Women’s rights in Islam are often debated and frequently misunderstood due to cultural practices and selective interpretations. This paper examines the historical evolution of women’s rights in Islam from pre-Islamic Arabia to modern times. It aims to show how Islamic teachings brought significant reforms in the social, legal, and moral status of women. Before Islam, women faced severe discrimination, denial of inheritance, and lack of personal choice. Islam addressed these injustices by granting women rights to inheritance, consent in marriage, education, religious responsibility, and participation in social life. The study also highlights the important contributions of Muslim women scholars, jurists, and educators during the prophetic and medieval periods. In addition, the paper briefly discusses Muslim feminism and contrasts it with Western feminist thought to clarify key ideological differences. Based on Qur’anic teachings, Hadith, and historical evidence, the paper argues that Islam fundamentally supports dignity, justice, and equity for women. The study concludes that many contemporary challenges faced by Muslim women arise from cultural misuse of religion rather than Islamic principles themselves. Understanding Islam through authentic sources is essential for an accurate view of women’s rights.
Alex Anvi Eponon, Ildar Batyrshin, Christian E. Maldonado-Sifuentes
et al.
The dominant paradigms of artificial intelligence were shaped by learning theories from psychology: behaviorism inspired reinforcement learning, cognitivism gave rise to deep learning and memory-augmented architectures, and constructivism influenced curriculum learning and compositional approaches. This paper argues that each AI paradigm inherited not only the strengths but the structural limitations of the psychological theory that inspired it. Reinforcement learning cannot account for the internal structure of knowledge, deep learning compresses representations into opaque parameter spaces resistant to principled update, and current integrative approaches lack a formal account of how new understanding is constructed from existing components. The paper further examines a cross-cultural divergence in the interpretation of rote learning, arguing that the Eastern conception of memorization as a structured, multi-phase precursor to understanding offers an underexploited bridge between psychological theory and AI methodology. Drawing on the systematicity debate and critique of Aizawa of both classicism and connectionism, this paper introduces ReSynth, a trimodular framework that separates reasoning (Intellect), purpose (Identity), and knowledge (Memory) as architecturally independent components. The paper traces the genealogy from psychological paradigm to AI method, diagnoses the inherited limitations at each stage, and argues that adaptability, the central challenge of artificial general intelligence requires a representational architecture in which systematic behavior is a necessary consequence rather than an accidental property.
Dolazak Družbe sestara milosrdnica sv. Vinka Paulskog na naše prostore označio je početak pružanja organizirane skrbi bolesnima i potrebitima, ali i podizanja stupnja obrazovanja prvenstveno ženske populacije. Osnivanjem brojnih odgojno-obrazovnih i zdravstveno-karitativnih ustanova na području jugoistočne Europe, sve do kraja Drugoga svjetskog rata, sestre milosrdnice su imale važnu i vodeću ulogu u promicanju i realizaciji svoje misije. U Karlovcu su do sredine 19. stoljeća skrb za bolesne i potrebite pružali vojni liječnici, franjevci i priučeno osoblje. Izgradnja Gradske javne bolnice 1846. godine označava važan korak u razvoju zdravstvene skrbi u Karlovcu. U bolnici su uz liječnike radile sestre milosrdnice sv. Vinka Paulskog i priučeno osoblje. Svrha je ovoga rada istražiti doprinos i djelokrug rada Družbe sestara milosrdnica sv. Vinka Paulskog u Gradskoj javnoj bolnici u Karlovcu u razdoblju od 1. travnja 1888. do 12. listopada 1978. godine.
O presente artigo tem como objetivo analisar a importância das relações sociais no contexto da categoria Ubuntu, bem como esclarecer sua origem e fundamentação filosófica. Ubuntu é compreendido como uma ontologia e epistemologia em fluxo, enraizada na ética dos povos Zulus, mas frequentemente associada a uma ética africana mais ampla. A concepção contemporânea de Ubuntu foi sistematizada pelo arcebispo sul-africano Desmond Tutu, no intuito de oferecer uma resposta ética às dinâmicas sociais no contexto pós-apartheid. Para que fosse reconhecido como representativo de uma ética africana, pesquisadores buscaram elementos comuns entre os povos de matriz linguística Bantu. No entanto, argumenta-se que o Ubuntu constitui, em grande medida, um procedimento formal – um ideal normativo – e não uma prática social amplamente efetivada. Essa observação é sustentada pela persistência de fenômenos como a xenofobia e a hostilidade étnica em diversas regiões do continente africano, em especial na própria África do Sul. Ainda assim, o Ubuntu permanece como um ideal ético relevante, fundamentado em seu princípio central: eu sou porque nós somos. Na concepção de Ubuntu formulada por Tutu, o “Eu” é compreendido como um sujeito inclusivo e fluido, que incorpora o “Nós” em sua própria constituição. Dessa forma, a junção linguística ubuntu não apenas expressa uma ética relacional, mas também evoca uma concepção existencial da vida em comunidade
Eleonora Vitanza, Pietro DeLellis, Chiara Mocenni
et al.
This study integrates causal inference, graph analysis, temporal complexity measures, and machine learning to examine whether individual symptom trajectories can reveal meaningful diagnostic patterns. Testing on a longitudinal dataset of N=45 individuals affected by General Anxiety Disorder (GAD) and/or Major Depressive Disorder (MDD) derived from Fisher et al. 2017, we propose a novel pipeline for the analysis of the temporal dynamics of psychopathological symptoms. First, we employ the PCMCI+ algorithm with nonparametric independence test to determine the causal network of nonlinear dependencies between symptoms in individuals with different mental disorders. We found that the PCMCI+ effectively highlights the individual peculiarities of each symptom network, which could be leveraged towards personalized therapies. At the same time, aggregating the networks by diagnosis sheds light to disorder-specific causal mechanisms, in agreement with previous psychopathological literature. Then, we enrich the dataset by computing complexity-based measures (e.g. entropy, fractal dimension, recurrence) from the symptom time series, and feed it to a suitably selected machine learning algorithm to aid the diagnosis of each individual. The new dataset yields 91% accuracy in the classification of the symptom dynamics, proving to be an effective diagnostic support tool. Overall, these findings highlight how integrating causal modeling and temporal complexity can enhance diagnostic differentiation, offering a principled, data-driven foundation for both personalized assessment in clinical psychology and structural advances in psychological research.
Psychological measurement is critical to many disciplines. Despite advances in measurement, building nomological networks, theoretical maps of how concepts and measures relate to establish validity, remains a challenge 70 years after Cronbach and Meehl proposed them as fundamental to validation. This limitation has practical consequences: clinical trials may fail to detect treatment effects, and public policy may target the wrong outcomes. We introduce Analysis of Latent Indicators to Generate Nomological Structures (ALIGNS), a large language model-based system trained with validated questionnaire measures. ALIGNS provides three comprehensive nomological networks containing over 550,000 indicators across psychology, medicine, social policy, and other fields. This represents the first application of large language models to solve a foundational problem in measurement validation. We report classification accuracy tests used to develop the model, as well as three evaluations. In the first evaluation, the widely used NIH PROMIS anxiety and depression instruments are shown to converge into a single dimension of emotional distress. The second evaluation examines child temperament measures and identifies four potential dimensions not captured by current frameworks, and questions one existing dimension. The third evaluation, an applicability check, engages expert psychometricians who assess the system's importance, accessibility, and suitability. ALIGNS is freely available at nomologicalnetwork.org, complementing traditional validation methods with large-scale nomological analysis.
Luciana Ciringione, Emma Franchino, Simone Reigl
et al.
Math anxiety poses significant challenges for university psychology students, affecting their career choices and overall well-being. This study employs a framework based on behavioural forma mentis networks (i.e. cognitive models that map how individuals structure their associative knowledge and emotional perceptions of concepts) to explore individual and group differences in the perception and association of concepts related to math and anxiety. We conducted 4 experiments involving psychology undergraduates from 2 samples (n1 = 70, n2 = 57) compared against GPT-simulated students (GPT-3.5: n2 = 300; GPT-4o: n4 = 300). Experiments 1, 2, and 3 employ individual-level network features to predict psychometric scores for math anxiety and its facets (observational, social and evaluational) from the Math Anxiety Scale. Experiment 4 focuses on group-level perceptions extracted from human students, GPT-3.5 and GPT-4o's networks. Results indicate that, in students, positive valence ratings and higher network degree for "anxiety", together with negative ratings for "math", can predict higher total and evaluative math anxiety. In contrast, these models do not work on GPT-based data because of differences in simulated networks and psychometric scores compared to humans. These results were also reconciled with differences found in the ways that high/low subgroups of simulated and real students framed semantically and emotionally STEM concepts. High math-anxiety students collectively framed "anxiety" in an emotionally polarising way, absent in the negative perception of low math-anxiety students. "Science" was rated positively, but contrasted against the negative perception of "math". These findings underscore the importance of understanding concept perception and associations in managing students' math anxiety.
Writing longer prompts for an AI assistant to generate a short story increases psychological ownership, a user's feeling that the writing belongs to them. To encourage users to write longer prompts, we evaluated two interaction techniques that modify the prompt entry interface of chat-based generative AI assistants: pressing and holding the prompt submission button, and continuously moving a slider up and down when submitting a short prompt. A within-subjects experiment investigated the effects of such techniques on prompt length and psychological ownership, and results showed that these techniques increased prompt length and led to higher psychological ownership than baseline techniques. A second experiment further augmented these techniques by showing AI-generated suggestions for how the prompts could be expanded. This further increased prompt length, but did not lead to improvements in psychological ownership. Our results show that simple interface modifications like these can elicit more writing from users and improve psychological ownership.
Christiaan Verwijs, Evelien Acun-Roos, Daniel Russo
As hybrid, distributed, and asynchronous work models become more prevalent, continuous learning in Agile Software Development (ASD) gains renewed importance. Communities of Practice (CoPs) are increasingly adopted to support social learning beyond formal education, often relying on virtual communication. Psychological safety, a prerequisite for effective learning, remains insufficiently understood in these settings. This mixed-methods study investigates psychological safety within Agile CoPs through survey data from 143 participants. Results indicate that psychological safety is significantly lower in online interactions compared to face-to-face settings. Moreover, low psychological safety reduces participants' intent to continue contributing and avoidance of interpersonal risk. No significant differences emerged based on gender, community seniority, or content creation activity. However, differences by role and age group suggest potential generational or role-related effects. Thematic analysis revealed exclusionary behavior, negative interaction patterns, and hostility as primary threats to psychological safety, often reinforced by tribalism and specific community dynamics. Suggested interventions include establishing explicit norms, structured facilitation, and active moderation. The findings were validated through member checking with 30 participants. This study provides a comparative perspective on interaction modalities and offers practical guidance for organizers seeking to cultivate inclusive, high-impact CoPs and similarly structured virtual or hybrid work environments.
Over the years, online scams have grown dramatically, with nearly 50% of global consumers encountering scam attempts each week. These scams cause not only significant financial losses to individuals and businesses, but also lasting psychological trauma, largely due to scammers' strategic employment of psychological techniques (PTs) to manipulate victims. Meanwhile, scammers continually evolve their tactics by leveraging advances in Large Language Models (LLMs) to generate diverse scam variants that easily bypass existing defenses. To address this pressing problem, we introduce PsyScam, a benchmark designed to systematically capture the PTs employed in real-world scam reports, and investigate how LLMs can be utilized to generate variants of scams based on the PTs and the contexts provided by these scams. Specifically, we collect a wide range of scam reports and ground its annotations of employed PTs in well-established cognitive and psychological theories. We further demonstrate LLMs' capabilities in generating through two downstream tasks: scam completion, and scam augmentation. Experimental results show that PsyScam presents significant challenges to existing models in both detecting and generating scam content based on the PTs used by real-world scammers. Our code and dataset are available.
Sharon Ferguson, Georgia Van de Zande, Alison Olechowski
The data created from virtual communication platforms presents the opportunity to explore automated measures for monitoring team performance. In this work, we explore one important characteristic of successful teams - Psychological Safety - or the belief that a team is safe for interpersonal risk-taking. To move towards an automated measure of this phenomenon, we derive virtual communication characteristics and message keywords related to elements of Psychological Safety from the literature. Using a mixed methods approach, we investigate whether these characteristics are present in the Slack messages from two design teams - one high in Psychological Safety, and one low. We find that some usage characteristics, such as replies, reactions, and user mentions, might be promising metrics to indicate higher levels of Psychological Safety, while simple keyword searches may not be nuanced enough. We present the first step towards the automated detection of this important, yet complex, team characteristic.
Psychological consultation is essential for improving mental health and well-being, yet challenges such as the shortage of qualified professionals and scalability issues limit its accessibility. To address these challenges, we explore the use of large language models (LLMs) like GPT-4 to augment psychological consultation services. Our approach introduces a novel layered prompting system that dynamically adapts to user input, enabling comprehensive and relevant information gathering. We also develop empathy-driven and scenario-based prompts to enhance the LLM's emotional intelligence and contextual understanding in therapeutic settings. We validated our approach through experiments using a newly collected dataset of psychological consultation dialogues, demonstrating significant improvements in response quality. The results highlight the potential of our prompt engineering techniques to enhance AI-driven psychological consultation, offering a scalable and accessible solution to meet the growing demand for mental health support.
Artificial Intelligence (AI) is increasingly being integrated into scientific research, particularly in the social sciences, where understanding human behavior is critical. Large Language Models (LLMs) have shown promise in replicating human-like responses in various psychological experiments. We conducted a large-scale study replicating 156 psychological experiments from top social science journals using three state-of-the-art LLMs (GPT-4, Claude 3.5 Sonnet, and DeepSeek v3). Our results reveal that while LLMs demonstrate high replication rates for main effects (73-81%) and moderate to strong success with interaction effects (46-63%), They consistently produce larger effect sizes than human studies, with Fisher Z values approximately 2-3 times higher than human studies. Notably, LLMs show significantly lower replication rates for studies involving socially sensitive topics such as race, gender and ethics. When original studies reported null findings, LLMs produced significant results at remarkably high rates (68-83%) - while this could reflect cleaner data with less noise, as evidenced by narrower confidence intervals, it also suggests potential risks of effect size overestimation. Our results demonstrate both the promise and challenges of LLMs in psychological research, offering efficient tools for pilot testing and rapid hypothesis validation while enriching rather than replacing traditional human subject studies, yet requiring more nuanced interpretation and human validation for complex social phenomena and culturally sensitive research questions.
Ben Prystawski, Paul Thibodeau, Christopher Potts
et al.
Probabilistic models of language understanding are valuable tools for investigating human language use. However, they need to be hand-designed for a particular domain. In contrast, large language models (LLMs) are trained on text that spans a wide array of domains, but they lack the structure and interpretability of probabilistic models. In this paper, we use chain-of-thought prompts to introduce structures from probabilistic models into LLMs. We explore this approach in the case of metaphor understanding. Our chain-of-thought prompts lead language models to infer latent variables and reason about their relationships in order to choose appropriate paraphrases for metaphors. The latent variables and relationships chosen are informed by theories of metaphor understanding from cognitive psychology. We apply these prompts to the two largest versions of GPT-3 and show that they can improve performance in a paraphrase selection task.
Promovendo uma releitura de L’aperto: l’uomo e l’animale de Giorgio Agamben, o presente artigo tem a dupla intenção de expor a máquina antropológica que opera clássica e modernamente a antropogênese e de apresentar aspectos da filosofia indígena (a filosofia produzida e expressada por ameríndios brasileiros) que orientam a relação entre o homem e o animal, bem como entre o humano e animalidade, em contraste. Arriscamos empregar a expressão filosofia indígena, conscientes de que ela pode ser mal recebida, embora tenha o texto uma implícita defesa dessa possibilidade. Entre os interlocutores indígenas, visitamos Gersem Baniwa, Daniel Munduruku e Davi Kopenawa, entre outros. Se o contemporâneo está absolutamente presente e cativa a nossa atenção com as luzes e obscuridades, nada pode ser mais contemporâneo do que o esforço de ampliar os nossos horizontes epistemológicos.
Resilience encourages individuals to face, overcome, and become stronger in difficult situations, especially for wives of domestic violence victims to face their issues. This study aims to observe the effectiveness of istighfar dzikr therapy in increasing the resilience of the wives. This study used a mixed-method with sequential explanatory design. The first stage was a quantitative approach involving 12 wives as victims of domestic violence in Yogyakarta. The second stage was a qualitative approach to deepen the quantitative data. The participants were divided into the control group (n=6) and the treatment group (n=6). They were selected using a purposive sampling technique, and the data were collected by CD-RISC (Connor-Davidson Resilience Scale). This study applied a nonrandomized control group with a pre-test-post-test design. The data analysis technique quantitatively used the SPSS software version 25. Then, Anava Mixed Design was used to analyze. The results showed an increase in resilience scores in the treatment group, and the treatment group had higher resilience scores than the control group. Participants in the treatment group were able to maintain the therapeutic effect two weeks after the istighfar dzikr therapy.
Nathan C. Higgins, Ambar G. Monjaras, Breanne D. Yerkes
et al.
In the presence of a continually changing sensory environment, maintaining stable but flexible awareness is paramount, and requires continual organization of information. Determining which stimulus features belong together, and which are separate is therefore one of the primary tasks of the sensory systems. Unknown is whether there is a global or sensory-specific mechanism that regulates the final perceptual outcome of this streaming process. To test the extent of modality independence in perceptual control, an auditory streaming experiment, and a visual moving-plaid experiment were performed. Both were designed to evoke alternating perception of an integrated or segregated percept. In both experiments, transient auditory and visual distractor stimuli were presented in separate blocks, such that the distractors did not overlap in frequency or space with the streaming or plaid stimuli, respectively, thus preventing peripheral interference. When a distractor was presented in the opposite modality as the bistable stimulus (visual distractors during auditory streaming or auditory distractors during visual streaming), the probability of percept switching was not significantly different than when no distractor was presented. Conversely, significant differences in switch probability were observed following within-modality distractors, but only when the pre-distractor percept was segregated. Due to the modality-specificity of the distractor-induced resetting, the results suggest that conscious perception is at least partially controlled by modality-specific processing. The fact that the distractors did not have peripheral overlap with the bistable stimuli indicates that the perceptual reset is due to interference at a locus in which stimuli of different frequencies and spatial locations are integrated.
This chapter, from the Routledge Companion to the Philosophy of Physics (Eleanor Knox and Alastair Wilson, eds., 2021), is an overview of the constraints that relativity places on interpretations of quantum theory. It focuses on four main avenues of approach: (i) additional beables theories, which include "hidden-variables" theories and modal interpretations, (ii) dynamical collapse theories, (iii) Everettian, or "many-worlds" interpretations, and (iv) non-realist interpretations, which deny that quantum states represent anything in physical reality independent of considerations of agents and their beliefs.