Narcissism, perfectionism, and self-promoting behavior on social networking sites among university students in Pakistan / Narcisismo, perfeccionismo y comportamiento de autopromoción en sitios de redes sociales entre estudiantes universitarios en Pakistán

Existe un debate considerable en la literatura sobre cómo el narcisismo predice diversos comportamientos asociados con la utilidad de los sitios de redes sociales, pero los investigadores han prestado menos atención a explorar los mediadores potenciales de esta relación. Con base en la literatura existente, anticipamos que el narcisismo predice comportamientos de autopromoción en los sitios de redes sociales. El estudio actual también investigó el papel mediador del perfeccionismo multidimensional entre el narcisismo y el comportamiento de autopromoción. Se recopiló un total de 605 cuestionarios completos de estudiantes de universidades de Rawalpindi e Islamabad, Pakistán, mediante un muestreo conveniente. El estudio utilizó el Inventario de Personalidad Narcisista (Ames et al., 2006), un cuestionario de desarrollo propio sobre comportamiento de autopromoción en sitios de redes sociales y la Escala de Perfeccionismo Multidimensional (Hewitt et al., 1991). Los hallazgos indicaron que las mujeres en comparación con los hombres y las solteras en comparación con las casadas obtuvieron puntuaciones más altas en narcisismo. Los niveles educativos más altos se asociaron con tasas más altas de narcisismo. Los resultados también sugieren que el narcisismo se correlaciona con el perfeccionismo orientado a uno mismo y, más significativamente, con el narcisismo orientado a los demás. El perfeccionismo orientado a uno mismo y a los demás medió significativamente la relación entre el narcisismo y el comportamiento de autopromoción en los sitios de redes sociales.(AU)

There is considerable debate in the literature about how narcis-sism predicts various behaviors associated with the utility of social net-working sites, but researchers have paid less attention to exploring the po-tential mediators of this relationship.Based on the existing literature, we anticipated that narcissism predicts self-promoting behaviors on social networking sites. The current study also investigated the mediating role of multidimensional perfectionismbetween narcissism and self-promoting behavior. A total of 605 complete questionnaires weregathered fromstu-dents from universities from Rawalpindi and Islamabad, Pakistan using convenient sampling. The study used Narcissistic Personality Inventory (Ames et al., 2006), self-developed Self-promoting Behavior on social net-working sites questionnaire, and the Multidimensional Perfectionism Scale (Hewitt et al., 1991). Findings indicated that females as compared to males and single as comparedto married individuals scored higher on narcissism. Higher educational levels were associated with higher rates of narcissism. The results also suggestthat narcissism correlated with self-oriented per-fectionism, and more significantlywith others-oriented narcissism. Self-oriented and others-oriented perfectionism significantly mediated the rela-tionship between narcissism and self-promoting behavior on social net-working sites.(AU)

Research into the Applications of a Multi-Scale Feature Fusion Model in the Recognition of Abnormal Human Behavior.

Due to the increasing severity of aging populations in modern society, the accurate and timely identification of, and responses to, sudden abnormal behaviors of the elderly have become an urgent and important issue. In the current research on computer vision-based abnormal behavior recognition, most algorithms have shown poor generalization and recognition abilities in practical applications, as well as issues with recognizing single actions. To address these problems, an MSCS-DenseNet-LSTM model based on a multi-scale attention mechanism is proposed. This model integrates the MSCS (Multi-Scale Convolutional Structure) module into the initial convolutional layer of the DenseNet model to form a multi-scale convolution structure. It introduces the improved Inception X module into the Dense Block to form an Inception Dense structure, and gradually performs feature fusion through each Dense Block module. The CBAM attention mechanism module is added to the dual-layer LSTM to enhance the model's generalization ability while ensuring the accurate recognition of abnormal actions. Furthermore, to address the issue of single-action abnormal behavior datasets, the RGB image dataset RIDS (RGB image dataset) and the contour image dataset CIDS (contour image dataset) containing various abnormal behaviors were constructed. The experimental results validate that the proposed MSCS-DenseNet-LSTM model achieved an accuracy, sensitivity, and specificity of 98.80%, 98.75%, and 98.82% on the two datasets, and 98.30%, 98.28%, and 98.38%, respectively.

Causal inference on human behaviour.

Making causal inferences regarding human behaviour is difficult given the complex interplay between countless contributors to behaviour, including factors in the external world and our internal states. We provide a non-technical conceptual overview of challenges and opportunities for causal inference on human behaviour. The challenges include our ambiguous causal language and thinking, statistical under- or over-control, effect heterogeneity, interference, timescales of effects and complex treatments. We explain how methods optimized for addressing one of these challenges frequently exacerbate other problems. We thus argue that clearly specified research questions are key to improving causal inference from data. We suggest a triangulation approach that compares causal estimates from (quasi-)experimental research with causal estimates generated from observational data and theoretical assumptions. This approach allows a systematic investigation of theoretical and methodological factors that might lead estimates to converge or diverge across studies.

People, places, things and communities: expanding behaviour settings theory in the twenty-first century.

Trends and developments in recent behavioural and cognitive sciences demonstrate the need for a well-developed theoretical and empirical framework for examining the ecology of human behaviour. The increasing recognition of the role of the environment and interaction with the environment in the organization of behaviour within the cognitive sciences has not been met with an equally disciplined and systematic account of that environment (Heft 2018 Ecol. Psychol. 30, 99-123 (doi:10.1080/10407413.2018.1410045); McGann 2014 Synth. Philos. 29, 217-233). Several bodies of work in behavioural ecology, anthropology and ecological psychology provide some frameworks for such an account. At present, however, the most systematic and theoretically disciplined account of the human behavioural ecosystem is that of behaviour settings, as developed by the researchers of the Midwest Psychological Field Station (see Barker 1968 Ecological psychology: concepts and methods for studying the environment of human behavior). The articles in this theme issue provide a critical examination of these theoretical and methodological resources. The collection addresses their theoretical value in connecting with contemporary issues in cognitive science and research practice in psychology, as well as the importance of the methodological specifics of behaviour settings research. Additionally, articles diagnose limitations and identify points of potential extension of both theory and methods, particularly with regard to changes owing to the advance of technology, and the complex relationship between the individual and the collective in behaviour settings work. This article is part of the theme issue 'People, places, things, and communities: expanding behaviour settings theory in the twenty-first century'.

A practitioner's field guide to the behaviour settings method.

Since the 1950s, Roger Barker's theory of behaviour settings has been useful for a wide number of disciplines. Few realize, however, that behaviour settings theory is also a methodology. Barker fully describes how to identify, describe and measure behaviour settings in his seminal book Ecological psychology: concepts and methods for studying the environment of human behavior (1968), and this method is further delineated in Phil Schoggen's Behavior settings: a revision and extension of Roger G. Barker's ecological psychology (1989). Nevertheless, beyond these two (rather expensive) books there are few other resources available to twenty-first century researchers who wish to systematically describe and measure behaviour in its ecological context using the principles of behaviour settings theory. In this article, I offer a practitioner's field guide to implementing the behaviour settings method, which includes a contemporary illustration of defining a behaviour setting using a recent observational study of an art gallery in Lethbridge, Canada. I discuss how researchers can use Barker's original methodology to determine what is a behaviour setting and how to define its boundaries, and I suggest best practices, offering practitioners the tools to replicate Barker's procedures. This article is part of the theme issue 'People, places, things, and communities: expanding behaviour settings theory in the twenty-first century'.

Physiology, patterns and behavior.

This article endeavors to provide a useful perspective for Researchers and Authors within the realm of Behavioral Sciences, particularly those engaged in the study of Behavioral Physiology, namely the discipline focusing on the intricate interplay between physiological processes and the related behavioral manifestations. Alongside the prevailing conservatism that has characterized the progression of behavioral sciences in recent decades, it advocates for an additional approach in the study of Behavioral Physiology that revolves around a more inclusive perspective: beyond the analysis of isolated behavioral events as discrete components, akin to scattered pieces of a larger puzzle, emphasis also is placed on elucidating their interconnectedness. It is within these interrelationships that the governing constraints of behavior, whether exhibited by humans or any other species, manifest as a cohesive and functional structure.

Nonlinear latent representations of high-dimensional task-fMRI data: Unveiling cognitive and behavioral insights in heterogeneous spatial maps.

Finding an interpretable and compact representation of complex neuroimaging data is extremely useful for understanding brain behavioral mapping and hence for explaining the biological underpinnings of mental disorders. However, hand-crafted representations, as well as linear transformations, may inadequately capture the considerable variability across individuals. Here, we implemented a data-driven approach using a three-dimensional autoencoder on two large-scale datasets. This approach provides a latent representation of high-dimensional task-fMRI data which can account for demographic characteristics whilst also being readily interpretable both in the latent space learned by the autoencoder and in the original voxel space. This was achieved by addressing a joint optimization problem that simultaneously reconstructs the data and predicts clinical or demographic variables. We then applied normative modeling to the latent variables to define summary statistics ('latent indices') and establish a multivariate mapping to non-imaging measures. Our model, trained with multi-task fMRI data from the Human Connectome Project (HCP) and UK biobank task-fMRI data, demonstrated high performance in age and sex predictions and successfully captured complex behavioral characteristics while preserving individual variability through a latent representation. Our model also performed competitively with respect to various baseline models including several variants of principal components analysis, independent components analysis and classical regions of interest, both in terms of reconstruction accuracy and strength of association with behavioral variables.

Applying the Barker School concept of 'behaviour settings' to virtual contexts.

People are spending more and more time interacting with virtual objects and environments. We argue that Roger Barker's concept of a 'behaviour setting' can be usefully applied to such experiences with relatively little modification if we recognize subjective aspects of such experiences such as presence and immersion. We define virtual behaviour settings as virtual environments where the partly or fully digital milieu is synomorphic with and circumjacent to embodied behaviour, as opposed to the fragmented behaviour settings of much-mediated interaction. We present two tools that can help explain and predict the outcomes of virtual experiences-the behaviour setting canvas (BSC) and model-and demonstrate their utility through examples. We conclude that the behaviour setting concept is helpful in both designing virtual environments and understanding their impact, while virtual environments offer a powerful new methodological paradigm for studying behaviour settings. This article is part of the theme issue 'People, places, things, and communities: expanding behaviour settings theory in the twenty-first century'.

Detection and prediction of anomalous behaviors of enterprise's employees based on data-mining and optimization algorithm.

Internal employees have always been at the core of organizational security management challenges. Once an employee exhibits behaviors that threaten the organization, the resulting damage can be profound. Therefore, analyzing reasonably stored behavioral data can equip managers with effective threat monitoring and warning solutions. Through data-mining, a knowledge graph for internal threat data is deduced, and models for detecting anomalous behaviors and predicting resignations are developed. Initially, data-mining is employed to model the knowledge ontology of internal threats and construct the knowledge graph; subsequently, using the behavioral characteristics, the BP neural network is optimized with the Sparrow Search Algorithm (SSA), establishing a detection model for anomalous behaviors (SBP); additionally, behavioral sequences are processed through data feature vectorization. Utilizing SBP, the LSTM network is further optimized, creating a predictive model for employee behaviors (SLSTM); ultimately, SBP detects anomalous behaviors, while SLSTM predicts resignation intentions, thus enhancing detection strategies for at-risk employees. The integration of these models forms a comprehensive threat detection technology within the organization. The efficacy and practicality of detecting anomalous behaviors and predicting resignations using SBP and SLSTM are demonstrated, comparing them with other algorithms and analyzing potential causes of misjudgment. This work has enhanced the detection efficiency and update speed of abnormal employee behaviors, lowered the misjudgment rate, and significantly mitigated the impact of internal threats on the organization.

Mathematical analysis of simple behavioral epidemic models.

COVID-19 highlighted the importance of considering human behavior change when modeling disease dynamics. This led to developing various models that incorporate human behavior. Our objective is to contribute to an in-depth, mathematical examination of such models. Here, we consider a simple deterministic compartmental model with endogenous incorporation of human behavior (i.e., behavioral feedback) through transmission in a classic Susceptible-Exposed-Infectious-Recovered (SEIR) structure. Despite its simplicity, the SEIR structure with behavior (SEIRb) was shown to perform well in forecasting, especially compared to more complicated models. We contrast this model with an SEIR model that excludes endogenous incorporation of behavior. Both models assume permanent immunity to COVID-19, so we also consider a modification of the models which include waning immunity (SEIRS and SEIRSb). We perform equilibria, sensitivity, and identifiability analyses on all models and examine the fidelity of the models to replicate COVID-19 data across the United States. Endogenous incorporation of behavior significantly improves a model's ability to produce realistic outbreaks. While the two endogenous models are similar with respect to identifiability and sensitivity, the SEIRSb model, with the more accurate assumption of the waning immunity, strengthens the initial SEIRb model by allowing for the existence of an endemic equilibrium, a realistic feature of COVID-19 dynamics. When fitting the model to data, we further consider the addition of simple seasonality affecting disease transmission to highlight the explanatory power of the models.

Abnormal Behavior Recognition Based on 3D Dense Connections.

Abnormal behavior recognition is an important technology used to detect and identify activities or events that deviate from normal behavior patterns. It has wide applications in various fields such as network security, financial fraud detection, and video surveillance. In recent years, Deep Convolution Networks (ConvNets) have been widely applied in abnormal behavior recognition algorithms and have achieved significant results. However, existing abnormal behavior detection algorithms mainly focus on improving the accuracy of the algorithms and have not explored the real-time nature of abnormal behavior recognition. This is crucial to quickly identify abnormal behavior in public places and improve urban public safety. Therefore, this paper proposes an abnormal behavior recognition algorithm based on three-dimensional (3D) dense connections. The proposed algorithm uses a multi-instance learning strategy to classify various types of abnormal behaviors, and employs dense connection modules and soft-threshold attention mechanisms to reduce the model's parameter count and enhance network computational efficiency. Finally, redundant information in the sequence is reduced by attention allocation to mitigate its negative impact on recognition results. Experimental verification shows that our method achieves a recognition accuracy of 95.61% on the UCF-crime dataset. Comparative experiments demonstrate that our model has strong performance in terms of recognition accuracy and speed.

Individual characteristics outperform resting-state fMRI for the prediction of behavioral phenotypes.

In this study, we aimed to compare imaging-based features of brain function, measured by resting-state fMRI (rsfMRI), with individual characteristics such as age, gender, and total intracranial volume to predict behavioral measures. We developed a machine learning framework based on rsfMRI features in a dataset of 20,000 healthy individuals from the UK Biobank, focusing on temporal complexity and functional connectivity measures. Our analysis across four behavioral phenotypes revealed that both temporal complexity and functional connectivity measures provide comparable predictive performance. However, individual characteristics consistently outperformed rsfMRI features in predictive accuracy, particularly in analyses involving smaller sample sizes. Integrating rsfMRI features with demographic data sometimes enhanced predictive outcomes. The efficacy of different predictive modeling techniques and the choice of brain parcellation atlas were also examined, showing no significant influence on the results. To summarize, while individual characteristics are superior to rsfMRI in predicting behavioral phenotypes, rsfMRI still conveys additional predictive value in the context of machine learning, such as investigating the role of specific brain regions in behavioral phenotypes.

Endogenous opiates and behavior: 2023.

This paper is the forty-sixth consecutive installment of the annual anthological review of research concerning the endogenous opioid system, summarizing articles published during 2023 that studied the behavioral effects of molecular, pharmacological and genetic manipulation of opioid peptides and receptors as well as effects of opioid/opiate agonists and antagonists. The review is subdivided into the following specific topics: molecular-biochemical effects and neurochemical localization studies of endogenous opioids and their receptors (1), the roles of these opioid peptides and receptors in pain and analgesia in animals (2) and humans (3), opioid-sensitive and opioid-insensitive effects of nonopioid analgesics (4), opioid peptide and receptor involvement in tolerance and dependence (5), stress and social status (6), learning and memory (7), eating and drinking (8), drug and alcohol abuse (9), sexual activity and hormones, pregnancy, development and endocrinology (10), mental illness and mood (11), seizures and neurologic disorders (12), electrical-related activity and neurophysiology (13), general activity and locomotion (14), gastrointestinal, renal and hepatic functions (15), cardiovascular responses (16), respiration and thermoregulation (17), and immunological responses (18).

The Skin-Brain Axis: From UV and Pigmentation to Behaviour Modulation.

The skin-brain axis has been suggested to play a role in several pathophysiological conditions, including opioid addiction, Parkinson's disease and many others. Recent evidence suggests that pathways regulating skin pigmentation may directly and indirectly regulate behaviour. Conversely, CNS-driven neural and hormonal responses have been demonstrated to regulate pigmentation, e.g., under stress. Additionally, due to the shared neuroectodermal origins of the melanocytes and neurons in the CNS, certain CNS diseases may be linked to pigmentation-related changes due to common regulators, e.g., MC1R variations. Furthermore, the HPA analogue of the skin connects skin pigmentation to the endocrine system, thereby allowing the skin to index possible hormonal abnormalities visibly. In this review, insight is provided into skin pigment production and neuromelanin synthesis in the brain and recent findings are summarised on how signalling pathways in the skin, with a particular focus on pigmentation, are interconnected with the central nervous system. Thus, this review may supply a better understanding of the mechanism of several skin-brain associations in health and disease.

Early Blood-Brain Barrier Impairment as a Pathological Hallmark in a Novel Model of Closed-Head Concussive Brain Injury (CBI) in Mice.

Concussion, caused by a rotational acceleration/deceleration injury mild enough to avoid structural brain damage, is insufficiently captured in recent preclinical models, hampering the relation of pathophysiological findings on the cellular level to functional and behavioral deficits. We here describe a novel model of unrestrained, single vs. repetitive concussive brain injury (CBI) in male C56Bl/6j mice. Longitudinal behavioral assessments were conducted for up to seven days afterward, alongside the evaluation of structural cerebral integrity by in vivo magnetic resonance imaging (MRI, 9.4 T), and validated ex vivo by histology. Blood-brain barrier (BBB) integrity was analyzed by means of fluorescent dextran- as well as immunoglobulin G (IgG) extravasation, and neuroinflammatory processes were characterized both in vivo by positron emission tomography (PET) using [18F]DPA-714 and ex vivo using immunohistochemistry. While a single CBI resulted in a defined, subacute neuropsychiatric phenotype, longitudinal cognitive testing revealed a marked decrease in spatial cognition, most pronounced in mice subjected to CBI at high frequency (every 48 h). Functional deficits were correlated to a parallel disruption of the BBB, (R2 = 0.29, p < 0.01), even detectable by a significant increase in hippocampal uptake of [18F]DPA-714, which was not due to activation of microglia, as confirmed immunohistochemically. Featuring a mild but widespread disruption of the BBB without evidence of macroscopic damage, this model induces a characteristic neuro-psychiatric phenotype that correlates to the degree of BBB disruption. Based on these findings, the BBB may function as both a biomarker of CBI severity and as a potential treatment target to improve recovery from concussion.

Computational reconstruction of mental representations using human behavior.

Revealing how the mind represents information is a longstanding goal of cognitive science. However, there is currently no framework for reconstructing the broad range of mental representations that humans possess. Here, we ask participants to indicate what they perceive in images made of random visual features in a deep neural network. We then infer associations between the semantic features of their responses and the visual features of the images. This allows us to reconstruct the mental representations of multiple visual concepts, both those supplied by participants and other concepts extrapolated from the same semantic space. We validate these reconstructions in separate participants and further generalize our approach to predict behavior for new stimuli and in a new task. Finally, we reconstruct the mental representations of individual observers and of a neural network. This framework enables a large-scale investigation of conceptual representations.