secondly, to take full advantage of the complementary information contained in these two self-supervised learning tasks to ensure the reliability of feature reconstruction, a weight sharing (WS) mechanism is introduced between the two graph decoders. Thirdly, an adaptive weight multi-task loss (AWML) strategy based on homoscedastic uncertainty is adopted to combine the supervised learning loss and the two self-supervised learning losses to enhance the performance further.Main results. Experimental results on SEED, SEED-V, and DEAP datasets demonstrate that: (i) Generally, the proposed model achieves higher averaged emotion classification accuracy than various baselines included in both subject-dependent and subject-independent scenarios. (ii) Each key module contributes to the performance enhancement of the proposed model. (iii) It achieves higher training efficiency, and significantly lower model size and computational complexity than the state-of-the-art (SOTA) multi-task-based model. (iv) The performances of the proposed model are less influenced by the key parameters.Significance. The introduction of the self-supervised learning task helps to enhance the generalization of the EEG emotion recognition model and eliminate overfitting to some extent, which can be modified to be applied in other EEG-based classification tasks.

Cross-modal credibility modelling for EEG-based multimodal emotion recognition

Objective.The study of emotion recognition through electroencephalography (EEG) has garnered significant attention recently. Integrating EEG with other peripheral physiological signals may greatly enhance performance in emotion recognition. Nonetheless, existing approaches still suffer from two predominant challenges: modality heterogeneity, stemming from the diverse mechanisms across modalities, and fusion credibility, which arises when one or multiple modalities fail to provide highly credible signals.Approach.In this paper, we introduce a novel multimodal physiological signal fusion model that incorporates both intra-inter modality reconstruction and sequential pattern consistency, thereby ensuring a computable and credible EEG-based multimodal emotion recognition. For the modality heterogeneity issue, we first implement a local self-attention transformer to obtain intra-modal features for each respective modality. Subsequently, we devise a pairwise cross-attention transformer to reveal the inter-modal correlations among different modalities, thereby rendering different modalities compatible and diminishing the heterogeneity concern. For the fusion credibility issue, we introduce the concept of sequential pattern consistency to measure whether different modalities evolve in a consistent way. Specifically, we propose to measure the varying trends of different modalities, and compute the inter-modality consistency scores to ascertain fusion credibility.Main results.We conduct extensive experiments on two benchmarked datasets (DEAP and MAHNOB-HCI) with the subject-dependent paradigm. For the DEAP dataset, our method improves the accuracy by 4.58%, and the F1 score by 0.63%, compared to the state-of-the-art baseline. Similarly, for the MAHNOB-HCI dataset, our method improves the accuracy by 3.97%, and the F1 score by 4.21%. In addition, we gain much insight into the proposed framework through significance test, ablation experiments, confusion matrices and hyperparameter analysis. Consequently, we demonstrate the effectiveness of the proposed credibility modelling through statistical analysis and carefully designed experiments.Significance.All experimental results demonstrate the effectiveness of our proposed architecture and indicate that credibility modelling is essential for multimodal emotion recognition.

Assessing the equivalency of face-to-face and online simulated patient interviews in an educational intervention

BACKGROUND

In adapting to COVID-19, many health professional training programs moved abruptly from in-person to online simulated patient interviews for teaching and evaluation without the benefit of evidence regarding the efficacy of this mode of delivery. This paper reports on a multi-methods research project comparing in-person and online simulated patient interviews conducted by allied health professionals as part of an educational intervention offered at a large university teaching hospital.

METHODS

Twenty-three participants conducted two 15-min interviews with simulated patients using previously validated scenarios of patients presenting with suicide risk. In order to assess the equivalency of the two modalities, physiological and psychological stress were measured using heart rate variability parameters and the State-Trait Anxiety Inventory respectively, and then were compared across cohorts using t-tests. Reflective interviews elicited qualitative impressions of the simulations that were subject to thematic qualitative analysis.

RESULTS

There were no statistical differences in measures of psychological stress or physiological arousal of participant health care professionals who engaged with in-person versus online simulated interviews, suggesting they were equally effective in eliciting reactions commonly found in challenging clinical situations. In reflective interviews, participants commented on the realism of both modalities of simulated patient encounters and that simulated interviews provoked emotional and physiological responses consistent with actual patient encounters.

CONCLUSIONS

These findings provide developing evidence that carefully designed online clinical simulations can be a useful tool for the education and assessment of healthcare professionals.

Instantaneous estimation of momentary affective responses using neurophysiological signals and a spatiotemporal emotional intensity regression network

Previous studies in affective computing often use a fixed emotional label to train an emotion classifier with electroencephalography (EEG) from individuals experiencing an affective stimulus. However, EEGs encode emotional dynamics that include varying intensities within a given emotional category. To investigate these variations in emotional intensity, we propose a framework that obtains momentary affective labels for fine-grained segments of EEGs with human feedback. We then model these labeled segments using a novel spatiotemporal emotional intensity regression network (STEIR-Net). It integrates temporal EEG patterns from nine predefined cortical regions to provide a continuous estimation of emotional intensity. We demonstrate that the STEIR-Net outperforms classical regression models by reducing the root mean square error (RMSE) by an average of 4∼9 % and 2∼4 % for the SEED and SEED-IV databases, respectively. We find that the frontal and temporal cortical regions contribute significantly to the affective intensity's variation. Higher absolute values of the Spearman correlation coefficient between the model estimation and momentary affective labels under happiness (0.2114) and fear (0.2072) compared to neutral (0.1694) and sad (0.1895) emotions were observed. Besides, increasing the input length of the EEG segments from 4 to 20 s further reduces the RMSE from 1.3548 to 1.3188.

Prenatal and postnatal influences on behavioral development in a mouse model of preconceptional stress

Depression during pregnancy is detrimental for the wellbeing of the expectant mother and can exert long-term consequences on the offspring's development and mental health. In this context, both the gestational environment and the postpartum milieu may be negatively affected by the depressive pathology. It is, however, challenging to assess whether the contributions of prenatal and postnatal depression exposure are distinct, interactive, or cumulative, as it is unclear whether antenatal effects are due to direct effects on fetal development or because antenatal symptoms continue postnatally. Preclinical models have sought to answer this question by implementing stressors that induce a depressive-like state in the dams during pregnancy and studying the effects on the offspring. The aim of our present study was to disentangle the contribution of direct stress in utero from possible changes in maternal behavior in a novel model of preconceptional stress based on social isolation rearing (SIR). Using a cross-fostering paradigm in this model, we show that while SIR leads to subtle changes in maternal behavior, the behavioral changes observed in the offspring are driven by a complex interaction between sex, and prenatal and postnatal maternal factors. Indeed, male offspring are more sensitive to the prenatal environment, as demonstrated by behavioral and transcriptional changes driven by their birth mother, while females are likely affected by more complex interactions between the pre and the postpartum milieu, as suggested by the important impact of their surrogate foster mother. Taken together, our findings suggest that male and female offspring have different time-windows and behavioral domains of susceptibility to maternal preconceptional stress, and thus underscore the importance of including both sexes when investigating the mechanisms that mediate the negative consequences of exposure to such stressor.

An attention-based approach for assessing the effectiveness of emotion-evoking in immersive environment

Visual stimuli within an immersive virtual environment impact human perception and behavior in notably different ways compared to the real world. Previous studies have presented evidence indicating that individuals in various emotional states exhibit an unconscious attentional bias toward either positive or negative stimuli. However, whether these findings can be replicated within an immersive virtual environment remains uncertain. In this study, we devised an attention-based experiment to explore whether the correlation between participants' emotional states and the valence of visual stimuli influences their attentional bias. Participants (n=28) viewed 360-degree videos with varying valence levels (positive and negative) to evoke emotions. Subsequently, we utilized standard emotional human faces as stimuli to assess how the consistency in video valence and emotional faces affects reaction time (RT) in Go tasks and error rates in No-go tasks. We employed the Ex-Gaussian approach to analyze the RT data. The parameters-mu (μ), sigma (σ), and tau (τ)-were computed to denote response speed and attentional lapses, respectively. Our findings revealed a significant increase in tau (τ) when the valence of the video and emotional faces aligned. This suggests that the Go/No-go paradigm is effective in evaluating the impact of emotion-evoking stimuli within an immersive environment.

MISNet: multi-source information-shared EEG emotion recognition network with two-stream structure

Introduction

When constructing machine learning and deep neural networks, the domain shift problem on different subjects complicates the subject independent electroencephalography (EEG) emotion recognition. Most of the existing domain adaptation methods either treat all source domains as equivalent or train source-specific learners directly, misleading the network to acquire unreasonable transfer knowledge and thus resulting in negative transfer.

Methods

This paper incorporates the individual difference and group commonality of distinct domains and proposes a multi-source information-shared network (MISNet) to enhance the performance of subject independent EEG emotion recognition models. The network stability is enhanced by employing a two-stream training structure with loop iteration strategy to alleviate outlier sources confusing the model. Additionally, we design two auxiliary loss functions for aligning the marginal distributions of domain-specific and domain shared features, and then optimize the convergence process by constraining gradient penalty on these auxiliary loss functions. Furthermore, the pre-training strategy is also proposed to ensure that the initial mapping of shared encoder contains sufficient emotional information.

Results

We evaluate the proposed MISNet to ascertain the impact of several hyper-parameters on the domain adaptation capability of network. The ablation experiments are conducted on two publically accessible datasets SEED and SEED-IV to assess the effectiveness of each loss function.

Discussion

The experimental results demonstrate that by disentangling private and shared emotional characteristics from differential entropy features of EEG signals, the proposed MISNet can gain robust subject independent performance and strong domain adaptability.

A physicochemical-sensing electronic skin for stress response monitoring

Approaches to quantify stress responses typically rely on subjective surveys and questionnaires. Wearable sensors can potentially be used to continuously monitor stress-relevant biomarkers. However, the biological stress response is spread across the nervous, endocrine, and immune systems, and the capabilities of current sensors are not sufficient for condition-specific stress response evaluation. Here we report an electronic skin for stress response assessment that non-invasively monitors three vital signs (pulse waveform, galvanic skin response and skin temperature) and six molecular biomarkers in human sweat (glucose, lactate, uric acid, sodium ions, potassium ions and ammonium). We develop a general approach to prepare electrochemical sensors that relies on analogous composite materials for stabilizing and conserving sensor interfaces. The resulting sensors offer long-term sweat biomarker analysis of over 100 hours with high stability. We show that the electronic skin can provide continuous multimodal physicochemical monitoring over a 24-hour period and during different daily activities. With the help of a machine learning pipeline, we also show that the platform can differentiate three stressors with an accuracy of 98.0%, and quantify psychological stress responses with a confidence level of 98.7%.

Instance-representation transfer method based on joint distribution and deep adaptation for EEG emotion recognition

Electroencephalogram (EEG) emotion recognition technology is essential for improving human-computer interaction. However, the practical application of emotion recognition technology is limited due to the variety of subjects and sessions. Transfer learning has been applied to address this issue and has received extensive research and application. Studies mainly concentrate on either instance transfer or representation transfer methods. This paper proposes an emotion recognition method called Joint Distributed Instances Represent Transfer (JD-IRT), which includes two core components: Joint Distribution Deep Adaptation (JDDA) and Instance-Representation Transfer (I-RT). Specifically, JDDA is different from common representation transfer methods in transfer learning. It bridges the discrepancies of marginal and conditional distributions simultaneously and combines multiple adaptive layers and kernels for deep domain adaptation. On the other hand, I-RT utilizes instance transfer to select source domain data for better representation transfer. We performed experiments and compared them with other representative methods in the SEED, SEED-IV, and SEED-V datasets. In cross-subject experiments, our approach achieved an average accuracy of 83.21% in SEED, 52.12% in SEED-IV, and 60.17% in SEED-V. Similarly, in cross-session experiments, the accuracy was 91.29% in SEED, 59.02% in SEED-IV, and 65.91% in SEED-V. These results demonstrate the improvement in the accuracy of EEG emotion recognition using the proposed approach.

Emotional arousal lingers in time to bind discrete episodes in memory

Temporal stability and change in neutral contexts can transform continuous experiences into distinct and memorable events. However, less is known about how shifting emotional states influence these memory processes, despite ample evidence that emotion impacts non-temporal aspects of memory. Here, we examined if emotional stimuli influence temporal memory for recent event sequences. Participants encoded lists of neutral images while listening to auditory tones. At regular intervals within each list, participants heard emotional positive, negative, or neutral sounds, which served as "emotional event boundaries" that divided each sequence into discrete events. Temporal order memory was tested for neutral item pairs that either spanned an emotional sound or were encountered within the same auditory event. Encountering a highly arousing event boundary led to faster response times for items encoded within the next event. Critically, we found that highly arousing sounds had different effects on binding ongoing versus ensuing sequential representations in memory. Specifically, highly arousing sounds were significantly more likely to enhance temporal order memory for ensuing information compared to information that spanned those boundaries, especially for boundaries with negative valence. These findings suggest that within aversive emotional contexts, fluctuations in arousal help shape the temporal organisation of events in memory.

Dynamic Neural Patterns of Human Emotions in Virtual Reality: Insights from EEG Microstate Analysis

Emotions play a crucial role in human life and affect mental health. Understanding the neural patterns associated with emotions is essential. Previous studies carried out some exploration of the neural features of emotions, but most have designed experiments in two-dimensional (2D) environments, which differs from real-life scenarios. To create a more real environment, this study investigated emotion-related brain activity using electroencephalography (EEG) microstate analysis in a virtual reality (VR) environment. We recruited 42 healthy volunteers to participate in our study. We explored the dynamic features of different emotions, and four characteristic microstates were analyzed. In the alpha band, microstate A exhibited a higher occurrence in both negative and positive emotions than in neutral emotions. Microstate C exhibited a prolonged duration of negative emotions compared to positive emotions, and a higher occurrence was observed in both microstates C and D during positive emotions. Notably, a unique transition pair was observed between microstates B and C during positive emotions, whereas a unique transition pair was observed between microstates A and D during negative emotions. This study emphasizes the potential of integrating virtual reality (VR) and EEG to facilitate experimental design. Furthermore, this study enhances our comprehension of neural activities during various emotional states.

Astrocytes in Pain Perception: A Systems Neuroscience Approach

Astrocytes play an active role in the function of the brain integrating neuronal activity and regulating back neuronal dynamic. They have recently emerged as active contributors of brain's emergent properties such as perceptions. Here, we analyzed the role of astrocytes in pain perception from the lens of systems neuroscience, and we do this by analyzing how astrocytes encode nociceptive information within brain processing areas and how they are key regulators of the internal state that determines pain perception. Specifically, we discuss the dynamic interactions between astrocytes and neuromodulators, such as noradrenaline, highlighting their role in shaping the level of activation of the neuronal ensemble, thereby influencing the experience of pain. Also, we will discuss the possible implications of an "Astro-NeuroMatrix" in the integration of pain across sensory, affective, and cognitive dimensions of pain perception.

Diagnosis Framework for Probable Alzheimer's Disease and Mild Cognitive Impairment Based on Multi-Dimensional Emotion Features

BACKGROUND

Emotion and cognition are intercorrelated. Impaired emotion is common in populations with Alzheimer's disease (AD) and mild cognitive impairment (MCI), showing promises as an early detection approach.

OBJECTIVE

We aim to develop a novel automatic classification tool based on emotion features and machine learning.

METHODS

Older adults aged 60 years or over were recruited among residents in the long-term care facilities and the community. Participants included healthy control participants with normal cognition (HC, n = 26), patients with MCI (n = 23), and patients with probable AD (n = 30). Participants watched emotional film clips while multi-dimensional emotion data were collected, including mental features of Self-Assessment Manikin (SAM), physiological features of electrodermal activity (EDA), and facial expressions. Emotional features of EDA and facial expression were abstracted by using continuous decomposition analysis and EomNet, respectively. Bidirectional long short-term memory (Bi-LSTM) was used to train classification model. Hybrid fusion was used, including early feature fusion and late decision fusion. Data from 79 participants were utilized into deep machine learning analysis and hybrid fusion method.

RESULTS

By combining multiple emotion features, the model's performance of AUC value was highest in classification between HC and probable AD (AUC = 0.92), intermediate between MCI and probable AD (AUC = 0.88), and lowest between HC and MCI (AUC = 0.82).

CONCLUSIONS

Our method demonstrated an excellent predictive power to differentiate HC/MCI/AD by fusion of multiple emotion features. The proposed model provides a cost-effective and automated method that can assist in detecting probable AD and MCI from normal aging.

Individual and context-evoked antecedents of exploration-exploitation performance

A central issue within the Carnegie approach is the exploration-exploitation tension that lies behind organizational adaptation. After decades of research, there is still little understanding of how the combination of individual and context-evoked differences affects exploration-exploitation performance. To address that issue, we build on recent psychological and neuroscientific studies to develop and test an integrative model. The model considers two individual antecedents (personality and cognitive flexibility) and three context-evoked antecedents that take place along different time horizons (recent stress, present emotional states, and present task motivation). We rely on a lab-in-the-field study of 282 leaders within the Swiss Armed Forces-an organization that exhibits the exploration-exploitation tension in an accentuated form. Using structural equation modeling, we conduct a multiple-mediation path analysis aimed at testing complex interactions between multiple variables. Our findings highlight the need to take an integrative approach; cognitive flexibility mediates the positive effect of the personality trait of emotional stability on exploration-exploitation performance, however, both cognitive flexibility and emotional stability play unique, underlying roles in explaining how organizational leaders interpret the context. Emotional stability decreases the negative effect of recent stress on a leader's cognitive flexibility. Cognitive flexibility, in turn, mediates the effect of the present positive affective signals of task motivation on exploration-exploitation performance. These findings shed new light on our understanding of how adaptive leaders leverage positive and negative context-evoked antecedents that, in turn, affect cognitive flexibility and exploration-exploitation.

The role of emotion in eating behavior and decisions

The present paper aims to provide the latest perspectives and future directions on the association between emotions and eating behavior. We discussed individual differences in the impact of negative emotions on eating, emotional eating as disinhibited eating decisions with heightened reward values of and sensitivity to palatable foods in response to negative emotions and social isolation, in addition to emotional eating as maladaptive coping strategies under negative emotion and stress, hedonic (pleasure-oriented) eating decisions mediated by the brain reward system, and self-controlled (health-oriented) eating decisions mediated by the brain control system. Perspectives on future directions were addressed, including the development of early eating phenotypes in infancy, shared neural mechanisms mediated by the ventromedial prefrontal cortex and the dorsolateral prefrontal cortex in emotion and eating decision regulation, possible roles of interoception incorporating hunger and satiety signals, gut microbiome, the insula and the orbitofrontal cortex, and emotional processing capacities in hedonic eating and weight gain.

Rapid withdrawal from a threatening animal is movement-specific and mediated by reflex-like neural processing

Responses to potentially dangerous stimuli are among the most basic animal behaviors. While research has shown that threats automatically capture the attention of human participants, research has failed to demonstrate automatic behavioral responses to threats in humans. Using a novel naturalistic paradigm, we show that two species of animals humans often report fearing trigger rapid withdrawal responses: participants withdrew their arm from photos of snakes and spiders faster, and with higher acceleration when compared to bird and butterfly stimuli. The behavior was specific to withdrawal as approach movements or button-press/release tasks failed to detect a similar difference. Moreover, between-participant differences in how aversive they found the stimuli predicted the participant's withdrawal speed, indicating that the paradigm was also sensitive to trait-level differences between individuals. Using electroencephalography (EEG), we show that the fast withdrawal was mediated by two attentional processes. First, fast withdrawal responses were associated with early amplification of sensory signals (40-110 ms after stimulus). Second, a later correlate of feature-based attention (early posterior negativity, EPN, 200-240 ms after stimulus) revealed the opposite pattern: Stronger EPN was associated with slower behavioral responses, suggesting that the deployment of attention towards the threatening stimulus features, or failure to "disengage" attention from the stimulus, was detrimental for withdrawal speed. Altogether, the results suggest that rapid behavioral withdrawal from a threatening animal is mediated by reflex-like attentional processing, and later, conscious attention to stimulus features may hinder escaping the treat.

A normative model of brain responses to social scenarios reflects the maturity of children and adolescents' social-emotional abilities

The rapid brain maturation in childhood and adolescence accompanies the development of socio-emotional functioning. However, it is unclear how the maturation of the neural activity drives the development of socio-emotional functioning and individual differences. This study aimed to reflect the age dependence of inter-individual differences in brain responses to socio-emotional scenarios and to develop naturalistic imaging indicators to assess the maturity of socio-emotional ability at the individual level. Using three independent naturalistic imaging datasets containing healthy participants (n = 111, 21 and 122), we found and validated that age-modulated inter-individual concordance of brain responses to socio-emotional movies in specific brain regions. The similarity of an individual's brain response to the average response of older participants was defined as response typicality, which predicted an individual's emotion regulation strategies in adolescence and theory of mind (ToM) in childhood. Its predictive power was not superseded by age, sex, cognitive performance or executive function. We further showed that the movie's valence and arousal ratings grounded the response typicality. The findings highlight that forming typical brain response patterns may be a neural phenotype underlying the maturation of socio-emotional ability. The proposed response typicality represents a neuroimaging approach to measure individuals' maturity of cognitive reappraisal and ToM.

Possibilistic distribution distance metric: a robust domain adaptation learning method

The affective Brain-Computer Interface (aBCI) systems, which achieve predictions for individual subjects through training on multiple subjects, often cannot achieve satisfactory results due to the differences in Electroencephalogram (EEG) patterns between subjects. One tried to use Subject-specific classifiers, but there was a lack of sufficient labeled data. To solve this problem, Domain Adaptation (DA) has recently received widespread attention in the field of EEG-based emotion recognition. Domain adaptation (DA) learning aims to solve the problem of inconsistent distributions between training and test datasets and has received extensive attention. Most existing methods use Maximum Mean Discrepancy (MMD) or its variants to minimize the problem of domain distribution inconsistency. However, noisy data in the domain can lead to significant drift in domain means, which can affect the adaptability performance of learning methods based on MMD and its variants to some extent. Therefore, we propose a robust domain adaptation learning method with possibilistic distribution distance measure. Firstly, the traditional MMD criterion is transformed into a novel possibilistic clustering model to weaken the influence of noisy data, thereby constructing a robust possibilistic distribution distance metric (P-DDM) criterion. Then the robust effectiveness of domain distribution alignment is further improved by a fuzzy entropy regularization term. The proposed P-DDM is in theory proved which be an upper bound of the traditional distribution distance measure method MMD criterion under certain conditions. Therefore, minimizing P-DDM can effectively optimize the MMD objective. Secondly, based on the P-DDM criterion, a robust domain adaptation classifier based on P-DDM (C-PDDM) is proposed, which adopts the Laplacian matrix to preserve the geometric consistency of instances in the source domain and target domain for improving the label propagation performance. At the same time, by maximizing the use of source domain discriminative information to minimize domain discrimination error, the generalization performance of the learning model is further improved. Finally, a large number of experiments and analyses on multiple EEG datasets (i.e., SEED and SEED-IV) show that the proposed method has superior or comparable robustness performance (i.e., has increased by around 10%) in most cases.

Local domain generalization with low-rank constraint for EEG-based emotion recognition

As an important branch in the field of affective computing, emotion recognition based on electroencephalography (EEG) faces a long-standing challenge due to individual diversities. To conquer this challenge, domain adaptation (DA) or domain generalization (i.e., DA without target domain in the training stage) techniques have been introduced into EEG-based emotion recognition to eliminate the distribution discrepancy between different subjects. The preceding DA or domain generalization (DG) methods mainly focus on aligning the global distribution shift between source and target domains, yet without considering the correlations between the subdomains within the source domain and the target domain of interest. Since the ignorance of the fine-grained distribution information in the source may still bind the DG expectation on EEG datasets with multimodal structures, multiple patches (or subdomains) should be reconstructed from the source domain, on which multi-classifiers could be learned collaboratively. It is expected that accurately aligning relevant subdomains by excavating multiple distribution patterns within the source domain could further boost the learning performance of DG/DA. Therefore, we propose in this work a novel DG method for EEG-based emotion recognition, i.e., Local Domain Generalization with low-rank constraint (LDG). Specifically, the source domain is firstly partitioned into multiple local domains, each of which contains only one positive sample and its positive neighbors and k2 negative neighbors. Multiple subject-invariant classifiers on different subdomains are then co-learned in a unified framework by minimizing local regression loss with low-rank regularization for considering the shared knowledge among local domains. In the inference stage, the learned local classifiers are discriminatively selected according to their importance of adaptation. Extensive experiments are conducted on two benchmark databases (DEAP and SEED) under two cross-validation evaluation protocols, i.e., cross-subject within-dataset and cross-dataset within-session. The experimental results under the 5-fold cross-validation demonstrate the superiority of the proposed method compared with several state-of-the-art methods.

Neuropeptide Signaling is Required to Implement a Line Attractor Encoding a Persistent Internal Behavioral State

Internal states drive survival behaviors, but their neural implementation is not well understood. Recently we identified a line attractor in the ventromedial hypothalamus (VMH) that represents an internal state of aggressiveness. Line attractors can be implemented by recurrent connectivity and/or neuromodulatory signaling, but evidence for the latter is scant. Here we show that neuropeptidergic signaling is necessary for line attractor dynamics in this system, using a novel approach that integrates cell type-specific, anatomically restricted CRISPR/Cas9-based gene editing with microendoscopic calcium imaging. Co-disruption of receptors for oxytocin and vasopressin in adult VMH Esr1 + neurons that control aggression suppressed attack, reduced persistent neural activity and eliminated line attractor dynamics, while only modestly impacting neural activity and sex- or behavior-tuning. These data identify a requisite role for neuropeptidergic signaling in implementing a behaviorally relevant line attractor. Our approach should facilitate mechanistic studies in neuroscience that bridge different levels of biological function and abstraction.

The positive-negative-competence (PNC) model of psychological responses to representations of robots

Robots are becoming an increasingly prominent part of society. Despite their growing importance, there exists no overarching model that synthesizes people's psychological reactions to robots and identifies what factors shape them. To address this, we created a taxonomy of affective, cognitive and behavioural processes in response to a comprehensive stimulus sample depicting robots from 28 domains of human activity (for example, education, hospitality and industry) and examined its individual difference predictors. Across seven studies that tested 9,274 UK and US participants recruited via online panels, we used a data-driven approach combining qualitative and quantitative techniques to develop the positive-negative-competence model, which categorizes all psychological processes in response to the stimulus sample into three dimensions: positive, negative and competence-related. We also established the main individual difference predictors of these dimensions and examined the mechanisms for each predictor. Overall, this research provides an in-depth understanding of psychological functioning regarding representations of robots.

Structural degeneration of the nucleus basalis of Meynert in mild cognitive impairment and Alzheimer's disease - Evidence from an MRI-based meta-analysis

Recent models of Alzheimer's disease (AD) suggest that neuropathological changes of the medial temporal lobe, especially entorhinal cortex, are preceded by degenerations of the cholinergic Nucleus basalis of Meynert (NbM). Evidence from imaging studies in humans, however, is limited. Therefore, we performed an activation-likelihood estimation meta-analysis on whole brain voxel-based morphometry (VBM) MRI data from 54 experiments and 2581 subjects in total. It revealed, compared to healthy older controls, reduced gray matter in the bilateral NbM in AD, but only limited evidence for such an effect in patients with mild cognitive impairment (MCI), which typically precedes AD. Both patient groups showed less gray matter in the amygdala and hippocampus, with hints towards more pronounced amygdala effects in AD. We discuss our findings in the context of studies that highlight the importance of the cholinergic basal forebrain in learning and memory throughout the lifespan, and conclude that they are partly compatible with pathological staging models suggesting initial and pronounced structural degenerations within the NbM in the progression of AD.

Anxiety is not the right choice! Individual differences in trait anxiety modulate biases in pseudoneglect

Pseudoneglect, the tendency to display a leftward perceptual bias, is consistently observed in line bisection tasks. Some studies have shown that pseudoneglect is sensitive to emotions. This emotion-related modulation is likely related to valence-dependent hemispheric lateralization, although the results do not converge. A possible explanation for these inconsistencies could be individual differences in emotional tone. Considering that negative and positive emotions produce different basic activations of the two hemispheres, emotional characteristics of the subjects, such as trait anxiety, could in fact modulate the pseudoneglect phenomenon. To verify this, high- and low-anxiety participants were asked to centrally bisect horizontal lines delimited by neutral or emotional (happy and sad) faces. In line with previous studies, results here showed a decrease in the leftward bisection error in the presence of happy faces, indicating a greater involvement of the left hemisphere in processing positive emotional stimuli. In addition, trait anxiety influenced the magnitude of the visual bias. High-anxiety subjects, compared to low-anxiety subjects, showed a general bias in visual attention toward the left space as a function of emotional valence. Results are discussed within the framework of valence-dependent hemispheric specialization and the relative degree of activation. In sum, our data highlight the relevance of considering emotional individual differences in studying the pseudoneglect phenomenon.

Effects of emotion, emotional tolerance, and emotional processing on reasoning

Emotion plays a significant role in our reasoning even without awareness, perhaps especially for individuals who have difficulties tolerating strong, negative emotions. Opportunity for reflection may help such individuals decide when emotions should influence reasoning. Two studies attempted to clarify the relationships among reasoning, emotions, and emotion tolerance (measured with the Affect Intolerance Scale). The first examined the effect of affect intolerance on a reasoning task. Participants were asked to determine whether conclusions logically followed from both emotional and neutral if-then statements. Emotion had a small effect on performance on the reasoning task, unmoderated by affect intolerance. The second study examined whether reflection on emotional responses impacts performance on the same reasoning task. Participants asked to reflect on their emotions performed more poorly on the reasoning task than participants asked to reflect on the task's cognitive aspects. People who endorse greater affect tolerance performed better in the cognitive reflection condition than the emotional reflection condition. People with less tolerance performed the same in both conditions. Overall, these studies support previous findings that emotion can negatively impact performance on reasoning tasks but suggest a more complex relationship for affect intolerance.

Role of facial familiarity and emotional expression intensity in ensemble emotion perception

When looking at groups of people, we can extract information from the different faces to derive properties of the group, such as its average facial emotion, although how this average is computed remains a matter of debate. Here, we examined whether our participants' personal familiarity with the faces in the group, as well as the intensity of the facial expressions, biased ensemble perception. Participants judged the average emotional expression of ensembles of four different identities whose expressions depicted either neutral, angry, or happy emotions. For the angry and happy expressions, the intensity of the emotion could be either low (e.g., slightly happy) or high (very happy). When all the identities in the ensemble were unfamiliar, the presence of any high intensity emotional face biased ensemble perception towards its emotion. However, when a familiar face was present in the ensemble, perception was biased towards the familiar face's emotion regardless of its intensity. These findings reveal that how we perceive the average emotion of a group is influenced by both the emotional intensity and familiarity of the faces comprising the group, supporting the idea that different faces may be weighted differently in ensemble perception. These findings have important implications for the judgements we make about a group's overall emotional state may be biased by individuals within the group.

Decoding Emotion From High-frequency Steady State Visual Evoked Potential (SSVEP)

BACKGROUND

Steady-state visual evoked potential (SSVEP) by flickering sensory stimuli has been widely applied in the brain-machine interface (BMI). Yet, it remains largely unexplored whether affective information could be decoded from the signal of SSVEP, especially from the frequencies higher than the critical flicker frequency (an upper-frequency limit one can see the flicker).

NEW METHOD

Participants fixated on visual stimuli presented at 60Hz above the critical flicker frequency. The stimuli were pictures with different affective valance (positive, neutral, negative) in distinctive semantic categories (human, animal, scene). SSVEP entrainment in the brain evoked by the flickering stimuli at 60Hz was used to decode the affective and semantic information.

RESULTS

During the presentation of stimuli (1s), the affective valance could be decoded from the SSVEP signals at 60Hz, while the semantic categories could not. In contrast, neither the affective nor the semantic information could be decoded from the brain signal 1second before the onset of stimuli.

COMPARISON WITH EXISTING METHOD(S)

Previous studies focused mainly on EEG activity tagged at frequencies lower than the critical flickering frequency and investigated whether the affective valence of stimuli drew participants' attention. The current study was the first to use SSVEP signals from high-frequency (60Hz) above the critical flickering frequency to decode affective information from stimuli. The high-frequency flickering was invisible and thus substantially reduced the fatigue of participants.

CONCLUSIONS

We found that affective information could be decoded from high-frequency SSVEP and the current finding could be added to designing affective BMI in the future.

GIGN: Learning Graph-in-graph Representations of EEG Signals for Continuous Emotion Recognition

Effectively learning the spatial topology information of EEG channels as well as the temporal contextual information underlying emotions is crucial for EEG emotion regression tasks. In this paper, we represent EEG signals as spatial graphs in a temporal graph (SGTG). A graph-in-graph neural network (GIGN) is proposed to learn the spatial-temporal information from the proposed SGTG for continuous EEG emotion recognition. A spatial graph neural network (GCN) with a learnable adjacency matrix is utilized to capture the dynamical relations among EEG channels. To learn the temporal contextual information, we propose to use GCN to combine the short-time emotional states of each spatial graph embeddings with the help of a learnable adjacency matrix. Experiments on a public dataset, MAHNOB-HCI, show the proposed GIGN achieves better regression results than recently published methods for the same task. The code of GIGN is available at: https://github.com/yi-ding-cs/GIGN.

Emotionality effects in Korean visual word recognition: Evidence from lab-based and web-based lexical decision tasks

Previous studies have shown that processes of word recognition are influenced by the emotional content of a word. This pattern is most readily explained by the motivated attention and affective states model (Lang, Bradley & Cuthbert, 1997), which states that emotional stimuli are motivationally significant and capture attention. Drawing on this theoretical account, the current study compared lexical decision response times to positive and negative emotion words versus neutral words across two experimental environments - a traditional lab-based environment and a web-based environment. In addition, the experiment was conducted using Korean words presented to native Korean speakers in order to test whether the emotionality effect emerges in a non-English language. The results revealed faster response times to emotion words versus neutral words across both experimental environments with no evidence of a difference between the two environments. These findings provide important evidence that emotion words successfully attract attention and facilitate word processing even in situations where participants might be more easily distracted than they would be in a traditional lab setting. This work also constitutes the first demonstration of an emotionality effect in Korean word recognition, thus providing further evidence that the emotionality effect may be a language-universal phenomenon.

The Association Between Emotional Expressivity in Autobiographies from Early Adulthood and the Risk of Dementia in the Context of Written Language Skills

Background

Risk factors for dementia, such as Alzheimer's disease, are complex and span a lifetime. Exploring novel factors, such as characteristics of writing, may provide insight into dementia risk.

Objective

To investigate the association between emotional expressivity and risk of dementia in the context of a previously identified risk factor, written language skills.

Methods

The Nun Study recruited 678 religious sisters aged 75 + years. Of these, 149 U.S.-born participants had archived autobiographies handwritten at a mean age of 22 years. The autobiographies were scored for frequency of emotion word usage and language skills (e.g., idea density). The association of emotional expressivity and a four-level composite variable (combining high/low emotional expressivity and high/low idea density) with dementia was assessed using logistic regression models adjusted for age, education, and apolipoprotein E.

Results

Within the composite variable, odds of dementia increased incrementally, with opposing effects of emotional expressivity across the two idea density levels. Compared to the referent category (low emotional expressivity/high idea density), the risk of dementia increased in those with high emotional expressivity/high idea density (OR = 2.73, 95% CI = 1.05-7.08), while those with low emotional expressivity/low idea density had the highest risk (OR = 18.58, 95% CI = 4.01-86.09).

Conclusion

Dementia risk is better captured by inclusion of multiple measures relating to characteristics of writing. Emotional expressivity may be protective when individuals are at increased risk due to poor written language skills (i.e., low idea density), but detrimental when not at risk (i.e., high idea density). Our findings indicate that emotional expressivity is a contextually-dependent novel risk factor for dementia.

Risky Decision-taking Task: a novel paradigm to assess the risk-taking behaviour in rats predisposed to early-life stress

One of the characteristic features of adolescence is risk-taking behavioural traits. Uncontrolled risk-taking without proper assessment may have harmful impact on mental health later in life. Therefore, it is essential to identify it early for the preventable health problems. In the present study, we have designed a novel paradigm, viz. Risky Decision-taking Task (RDTT), to evaluate the spontaneous risk-taking behavioural repertoire in adolescent rodents. The task was designed based on both risk and cognitive factors. To validate and compare the risk-taking tendency, we have used early maternal separation and isolation (MS) stress model, as it is known to increase anxiety and curiosity-like behaviour at adolescence. We have used Sprague-Dawley rats of both sexes. Rats were exposed to MS stress for 10 days daily for six hours during stress hyporesponsive period (SHRP) from postnatal day 4 to 13. These rats were subjected to RDTT during adolescence. This task is a reward-based task where the latency to collect reward in the presence or absence of a risk factor is assessed. It consists of habituation, training to find the location of small and large rewards, reward preference for small and large reward and testing period under risky situation. Rats were trained individually to retrieve the valuation-based rewards under the risky, but innate aversive environments. The results from RDTT showed that as compared to controls, MS rats from both sexes showed reduced latency to collect large reward in the presence of a risk element and a reduced risk-index which is indicative of a higher risk-taking tendency in these rats. In addition, MS rats showed a trend towards anxiety-like behaviour as compared to controls in the Light-Dark Test. These results together show decreased risk latency for the large reward and reduced risk assessment in MS rats which is suggestive of more risk-taking tendency in these rats. Thus, we propose that RDTT paradigm can be used to evaluate the spontaneous risk-taking behavioural repertoire based on innate, spontaneous aversion and cognitive factors in rats.

Intolerance of uncertainty and anxiety among college students during the re-emergence of COVID-19: Mediation effects of cognitive emotion regulation and moderation effects of family function

OBJECTIVE

In June 2021, the COVID-19 spread again in the community, and residents had to face the impact of the outbreak again after 276 days, none of the local cases in Guangdong Province, China. The purpose of this study was to investigate the mechanisms underlying the relationship between intolerance of uncertainty (IU) and anxiety in college students in non-epidemic area during the periods of re-emergence of COVID-19.

METHODS

A survey was conducted among 86,767 college students in Guangdong Province, China from 10 to 18 June 2021, information on the Intolerance of Uncertainty Scale (IUS), General Anxiety Disorder-7 (GAD-7), Cognitive Emotion Regulation Questionnaire (CERQ) and Family APGAR Index were collected. Five moderation and mediation models were analyzed using latent moderated structural equations.

RESULTS

The results showed that IU was positively related to anxiety (r = 0.42, p < 0.000). After controlling for age and gender, latent moderated structural equations indicated that catastrophizing mediated the relationship between IU and anxiety, and family function acted as a moderator in this relationship. Further analyses indicated that IU directly affected anxiety and had indirect effects on anxiety by catastrophizing. This relationship was weaker among college students who reported lower family function.

CONCLUSION

This study provides practical implications for designing intervention strategies to reduce anxiety in college students when the epidemic re-emerges.

Dynamic autonomic nervous system states arise during emotions and manifest in basal physiology

The outflow of the autonomic nervous system (ANS) is continuous and dynamic, but its functional organization is not well understood. Whether ANS patterns accompany emotions, or arise in basal physiology, remain unsettled questions in the field. Here, we searched for brief ANS patterns amidst continuous, multichannel physiological recordings in 45 healthy older adults. Participants completed an emotional reactivity task in which they viewed video clips that elicited a target emotion (awe, sadness, amusement, disgust, or nurturant love); each video clip was preceded by a pre-trial baseline period and followed by a post-trial recovery period. Participants also sat quietly for a separate 2-min resting period to assess basal physiology. Using principal components analysis and unsupervised clustering algorithms to reduce the second-by-second physiological data during the emotional reactivity task, we uncovered five ANS states. Each ANS state was characterized by a unique constellation of patterned physiological changes that differentiated among the trials of the emotional reactivity task. These ANS states emerged and dissipated over time, with each instance lasting several seconds on average. ANS states with similar structures were also detectable in the resting period but were intermittent and of smaller magnitude. Our results offer new insights into the functional organization of the ANS. By assembling short-lived, patterned changes, the ANS is equipped to generate a wide range of physiological states that accompany emotions and that contribute to the architecture of basal physiology.

Interactive teaching enhances students' physiological arousal during online learning

The pure transfer of face-to-face teaching to a digital learning environment can be accompanied by a significant reduction in the physiological arousal of students, which in turn can be associated with passivity during the learning process, often linked to insufficient levels of concentration and engagement in the course work. Therefore, the aim of this study was to investigate whether students' psychobiological stress responses can be enhanced in the context of anatomical online learning and how increased physiological parameters correlate with characteristics of learning experiences in a digital learning environment. Healthy first-year medical students (n = 104) experienced a regular practical course in Microscopic Anatomy either in face-to-face learning, in passive online learning or in an interaction-enhanced version of online learning. Compared to passive online learning, students engaged in the interaction-enhanced version of online learning displayed a significantly reduced Heart Rate Variability (P 0.001, partial η² = 0.381) along with a strong increase in salivary cortisol (P 0.001, partial η² = 0.179) and salivary alpha-amylase activity (P 0.001, partial η² = 0.195). These results demonstrated that the physiological arousal of students engaged in online learning can be enhanced via interactive teaching methods and pointed towards clear correlations between higher physiological responses and elementary criteria of learning experience such as engagement and attention.

Lateral septal nucleus, dorsal part, and dentate gyrus are necessary for spatial and object recognition memory, respectively, in mice

Introduction

Cognitive impairment includes the abnormality of learning, memory and judgment, resulting in severe learning and memory impairment and social activity impairment, which greatly affects the life quality of individuals. However, the specific mechanisms underlying cognitive impairment in different behavioral paradigms remain to be elucidated.

Methods

The study utilized two behavioral paradigms, novel location recognition (NLR) and novel object recognition (NOR), to investigate the brain regions involved in cognitive function. These tests comprised two phases: mice were presented with two identical objects for familiarization during the training phase, and a novel (experiment) or familiar (control) object/location was presented during testing. Immunostaining quantification of c-Fos, an immediate early gene used as a neuronal activity marker, was performed in eight different brain regions after the NLR or NOR test.

Results

The number of c-Fos-positive cells was significantly higher in the dorsal part of the lateral septal nucleus (LSD) in the NLR and dentate gyrus (DG) in the NOR experiment group than in the control group. We further bilaterally lesioned these regions using excitotoxic ibotenic acid and replenished the damaged areas using an antisense oligonucleotide (ASO) strategy.

Discussion

These data reinforced the importance of LSD and DG in regulating spatial and object recognition memory, respectively. Thus, the study provides insight into the roles of these brain regions and suggests potential intervention targets for impaired spatial and object recognition memory.

The influence of bilingual experience on executive function under emotional interference: Evidence from the N1 component

The influence of bilingual education and experience on an individual’s information-processing ability has recently been a hot issue in international studies. Previous studies have found that bilingual experience affects executive function, but the results remain controversial. Executive function refers to the conscious control of purposeful behavior. It is responsible for processing high-level action controls, including such sub-functions as inhibitory control, cognitive switching, and working memory updating. Emotion, as an essential factor in daily life, also has a complex interaction with executive function. This paper explores whether the bilingual cognitive advantage effect can continue in the more complex conditions of emotional interference. To investigate the specific electrophysiological characteristics of the participants at different stages of cognitive processing, we used a combination of the behavioral and ERP experiments in which the positive, neutral, and negative emotional stimuli were selected as emotional interference conditions and the emotional Simon paradigm, the cognitive switching of emotion paradigm, and the emotional N-back paradigm was adopted. The results show that the main effect of the N1 component amplitude is significant. Specifically, the amplitude of the N1 component in the proficient bilinguals is significantly smaller than that of the non-proficient bilinguals, while the main effects of other component groups are not significant, indicating that under the condition of emotional interference, the influence of bilingual experience on executive function only exists in the early attention stage and that the bilingual experience can improve the individual’s attentional control and speed up attention processing in the early attention stage.

The effect of sad mood on early sensory event-related potentials to task-irrelevant faces

It has been shown that the perceiver's mood affects the perception of emotional faces, but it is not known how mood affects preattentive brain responses to emotional facial expressions. To examine the question, we experimentally induced sad and neutral mood in healthy adults before presenting them with task-irrelevant pictures of faces while an electroencephalography was recorded. Sad, happy, and neutral faces were presented to the participants in an ignore oddball condition. Differential responses (emotional - neutral) for the P1, N170, and P2 amplitudes were extracted and compared between neutral and sad mood conditions. Emotional facial expressions modulated all the components, and an interaction effect of expression by mood was found for P1: an emotional modulation to happy faces, which was found in neutral mood condition, disappeared in sad mood condition. For N170 and P2, we found larger response amplitudes for both emotional faces, regardless of the mood. The results add to the previous behavioral findings showing that mood already affects low-level cortical feature encoding of task-irrelevant faces.

Relationships between feeding behaviors and emotions: an electroencephalogram (EEG) frequency analysis study

Feeding behaviors may be easily affected by emotions, both being based on brain activity; however, the relationships between them have not been explicitly defined. In this study, we investigated how emotional environments modulate subjective feelings, brain activity, and feeding behaviors. Electroencephalogram (EEG) recordings were obtained from healthy participants in conditions of virtual comfortable space (CS) and uncomfortable space (UCS) while eating chocolate, and the times required for eating it were measured. We found that the more participants tended to feel comfortable under the CS, the more it took time to eat in the UCS. However, the EEG emergence patterns in the two virtual spaces varied across the individuals. Upon focusing on the theta and low-beta bands, the strength of the mental condition and eating times were found to be guided by these frequency bands. The results determined that the theta and low-beta bands are likely important and relevant waves for feeding behaviors under emotional circumstances, following alterations in mental conditions.

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.