En psicología, de modo genérico, se define juventud por la relación entre la edad del individuo y el desarrollo de su personalidad. Es en las redes de sociabilidad que los jóvenes construyen sus valores. En países del Tercer Mundo hay que pensar en clases distintas de juventud. Una de ellas reúne un contingente numéricamente expresivo de marginalizados de la economía y de la cultura. El que no estudia y tampoco trabaja es considerado un paria de la sociedad; o excluido. Desde la perspectiva de los medios de comunicación, ese individuo casi siempre está asociado a la idea de criminalidad y pobreza, además de se le imputar la culpa por la violencia colectiva. Son muchas las gangs: skinheads, punks, anarco-punks, white power, y en ellas se observa la prevalencia del contenido cultural sobre el económico. En Brasil, la mayoría de los jóvenes se halla fuera de la escuela, agrandando el contingente de excluidos. La “ola” de gangs es planetaria y su ideario, _mundial. El “enemigo” - objeto de su ataque — es el “diferente” considerado un privilegiado. En el caso brasileño, ?cual la capacidad del Estado y de la sociedad civil de crear un proyecto nacional que garantiza la ciudadanía de las mayorías?
This study examines whether LLMs can simulate culturally grounded psychological patterns based on demographic information. Using DeepSeek, we generated 2943 virtual participants matched to demographic distributions from the CFPS2018 and compared them with human responses on the Big Five personality traits and subjective well-being across seven Chinese regions.Personality was measured using a 15-item Chinese Big Five inventory, and happiness with a single-item rating. Results revealed broad similarity between real and simulated datasets, particularly in regional variation trends. However, systematic differences emerged:simulated participants scored lower in extraversion and openness, higher in agreeableness and neuroticism, and consistently reported lower happiness. Predictive structures also diverged: while human data identified conscientiousness, extraversion and openness as positive predictors of happiness, the AI emphasized openness and agreeableness, with extraversion predicting negatively. These discrepancies suggest that while LLMs can approximate population-level psychological distributions, they underrepresent culturally specific and affective dimensions. The findings highlight both the potential and limitations of LLM-based virtual participants for large-scale psychological research and underscore the need for culturally enriched training data and improved affective modeling.
Using intelligent systems to perceive psychological and social behaviors, that is, the underlying affective, cognitive, and pathological states that are manifested through observable behaviors and social interactions, remains a challenge due to their complex, multifaceted, and personalized nature. Existing work tackling these dimensions through specialized datasets and single-task systems often miss opportunities for scalability, cross-task transfer, and broader generalization. To address this gap, we curate Human Behavior Atlas, a unified benchmark of diverse behavioral tasks designed to support the development of foundation models for understanding psychological and social behaviors. Human Behavior Atlas comprises over 100,000 samples spanning text, audio, and visual modalities, covering tasks on affective states, cognitive states, pathologies, and social processes. Our unification efforts can reduce redundancy and cost, enable training to scale efficiently across tasks, and enhance generalization of behavioral features across domains. On Human Behavior Atlas, we train three models: Omnisapiens-7B SFT, Omnisapiens-7B BAM, and Omnisapiens-7B RL. We show that training on Human Behavior Atlas enables models to consistently outperform existing multimodal LLMs across diverse behavioral tasks. Pretraining on Human Behavior Atlas also improves transfer to novel behavioral datasets; with the targeted use of behavioral descriptors yielding meaningful performance gains. The benchmark, models, and codes can be found at: https://github.com/MIT-MI/human_behavior_atlas.
Micro-expressions (MEs) are brief, low-intensity, often localized facial expressions. They could reveal genuine emotions individuals may attempt to conceal, valuable in contexts like criminal interrogation and psychological counseling. However, ME recognition (MER) faces challenges, such as small sample sizes and subtle features, which hinder efficient modeling. Additionally, real-world applications encounter ME data privacy issues, leaving the task of enhancing recognition across settings under privacy constraints largely unexplored. To address these issues, we propose a FED-PsyAU research framework. We begin with a psychological study on the coordination of upper and lower facial action units (AUs) to provide structured prior knowledge of facial muscle dynamics. We then develop a DPK-GAT network that combines these psychological priors with statistical AU patterns, enabling hierarchical learning of facial motion features from regional to global levels, effectively enhancing MER performance. Additionally, our federated learning framework advances MER capabilities across multiple clients without data sharing, preserving privacy and alleviating the limited-sample issue for each client. Extensive experiments on commonly-used ME databases demonstrate the effectiveness of our approach.
Seyed Moein Ayyoubzadeh, Kourosh Shahnazari, Mohammadamin Fazli
et al.
Exploration of the impact of personality traits on social interactions within anonymous online communities poses a challenge at the interface of networked social sciences and psychology. We analyze whether Myers-Briggs Type Indicator (MBTI) personality types impact the dynamics of interactions on an anonymous chat system with over 288,000 messages from 6,076 users. Using a data set including 940 users voluntarily providing MBTI typing and gender, we create a weighted undirected network and apply network-science measures-such as assortativity, centrality measures, and community detection with the Louvain algorithm-to estimate the level of personality-based homophily and heterophily. Contrary to previous observations in structured social settings, our research shows a dominance of heterophilous interactions (89.3%), particularly among cognitively complementary types, i.e., NT (Intuitive-Thinking) and NF (Intuitive-Feeling). However, there is a moderate level of personality-based homophily (10.7%), notably among introverted intuitive personalities (e.g., INTJ, INFP, INFJ), reflecting an underlying cognitive alignment that persists regardless of identity markers. The interaction network exhibits scale-free properties with a power-law exponent of 1.45. In contrast, gender is a stronger homophily attribute, as evidenced by stronger levels of female users' group interactions compared with male users. While MBTI type influences minor interaction preferences, community structure exhibits low modularity (Q = 0.2584). The findings indicate that, in the absence of identity cues, psychological traits subtly shape online behavior, blending exploratory heterophily with subtle homophilic inclinations.
During sudden disaster events, accurately predicting public panic sentiment on social media is crucial for proactive governance and crisis management. Current efforts on this problem face three main challenges: lack of finely annotated data hinders emotion prediction studies, unmodeled risk perception causes prediction inaccuracies, and insufficient interpretability of panic formation mechanisms. We address these issues by proposing a Psychology-driven generative Agent framework (PsychoAgent) for explainable panic prediction based on emotion arousal theory. Specifically, we first construct a fine-grained open panic emotion dataset (namely COPE) via human-large language models (LLMs) collaboration to mitigate semantic bias. Then, we develop a framework integrating cross-domain heterogeneous data grounded in psychological mechanisms to model risk perception and cognitive differences in emotion generation. To enhance interpretability, we design an LLM-based role-playing agent that simulates individual psychological chains through dedicatedly designed prompts. Experimental results on our annotated dataset show that PsychoAgent improves panic emotion prediction performance by 12.6% to 21.7% compared to baseline models. Furthermore, the explainability and generalization of our approach is validated. Crucially, this represents a paradigm shift from opaque "data-driven fitting" to transparent "role-based simulation with mechanistic interpretation" for panic emotion prediction during emergencies. Our implementation is publicly available at: https://anonymous.4open.science/r/PsychoAgent-19DD.
Psychotherapy reaches only a small fraction of individuals suffering from mental disorders due to social stigma and the limited availability of therapists. Large language models (LLMs), when equipped with professional psychotherapeutic skills, offer a promising solution to expand access to mental health services. However, the lack of psychological conversation datasets presents significant challenges in developing effective psychotherapy-guided conversational agents. In this paper, we construct a long-periodic dialogue corpus for counseling based on cognitive behavioral therapy (CBT). Our curated dataset includes multiple sessions for each counseling and incorporates cognitive conceptualization diagrams (CCDs) to guide client simulation across diverse scenarios. To evaluate the utility of our dataset, we train an in-depth counseling model and present a comprehensive evaluation framework to benchmark it against established psychological criteria for CBT-based counseling. Results demonstrate that DiaCBT effectively enhances LLMs' ability to emulate psychologists with CBT expertise, underscoring its potential for training more professional counseling agents.
Rüya Su Şencan, Batja Mesquita, Katie Hoemann
et al.
Cultural fit is thought to benefit immigrants’ wellbeing and integration. Previous research on cultural fit focused on explicit attitudes (e.g., how individuals identify with their heritage and host cultures) at the expense of psychological processes (e.g., the extent to which individuals make meaning in similar ways with their surrounding culture). We examined cultural fit in meaning-making in emotional contexts in two complementary ways: first, based on patterns of emotion endorsement (emotional fit), second, based on patterns of word use describing emotional situations (language fit). Dutch-speaking Belgians and Turkish migrants in Belgium (Ns = 100) described two positive and two negative emotional situations, and rated the intensity of their experience on a set of emotion terms. Language patterns in the descriptions, as quantified by the Linguistic Inquiry and Word Count, distinguished between cultures more effectively than rating patterns. The two fit measures did not converge; they were in fact negatively associated in some analyses, particularly for Turkish migrants’ emotional fit and language fit with Belgian culture, suggesting that when these migrants felt similar emotions, they attended to different aspects of their experience. Future research should disentangle the implications of various types of cultural fit on outcomes relevant to immigrant minorities.
Nikolay B Petrov, Gregory Serapio-García, Jason Rentfrow
The humanlike responses of large language models (LLMs) have prompted social scientists to investigate whether LLMs can be used to simulate human participants in experiments, opinion polls and surveys. Of central interest in this line of research has been mapping out the psychological profiles of LLMs by prompting them to respond to standardized questionnaires. The conflicting findings of this research are unsurprising given that mapping out underlying, or latent, traits from LLMs' text responses to questionnaires is no easy task. To address this, we use psychometrics, the science of psychological measurement. In this study, we prompt OpenAI's flagship models, GPT-3.5 and GPT-4, to assume different personas and respond to a range of standardized measures of personality constructs. We used two kinds of persona descriptions: either generic (four or five random person descriptions) or specific (mostly demographics of actual humans from a large-scale human dataset). We found that the responses from GPT-4, but not GPT-3.5, using generic persona descriptions show promising, albeit not perfect, psychometric properties, similar to human norms, but the data from both LLMs when using specific demographic profiles, show poor psychometrics properties. We conclude that, currently, when LLMs are asked to simulate silicon personas, their responses are poor signals of potentially underlying latent traits. Thus, our work casts doubt on LLMs' ability to simulate individual-level human behaviour across multiple-choice question answering tasks.
The rapid spread of digital technologies has produced data-driven feedback loops, wearable devices, social media networks, and mobile applications that shape user behavior, motivation, and mental well-being. While these systems encourage self-improvement and the development of healthier habits through real-time feedback, they also create psychological risks such as technostress, addiction, and loss of autonomy. The present study also aims to investigate the positive and negative psychological consequences of feedback mechanisms on users' behaviour and well-being. Employing a descriptive survey method, the study collected data from 200 purposely selected users to assess changes in behaviour, motivation, and mental well-being related to health, social, and lifestyle applications. Results indicate that while feedback mechanisms facilitate goal attainment and social interconnection through streaks and badges, among other components, they also enhance anxiety, mental weariness, and loss of productivity due to actions that are considered feedback-seeking. Furthermore, test subjects reported that their actions are unconsciously shaped by app feedback, often at the expense of personal autonomy, while real-time feedback minimally influences professional or social interactions. The study shows that data-driven feedback loops deliver not only motivational benefits but also psychological challenges. To mitigate these risks, users should establish boundaries regarding their use of technology to prevent burnout and addiction, while developers need to refine feedback mechanisms to reduce cognitive load and foster more inclusive participation. Future research should focus on designing feedback mechanisms that promote well-being without compromising individual freedom or increasing social comparison.
Cybercrime is estimated to cost the global economy almost \$10 trillion annually and with businesses and governments reporting an ever-increasing number of successful cyber-attacks there is a growing demand to rethink the strategy towards cyber security. The traditional, perimeter security approach to cyber defence has so far proved inadequate to combat the growing threat of cybercrime. Cyber deception offers a promising alternative by creating a dynamic defence environment. Deceptive techniques aim to mislead attackers, diverting them from critical assets whilst simultaneously gathering cyber threat intelligence on the threat actor. This article presents a proof-of-concept (POC) cyber deception system that has been developed to capture the profile of an attacker in-situ, during a simulated cyber-attack in real time. By dynamically and autonomously generating deception material based on the observed attacker behaviour and analysing how the attacker interacts with the deception material, the system outputs a prediction on the attacker's motive. The article also explores how this POC can be expanded to infer other features of the attacker's profile such as psychological characteristics. By dynamically and autonomously generating deception material based on observed attacker behaviour and analysing how the attacker interacts with the deception material, the system outputs a prediciton on the attacker's motive. The article also explores how this POC can be expanded to infer other features of the attacker's profile such as psychological characteristics.
Andrei Khrennikov, Masanao Ozawa, Felix Benninger
et al.
The past few years have seen a surge in the application of quantum theory methodologies and quantum-like modeling in fields such as cognition, psychology, and decision-making. Despite the success of this approach in explaining various psychological phenomena such as order, conjunction, disjunction, and response replicability effects there remains a potential dissatisfaction due to its lack of clear connection to neurophysiological processes in the brain. Currently, it remains a phenomenological approach. In this paper, we develop a quantum-like representation of networks of communicating neurons. This representation is not based on standard quantum theory but on generalized probability theory (GPT), with a focus on the operational measurement framework. Specifically, we use a version of GPT that relies on ordered linear state spaces rather than the traditional complex Hilbert spaces. A network of communicating neurons is modeled as a weighted directed graph, which is encoded by its weight matrix. The state space of these weight matrices is embedded within the GPT framework, incorporating effect observables and state updates within the theory of measurement instruments a critical aspect of this model. This GPT based approach successfully reproduces key quantum-like effects, such as order, non-repeatability, and disjunction effects (commonly associated with decision interference). Moreover, this framework supports quantum-like modeling in medical diagnostics for neurological conditions such as depression and epilepsy. While this paper focuses primarily on cognition and neuronal networks, the proposed formalism and methodology can be directly applied to a wide range of biological and social networks.
La crisis iniciada en 2007-08 y las repercusiones habitacionales tuvieron como respuesta ciudadana la formación de grupos de resistencia a la oleada de desahucios que se produjeron por los impagos de hipoteca en primer lugar, y de alquiler o de ocupación más adelante. La Plataforma de Afectados por la Hipoteca fue considerada la pionera en 2009, pero analizando el caso de Barcelona se constata como en el último lustro han llegado a surgir más de 30 colectivos diferentes que inciden en la lucha por una vivienda digna. En este artículo se pretende analizar el contexto y la localización de estos movimientos sociales, a partir del análisis territorial del impacto de los desahucios, para analizar el alcance territorial de cada uno de ellos y ver posibles vacíos o yuxtaposiciones.
Stať pojednává o některých nedostatečně prozkoumaných vazbách mezi pojmovými systémy logiky u Kurta Gödela, teorie automatů u Alana Turinga a teorie sebe-reprodukujících se automatů u Johna von Neumanna. Stranou jsou ponechány tradiční polemiky (především opozice Gödela a Turinga v pojetí mysli) a pozornost je soustředěna na podobnosti mezi všemi třemi autory. V jednotlivých kapitolách se text věnuje postupně: podobě odlišení syntaxe a sémantiky formálního systému u Gödela, Turinga a von Neumanna; von Neumannově variantě Gödelova důkazu a von Neuma-nnově a Gödelově pojetí Turingova stroje; a konečně stejnému základu pojetí vztahu mezi myslí a strojem u všech tří autorů.
The era of digitalization has a real and broad effect from various walks of life, one of which demands in governance and the education system is clear evidence that this era is a new challenge in the world of education. Education is expected to be able to produce full quality human seed products, which are called 21st century competencies. 21st century competence is the main ability that must be pocketed by students in order to collaborate in real life in the 21st century. In the 21st century, the challenge is given to be able to create education that can participate in producing thinkers who are qualified in the development of economic and social order and are aware of how to become a decent citizen of the world in the 21st century. This study aims to describe 21st century students and look for relationships or relevance to Al Ghazali's thoughts regarding students in the book of Ayyuhal-Walad. The research method applied is descriptive qualitative. The results of the study indicate that Imam al-Ghazali's thoughts in his book, have relevance to the perspectives of 21st century students related to character education until now the main focus in education. and the 21st century teaches them to judge the good and the bad of all things. In accordance with the thoughts of Imam Al Ghazali in his book Ayyuhal-Walad which contains advice and suggestions to do good things and consider bad things.
<p style="text-align: justify;">The article is devoted to the review of national and foreign studies in professional resilience. The analyzed researches demonstrate that it is typical for foreign psychological science to consider professional resilience as a set of resources that allows a specialist in a difficult situation to provide himself with psychological well-being, which requires the skills of taking care of himself and his psychological state, providing self-help and the ability to adapt to changing conditions. The data of foreign empirical studies of professional resilience of specialists in helping professions have shown a correlation between professional resilience and emotional burnout, psychological well-being, self-compassion, and creative thinking. Also, researchers note the need for the formation of professional resilience in the educational process and labor activity. National researchers consider resilience mainly as the ability to manage functions and processes (coping, recovery) based on the internal resources of the individual, external and internal protective factors in difficult conditions in order to adapt to them and restore life satisfaction.</p>
This paper studies Erich Fromm’s critical theory of religion and looks into the evolution of the philosopher’s views. We analyze key concepts of Fromm’s humanistic psychology, including biophilia, rejection of idolatry, X-experience, classification of religions as humanistic or authoritarian, plea for sustainable coexistence with the environment, and some others. The author demonstrates close connection of these concepts with Judaic tradition, especially the messianism and negative theology of Maimonides. The paper is divided into chapters tracing the evolution of Fromm’s views on religion — from Hasidic Judaism, through following Freud and Marx, to the concept of humanistic religion. The analysis shows that starting from his early works and up to the radical, socialistic humanism as the pinnacle of his thought, Fromm as a philosopher and a strong Israelite draws inspiration from the religious tradition. Notably, his PhD thesis was devoted to the sociology of Hebrew diaspora, Der Sabbath, The Dogma of Christ. However, Fromm’s theory of religion, accordant with the Frankfurt School, combines aspects of Hegel, Marx and Freud’s teachings. Fromm’s views on religion are an original, self-consistent synthesis of diverse ideas, and result in the concept of radical humanism. The paper specifically considers Fromm’s view on idolatry as a form of alienation. Fromm urges to fight against idolatry in a bro.ader sense, finding it in various social phenomena, ranging from consumerism to religious fundamentalism. The paper also reviews the concept of X-experience that Fromm gives in You Shall Be as Gods. The X-experience is a special transcendental experience, separated from its multiple theistic or non-theistic conceptualizations. X-experience is psychological in its nature and leads to diminishing or eliminating narcissism. It constitutes a certain opposition to the alienation caused by idolatry. The work also considers Fromm’s idea of humanistic religion as related to his other concepts. The author supposes that the distinction between authoritarian and humanistic religions is tied to the earlier separation into authoritarian and humanistic ethics that Fromm presents in Man for himself. The impact of Marx and Freud on Fromm’s philosophy of religion is highlighted. While drawing from both, Fromm considered Marx’s theory to be deeper and more significant.