Self-efficacy is related to the judgments and beliefs that a person has about him or her own capability to achieve goals, in which she or he also needs to be able of planning, organizing, and executing tasks to achieve that milestone. In this study, we are investigating if attention has a relevant role in self-efficacy. The participants were students at Institución Universitaria de Envigado (N=25), aged between 18 and 40 years old. They filled out the informed consent, the General Self-Efficacy Scale (GSS), Digits and Symbols (DS), the Brief Attention Test (BTA), and the Theta-Beta ratio (TBR) using EEG at points C3-C4 of the cerebral cortex. The results were as follows: mean GSS, 31.56 (SD=4.5) (max. 40 points); mean DS direct score, 45.16 (SD=8.6) (max. 120 points); mean total BTA, 9.4 (SD = 3.31) (max. 20 points); mean TBR C3 eyes open, 5.5 (SD = 1.7); TBR C4 eyes open, 5.2 (SD = 2). A negative correlation was found between the TBR C4 eyes open and the result of the Digits and Symbols DS test, which was statistically significant, using Spearman correlation, (-.529); however, there was no significant correlation between GSS self-efficacy and the three measures of attention (DS, BTA, QEEG). The conclusion of this study is that there is no clear statistically significant relationship between high self-efficacy and a high level of attention. However, a sig nificant negative correlation was found between the DS test and the QEEG measures, which indicates that the neurophysiological technique of attentional measurement is related to the psychometric measurement.

The ability to see and process images depends on the function of the eyes and the processing of visual information by neurons in the cerebral cortex, something that could be measured through electroencephalography (EEG). Although the EEG is used to evaluate visual pathways in children and demyelination diseases, the limited utilization of brain recording techniques in other applications like therapy is primarily due to budget constraints. The goal of this paper is to demonstrate results from studying brain aspects of vision, utilizing measurements based on oscillatory activity analysis, low-cost, portable equipment, and a processing pipeline relying on Python's open-source libraries. These studies involve healthy subjects who wear glasses to assess changes in visual perception.

This article presents the findings of a study that seeks to explore how im plementing Lean Management, a widely used form of flexible management, influences employees' subjective experiences at work. The study focuses on the changes and innovations by Lean Management in the technical and social aspects of work after its introduction in the Chilean retail industry. The study, which is both descriptive and analytical, is based on 26 interviews with industry consultants, managers, department heads, and union leaders. Using a work-clinic and socio-phenomenological framework, it illustrates how the industry's unique characteristics play a crucial role in understanding the process of work reorganization and the paradoxical realms of subjective experience it has created. The article also discusses how these contradictions are influenced by the broader social, labor, and business context of neoliberal modernization in Chilean society.

The pursuit of a unified theory that captures the intricacies of the brain and mind continues to be a significant challenge in theoretical neuroscience. This paper presents a novel, triune framework that utilizes the concept of collective biased random walk (cBRW). Our approach strives to transcend biological specifics, offering a high-level abstraction that remains general and applicable across various neural phenomena. Despite the solid traditional foundation of computational neuroscience, the intricate delicacy of neural processes calls for a renewed probabilistic approach. We aim to utilize the intuitive nature of probability concepts -such as the probability of localization and state, and uniform probability distribution- to study the stochastic organization of electric charges and signals in the brain. This electrophysiological intricacy emerges from the seemingly paradoxical reality that tiny electric events, while random, collectively give rise to predictable, long-range oscillations. These oscillations manifest in three groups of activation states. Our framework categorizes the brain as a triune system, accommodating classical, semiclassical, and non-classical interpretations of both probabilistic phenomena and cBRW models, alongside three groups of states. We conclude that by appreciating, rather than overlooking, the tiny random walks of electric charges and signals in the brain, we can gain a triune mathematical foundation for theoretical brain science, the powerful capabilities of this organ, and the electromagnetic interfaces we can develop.

This study focuses on understanding visual coding in multiple brain areas and its implications for neural processing in the visual system. It highlights the use of simultaneous recordings of large neuronal populations to inves tigate how visual information is encoded and processed in the brain. By studying the activity of multiple brain areas, the paper aims to uncover the mechanisms underlying brain-wide visual perception and provide insights into the neural basis of visual processing. The findings of this research contribute to the broader field of neuroscience and have implications for understanding visual disorders and developing therapeutic interventions.

The influence of behavioral disinhibition may vary according to the way this parameter is assessed and in relation to different patterns of smoke. This study evaluated the effect of gender, levels of addiction, nicotine deprivation and smoking patterns on behavioral disinhibition in smokers and nonsmokers. A sample of 180 participants from 18 to 30 years old was recruited to complete the Parametric Go/No-Go (PGNG) and Stop Signal Task (SST). The results identified that smokers have more difficulty inhibiting a prepotent response than nonsmokers in SST, but not with PGNG. Female nonsmokers presented shorter SSRT than male nonsmokers and smokers. Moderate to high nicotine dependence influenced the poor precision on no-go trials of PGNG. Smoking treatment should not be directly influenced by gender, but understanding the effects of smoking history and nicotine deprivation is a key aspect in facilitating smoking cessation and prevention.

. During adolescence, conduct disorders emerge, associated with frontal alterations and executive function (EF) deficits, influencing delinquent trajectories. The study aimed to compare EF in delinquent (N = 125) and non-delinquent (N = 153) adolescents.

The objectives of this study were to develop the Laboratory of Immersive Learn ing in Health and Nursing - LIASE, based on the main themes of biosafety in health, and to evaluate the learning process of undergraduate nursing students from a public federal university through portable Electroencephalogram (EEG) Emotiv Insight 2.0, observation, and Intrinsic Motivation Inventory. The present research contains a qualitative-quantitative, exploratory, and experimental methodology from a pilot virtual laboratory, developed in the Immersive Virtual World - IVW, represented in this study by Second Life - SL. The sample consisted of 17 students who agreed with the inclusion criteria of the study. Among them, 9 students had stable EEG signals. Those students were observed during the monitoring of brain activity by the EEG, and at the end of the proposed learning pathway, they filled out the Intrinsic Motivation Inventory (IMI). The results were obtained through triangulation of the different collec tion instruments and the following variables: Stress, Enthusiasm/Excitement, Engagement, Focus/attention, and Relaxation, measured and verified by the Emotiv algorithm's brain wave analysis, which algorithm which correspond to the metrics of brain performance.

To report the psychometric properties of the Spanish version of the Oldenburg Burnout Inventory (OLBI) in two samples of university professors from Bogotá and other regions of Colombia.

Vitamin D is a neurosteroid that modulates multiple brain functions and may be involved in the clinical presentation of psychotic disorders. Vitamin D deficiency has been associated with schizophrenia and cognitive decline, particularly in executive functions.

Fasting might affect attentional processes; however, its effects on quantitative electroencephalographic activity (qEEG) remain unclear. We used an n-back task to assess the effects of an 18-hour fasting period on behavior and qEEG absolute power. 26 participants performed the experimental task with two cognitive load levels during fasting and regular breakfast in different sessions. Artifact-free EEG epochs were selected for further analysis between conditions. The higher cognitive load affected accuracy, which decreased, while frontal and parietal theta power increased. We also found greater absolute theta power magnitudes for the left-frontocentral locations and a significant interaction between cognitive load and recording site, reflecting the greater increase in left-central parietal locations. Alpha increased in left-frontocentral locations. Although fasting did not consider- ably vary EEG power, there was a relevant fasting-related increase in theta power over frontal areas, probably reflecting transient changes in cognitive control mechanisms.

The main objective of this study was to analyze the role of the Big Five personality traits (extraversion, agreeableness, conscientiousness, neuroticism, andopenness to experience) in the capacity to love. The capacity to love involves the ability to engage in, invest in, and sustain a committed romantic relationship, arising from complex developmental processes beginning in childhood and evolving throughout life. Although it may appear as a dispositional attribute, limited evidence exists regarding its association with personality traits.

Parkinson's disease (PD) is a common neurodegenerative disorder worldwide, with over 6.2 million registered cases. Gait analysis plays a fundamental role in evaluating motor abnormalities associated with this disease. However, current methods, such as marker-based systems, are intrusive and expert-dependent. Markerless alternatives, like video sequence analysis, have been proposed, but they tend to provide overall classification scores and lack the ability to interpret joint kinematics in detail. An innovative technique is presented using volumetric convolutional networks that can learn intermediate postural patterns and distinguish between Parkinson's patients and control subjects. This approach utilizes activations and then applies hierarchical convolution to minimize classification. In tests conducted with 14 Parkinson's patients and 16 control subjects, this method achieved a classification accuracy of 98%.

The main objective is to analyze the directionality and frequency of different offline and online psychological violence in young heterosexual couples, and to examine the degree of agreement of the partners in their perception and motives of their use of violence. 230 young couples completed the study. The average age was 19.27 for women (SD = 1.73; range 15-24) and 20.51 for men (SD = 2.83; range 16-28). The results showed a high prevalence of violence with the exception of direct online aggression. Frequency was low, and agreement was generally acceptable. There were significant differences between the violence reported by each member. Both partners reported a low self-perception of victims or aggressors, and a low perception of their abusive online behaviors as a form of violence. The main justifications were the same for members. These results could help explain the relationship between adherence to romantic love myths and psychological violence.

Epilepsy is a worldwide public health issue, given its biological, social, and economic impacts. Considering several open questions about synchronization and desynchronization mechanisms underlying epileptic phenomena, the development of algorithms and computational toolboxes for such analysis is highly relevant to their research. Moreover, given the recent developments of neurotechnology for epilepsy, it is essential to understand that proposals like computational tools may provide consistent data for closed-loop control systems, necessary in neuromodulation treatment alternatives, and for real-time monitoring systems to predict the occurrence of epileptic seizures. In the present work, SynchroLINNce, a freely distributable MATLAB toolbox designed to be used by epilepsy neuroscientists, including software-untrained), is proposed. Among its features, several functionalities such as recording visualization, digital filtering, and correlation analysis, as well as more specific methodologies, such as mechanisms for the automatic detection of epileptiform spikes, morphology analysis of these spikes, and their coincidence between channels are presented.

Today, alcohol drinking frequently accompanies socialising as a routine activity in various groups of society. 84.0% of individuals aged 18 and above in the United States have drunk alcohol at some point in their life (National Institute on Alcohol Abuse & US, 2023). Similarly, 81.7% of Norwegians in the age group 16 to 79 have drunk alcohol in 2021 (Bye, 2018). Driving after the consumption of alcohol is a worldwide problem, causing a large number of deaths and injuries a year. This work proposes the first steps towards developing an electroencephalography (EEG)-based alcohol detector conceived with the idea to prevent people from driving under the influence of alcohol. This includes the design of an experimental protocol for EEG data collection, during which participants performed the Flanker task, and their blood alcohol concentration (BAC) was measured. The resulting data set consists of two sessions per participant, both while they are affected and not-affected by alcohol. Statistical analysis of the Flanker task indicated that participants were affected by alcohol and, therefore, their EEG signals were expected to be affected as well. The collected EEG signals were used as input for intra-subject and inter-subject models, both based on the EEGNet architecture. The intra-subject model obtained a mean classification accuracy of 90.7% and the inter-subject model a mean classification accuracy of 62.9%. The result suggest that alcohol can be detected with high accuracy when developing individual models and above the change accuracy when using a general model. Therefore, the work presented here could be used as the first steps towards the development of an EEG-based alcohol detector for drivers.

Despite institutional efforts, reconciliation among former actors of the Colombian armed conflict has yet to be achieved, with prejudice being one direct driver of this drawback. We present an EEG-based functional connectivity study applied to four groups of former actors who completed an Implicit Association Test designed to measure prejudice toward victims or combatants. We analyzed seven measures of functional connectivity calculated in six different frequency bands and two experimental conditions. In the behavioral task, we found more prejudice toward victims from the same victims and more prejudice of civilians toward combatants. For the connectivity measures, we found differences in theta band among the victims' and ex-paramilitaries' groups concerning the civilians' and ex-guerrillas' groups, and differences in the beta2 band among the victims' and ex-guerrillas' groups concerning the ex-paramilitaries' group. The results help us design more effective socio-cognitive interventions to reduce prejudice.

Stroke, the second leading cause of death globally, necessitates prompt diagnosis for effective prognosis. CT imaging has limitations, especially in identifying acute lesions. This work introduces a novel deep repre sentation that uses multimodal inputs from CT studies and perfusion parametric maps, to retrieve stroke lesions. The architecture follows an autoencoder representation that forces attention on the geometry of stroke through additive cross-attention modules. Besides, a cascade train is herein proposed to generate synthetic perfusion maps that complement multimodal inputs, refining stroke lesion segmentation at each stage of processing and supporting the observational expert analysis. The proposed approach was validated on the ISLES 2018 dataset with 92 studies; the method outperforms classical techniques with a Dice score of .66 and a precision of .67.

The behavioral strategies for emotional regulation in adolescents are associated with mental health issues such as depression, making it important to have tools that allow for their assessment. This study uses an instrumental design and aims to evaluate the psychometric properties of the Behavioral Emotion Regulation Questionnaire (BERQ) in Peruvian adolescents. The sample was selected using a non-probabilistic convenience sampling method and consisted of 392 adolescents aged 14 to 17 years (M = 15.75; SD = .85), with 52.5% being male. Through exploratory structural equation modeling, it was found that the original five-factor structure was replicated (CFI = .988; RMSEA = .045, 90% CI .031, .058; WRMR = .429), and each factor achieved acceptable reliability values through Cronbach's alpha and McDonald's omega coefficients. Additionally, it was found that only withdrawal (β = -.49), active coping (β = -.21), and ignoring the problem (β = .24) are statistically significant predictors of depression. It is concluded that the instrument can be used in research and as support for professionals in the evaluation of the construct.