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Previous research has shown that state anxiety facilitates stimulus-driven attentional capture and impairs goal-directed attentional control by increasing sensitivity to salient distractors or threat cues or narrowing spatial attention. However, recent findings in this area have been mixed, and less is known about how state-dependent anxiety may affect attentional performance. Here, we employed a novel dual-target search paradigm to investigate this relationship. This paradigm allowed us to investigate attentional control and how focus narrows under different anxiety states. Participants watched a short movie-either anxiety-inducing or neutral-before engaging in the dual-target visual search task. We found that they performed faster and more accurately in trials without the salient distractor compared to those with distractors, and they performed better in tasks presented on the center than the periphery. However, despite a significant increase in self-reported anxiety in the anxiety-inducing session, participants' performance in terms of speed and accuracy remain comparable across both anxious and neutral sessions. This resilience is likely due to compensatory mechanisms that offset anxiety, a result of the high demands and working memory load inherent in the dual-target task., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Some studies have suggested that emotion-associated features might influence attentional capture. However, demonstrating valence-dependent distractor interference has proven challenging, possibly due to the neglect of individuals' color-valence preferences in standard, averaged reaction-time (RT) measures. To address this, we investigated valence-driven attentional-capture using an association phase in which emotionally neutral vs. positive-feedback photographs were paired with two alternative target colors, red vs. green. This was followed by a test phase requiring participants to search for a pop-out shape target in the presence or absence of an emotion-associated color. In Experiments 1 and 2, this color could only appear in a distractor, while in Experiment 3, it appeared in the target. Analyzing the standard, averaged RT measures, we found no significant valence association or valence-modulated attentional capture. However, correlational analyses revealed a positive relationship between individual participants' color-valence preference during the association phase and their valence-based effect during the test phase. Moreover, most individuals favored red over green in the association phase, leading to marked color-related asymmetries in the average measures. Crucially, the presence of the valence-preferred color anywhere in the test display facilitated RTs. This effect persisted even when the color appeared in one of the distractors (Experiments 1 and 2), at variance with this distractor capturing attention. These findings suggest that task-irrelevant valence-preferred color signals were registered pre-attentively and boosted performance, likely by raising the general (non-spatial) alertness level. However, these signals were likely kept out of attentional-priority computation to prevent inadvertent attentional capture., (© 2023. The Author(s).)

SWIR imaging bears considerable advantages over visible-light (color) and thermal images in certain challenging propagation conditions. Thus, the SWIR imaging channel is frequently used in multi-spectral imaging systems (MSIS) for long-range surveillance in combination with color and thermal imaging to improve the probability of correct operation in various day, night and climate conditions. Integration of deep-learning (DL)-based real-time object detection in MSIS enables an increase in efficient utilization for complex long-range surveillance solutions such as border or critical assets control. Unfortunately, a lack of datasets for DL-based object detection models training for the SWIR channel limits their performance. To overcome this, by using the MSIS setting we propose a new cross-spectral automatic data annotation methodology for SWIR channel training dataset creation, in which the visible-light channel provides a source for detecting object types and bounding boxes which are then transformed to the SWIR channel. A mathematical image transformation that overcomes differences between the SWIR and color channel and their image distortion effects for various magnifications are explained in detail. With the proposed cross-spectral methodology, the goal of the paper is to improve object detection in SWIR images captured in challenging outdoor scenes. Experimental tests for two object types (cars and persons) using a state-of-the-art YOLOX model demonstrate that retraining with the proposed automatic cross-spectrally created SWIR image dataset significantly improves average detection precision. We achieved excellent improvements in detection performance in various variants of the YOLOX model (nano, tiny and x).

In this paper, we propose a new algorithm for distributed spectrum sensing and channel selection in cognitive radio networks based on consensus. The algorithm operates within a multi-agent reinforcement learning scheme. The proposed consensus strategy, implemented over a directed, typically sparse, time-varying low-bandwidth communication network, enforces collaboration between the agents in a completely decentralized and distributed way. The motivation for the proposed approach comes directly from typical cognitive radio networks' practical scenarios, where such a decentralized setting and distributed operation is of essential importance. Specifically, the proposed setting provides all the agents, in unknown environmental and application conditions, with viable network-wide information. Hence, a set of participating agents becomes capable of successful calculation of the optimal joint spectrum sensing and channel selection strategy even if the individual agents are not. The proposed algorithm is, by its nature, scalable and robust to node and link failures. The paper presents a detailed discussion and analysis of the algorithm's characteristics, including the effects of denoising, the possibility of organizing coordinated actions, and the convergence rate improvement induced by the consensus scheme. The results of extensive simulations demonstrate the high effectiveness of the proposed algorithm, and that its behavior is close to the centralized scheme even in the case of sparse neighbor-based inter-node communication.

The present review proposes a novel dynamic model of brain lateralization of emotional (happy, surprised, fearful, sad, angry, and disgusted) and neutral face perception. Evidence to date suggests that emotional face perception is lateralized in the brain. At least five prominent hypotheses of the lateralization of emotional face perception have been previously proposed; the right-hemisphere hypothesis; the valence-specific hypothesis; the modified valence-specific hypothesis; the motivational hypothesis; and behavioral activation/inhibition system hypothesis. However, a growing number of recent replication studies exploring those hypotheses frequently provide inconsistent or even contradictory results. The latest neuroimaging and behavioral studies strongly demonstrate the functional capacity of both hemispheres to process emotions relatively successfully. Moreover, the flexibility of emotional brain-networks in both hemispheres is functionally high even to the extent of a possible reversed asymmetry of the left and the right hemisphere performance under altered neurophysiological and psychological conditions. The present review aims to a) provide a critical conceptual analysis of prior and current hypotheses of brain lateralization of emotional and neutral face perception; b) propose an integrative introduction of a novel hemispheric functional-equivalence (HFE) model in emotional and neutral face perception based on the evaluation of theoretical considerations, behavioral and neuroimaging studies: the brain is initially right-biased in emotional and neutral face perception by default; however, altered psychophysiological conditions (e.g., acute stress, a demanding emotional task) activate a distributed brain-network of both hemispheres toward functional equivalence that results in relatively equalized behavioral performance in emotional and neutral face perception. The proposed novel model may provide a practical tool in further experimental investigation of brain lateralization of emotional face perception., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Empirical evidence has demonstrated functional (mostly right-biased) brain asymmetry for emotion perception, whereas recent studies indicate that acute stress may modulate left and/or right hemisphere activation. However, it is still unknown whether emotion perception can be influenced by stress-induced hemispheric activation since behavioral studies report inconsistent or even contradictory results. We sought to reevaluate this gap. Eighty-eight healthy Caucasian university students participated in the study. In half of the randomly selected participants, acute psychological stress was induced by displaying a brief stressful movie clip (the stress condition), whereas the other half were shown a neutral movie clip (the non-stress condition). Prior to (the baseline) and following the movie clip display an emotion perception task was applied by presenting an emotional (happy, surprised, fearful, sad, angry, or disgusted) or neutral face to the left or right visual field. We found a more accurate perception of emotional and neutral faces presented to the LVF (the right hemisphere) in the baseline. However, we revealed that after watching a neutral movie clip, behavioral performance in emotional and neutral face perception accuracy became relatively equalized for both visual fields, whereas after watching a stressful movie clip, the RVF (the left hemisphere) even became dominant in emotional face perception. We propose a novel hemispheric functional-equivalence model: the brain is initially right-biased in emotional and neutral face perception by default; however, psychophysiological activation of a distributed brain-network due to watching neutral movie clips redistributes hemispheric performance toward relative equivalence. Moreover, even reversed hemispheric asymmetry may occur.

This paper deals with recently proposed algorithms for real-time distributed blind macro-calibration of sensor networks based on consensus (synchronization). The algorithms are completely decentralized and do not require a fusion center. The goal is to consolidate all of the existing results on the subject, present them in a unified way, and provide additional important analysis of theoretical and practical issues that one can encounter when designing and applying the methodology. We first present the basic algorithm which estimates local calibration parameters by enforcing asymptotic consensus, in the mean-square sense and with probability one (w.p.1), on calibrated sensor gains and calibrated sensor offsets. For the more realistic case in which additive measurement noise, communication dropouts and additive communication noise are present, two algorithm modifications are discussed: one that uses a simple compensation term, and a more robust one based on an instrumental variable. The modified algorithms also achieve asymptotic agreement for calibrated sensor gains and offsets, in the mean-square sense and w.p.1. The convergence rate can be determined in terms of an upper bound on the mean-square error. The case when the communications between nodes is completely asynchronous, which is of substantial importance for real-world applications, is also presented. Suggestions for design of a priori adjustable weights are given. We also present the results for the case in which the underlying sensor network has a subset of (precalibrated) reference sensors with fixed calibration parameters. Wide applicability and efficacy of these algorithms are illustrated on several simulation examples. Finally, important open questions and future research directions are discussed.

While previous studies have shown that left- (vs. right-) hand contractions can improve the performance of global (vs. local) attention, these results were inconsistent in certain behavioral studies in which the left cerebral hemisphere was found to be specialized for local attention, while the right hemisphere was not specialized for global attention. Thus, we reexamined whether different global (vs. local) attention performances follow left- (vs. right-) hand contractions due to right (vs. left) hemisphere activation. We recruited 100 right-handed healthy female, university student (aged 19-26 years) participants. We used the Navon task to present 80 letter-stimuli (40 in the global task; 40 in the local task) to either the participants' left or right visual fields. Half of the participants (randomly selected) were instructed to squeeze a dynamometer with the left hand, while the other half squeezed with the right hand, prior to completing the Navon task. In line with previous research, we observed a perceptual advantage of global (large letters) over local (small letters) stimuli. Participants who contracted the right hand showed an enhanced local attention performance (from left hemisphere activation), whereas those who contracted the left hand did not improve global attention performance (from right hemisphere activation). Thus, we supported prior behavioral research suggesting that hemispheric asymmetry for attention processing is evident for left but not right cortical hemisphere processing.

Background: Although schools are a key setting for the provision of mental health support for young people, little is known about the facilitators and barriers for providing such support. This study aimed to collect information from schools in 10 European countries regarding the priority given to mental health support for students, existence of a mental health-related school policy, links with relevant external agencies, schools' perceptions on whether they are providing sufficient mental health support and the barriers to provision of mental health support., Methods: Data from 1346 schools were collected in France, Germany, Ireland, the Netherlands, Poland, Serbia, Spain, Sweden, United Kingdom and Ukraine through an online survey., Results: Around 3% of the surveyed schools indicated that mental health provision was not a priority, compared to 47% indicating that it was a high/essential priority. More than half the surveyed schools did not implement a school policy regarding mental health. Half the surveyed schools reported not providing sufficient support with the key barriers identified including limited staff capacity, funding, access to specialists and lack of national policy and less than a third of schools reported good or excellent links with local mental health services. However, the responses varied by country with 8-19% between-country variation across the study outcomes. Secondary schools reported significantly better links with agencies, were more likely to have a school policy and were less likely to indicate having sufficient existing support compared to primary schools. Privately funded schools reported that mental health support was a higher priority and identified less barriers to provision compared to publicly funded schools., Conclusions: This study provides an up-to-date and cross-country insight into schools' perceptions regarding priority given to mental health support and the barriers they face in providing sufficient mental health and wellbeing support for their students. The cross-country comparisons allow for a better understanding of the relationships between policy, practice and implementation and provide a platform for shared experiences and learning., (© 2016 The Authors. Child and Adolescent Mental Health published by John Wiley & Sons Ltd on behalf of Association for Child Adolescent Mental Health.)