Hasil untuk "Philosophy. Psychology. Religion"

M. Nussbaum

Satoshi Usami

The cross-lagged panel model (CLPM) has been widely used, particularly in psychology, to infer longitudinal relations among variables. At the same time, controlling for between-person heterogeneity and capturing within-person relations as processes of within-person change are regarded as key components to causal inference based on longitudinal data. Since Hamaker, Kuiper, and Grasman (2015) criticized the CLPM for its limitations in inferring within-person relations, the random intercept cross-lagged panel model (RI-CLPM), which incorporates stable trait factors representing stable individual differences, has rapidly spread, especially in psychology. At the same time, although many statistical models are available for inferring within-person relations, the distinctions among them have not been clearly delineated, and discussions over the interpretation and selection of statistical models remain active. In this paper, I position the RI-CLPM as one useful method for inferring within-person relations, explain its practical issues, and organize its mathematical and conceptual relationships with other statistical models, as well as potential problems that may arise in their application. In particular, I point out that a distinctive feature of the stable trait factors in the RI-CLPM, in representing between-person heterogeneity, is the assumption that they are uncorrelated with within-person variability, and that this point serves as an important link to the mathematical relationship with the dynamic panel model, another promising alternative.

Han Wang, Jianqiang Li, Qing Zhao et al.

Mental health is a critical global public health issue, and psychological support hotlines play a pivotal role in providing mental health assistance and identifying suicide risks at an early stage. However, the emotional expressions conveyed during these calls remain underexplored in current research. This study introduces a method that combines pitch acoustic features with deep learning-based features to analyze and understand emotions expressed during hotline interactions. Using data from China's largest psychological support hotline, our method achieved an F1-score of 79.13% for negative binary emotion classification.Additionally, the proposed approach was validated on an open dataset for multi-class emotion classification,where it demonstrated better performance compared to the state-of-the-art methods. To explore its clinical relevance, we applied the model to analysis the frequency of negative emotions and the rate of emotional change in the conversation, comparing 46 subjects with suicidal behavior to those without. While the suicidal group exhibited more frequent emotional changes than the non-suicidal group, the difference was not statistically significant.Importantly, our findings suggest that emotional fluctuation intensity and frequency could serve as novel features for psychological assessment scales and suicide risk prediction.The proposed method provides valuable insights into emotional dynamics and has the potential to advance early intervention and improve suicide prevention strategies through integration with clinical tools and assessments The source code is publicly available at https://github.com/Sco-field/Speechemotionrecognition/tree/main.

Margarita Leib, Nils Köbis, Ivan Soraperra

People increasingly rely on AI-advice when making decisions. At times, such advice can promote selfish behavior. When individuals abide by selfishness-promoting AI advice, how are they perceived and punished? To study this question, we build on theories from social psychology and combine machine-behavior and behavioral economic approaches. In a pre-registered, financially-incentivized experiment, evaluators could punish real decision-makers who (i) received AI, human, or no advice. The advice (ii) encouraged selfish or prosocial behavior, and decision-makers (iii) behaved selfishly or, in a control condition, behaved prosocially. Evaluators further assigned responsibility to decision-makers and their advisors. Results revealed that (i) prosocial behavior was punished very little, whereas selfish behavior was punished much more. Focusing on selfish behavior, (ii) compared to receiving no advice, selfish behavior was penalized more harshly after prosocial advice and more leniently after selfish advice. Lastly, (iii) whereas selfish decision-makers were seen as more responsible when they followed AI compared to human advice, punishment between the two advice sources did not vary. Overall, behavior and advice content shape punishment, whereas the advice source does not.

Xilong Cheng, Yunxiao Qin, Yuting Tan et al.

Existing LLM-based role-playing methods often rely on superficial textual descriptions or simplistic metrics, inadequately modeling both intrinsic and extrinsic character dimensions. Additionally, they typically simulate character memory with implicit model knowledge or basic retrieval augment generation without explicit memory alignment, compromising memory consistency. The two issues weaken reliability of role-playing LLMs in several applications, such as trustworthy social simulation. To address these limitations, we propose PsyMem, a novel framework integrating fine-grained psychological attributes and explicit memory control for role-playing. PsyMem supplements textual descriptions with 26 psychological indicators to detailed model character. Additionally, PsyMem implements memory alignment training, explicitly trains the model to align character's response with memory, thereby enabling dynamic memory-controlled responding during inference. By training Qwen2.5-7B-Instruct on our specially designed dataset (including 5,414 characters and 38,962 dialogues extracted from novels), the resulting model, termed as PsyMem-Qwen, outperforms baseline models in role-playing, achieving the best performance in human-likeness and character fidelity.

Marjan Naghshbandi, Sharon Ferguson, Alison Olechowski

While the unique challenges of hybrid work can compromise collaboration and team dynamics, hybrid teams can thrive with well-informed strategies and tools that nurture interpersonal engagements. To inform future supports, we pursue a mixed-methods study of hybrid engineering design capstone teams' Psychological Safety (PS) (i.e., their climate of interpersonal risk-taking and mutual respect) to understand how the construct manifests in teams engaged in innovation. Using interviews, we study six teams' perceptions of PS indicators and how they present differently on Slack (when compared to in-person interactions). We then leverage the interview insights to design Slack-based PS indicators. We present five broad facets of PS in hybrid teams, four perceived differences of PS on Slack compared to in-person, and 15 Slack-based, PS indicators--the groundwork for future automated PS measurement on instant-messaging platforms. These insights produce three design implications and illustrative design examples for ways instant-messaging platforms can support Psychologically Safe hybrid teams, and best practices for hybrid teams to support interpersonal risk-taking and build mutual respect.

Caitlin Cisar, Emily Sheffield, Joshua Drake et al.

Generative AI applications commonly leverage user personas as a steering mechanism for synthetic data generation, but reliance on natural language representations forces models to make unintended inferences about which attributes to emphasize, limiting precise control over outputs. We introduce PILOT (Psychological and Linguistic Output Targeting), a two-phase framework for steering large language models with structured psycholinguistic profiles. In Phase 1, PILOT translates natural language persona descriptions into multidimensional profiles with normalized scores across linguistic and psychological dimensions. In Phase 2, these profiles guide generation along measurable axes of variation. We evaluate PILOT across three state-of-the-art LLMs (Mistral Large 2, Deepseek-R1, LLaMA 3.3 70B) using 25 synthetic personas under three conditions: Natural-language Persona Steering (NPS), Schema-Based Steering (SBS), and Hybrid Persona-Schema Steering (HPS). Results demonstrate that schema-based approaches significantly reduce artificial-sounding persona repetition while improving output coherence, with silhouette scores increasing from 0.098 to 0.237 and topic purity from 0.773 to 0.957. Our analysis reveals a fundamental trade-off: SBS produces more concise outputs with higher topical consistency, while NPS offers greater lexical diversity but reduced predictability. HPS achieves a balance between these extremes, maintaining output variety while preserving structural consistency. Expert linguistic evaluation confirms that PILOT maintains high response quality across all conditions, with no statistically significant differences between steering approaches.

Abhijeet Kumar, Chetan Agarwal, Pronoy B. Neogi et al.

This research explores the fusion of graphology and artificial intelligence to quantify psychological stress levels in students by analyzing their handwritten examination scripts. By leveraging Optical Character Recognition and transformer based sentiment analysis models, we present a data driven approach that transcends traditional grading systems, offering deeper insights into cognitive and emotional states during examinations. The system integrates high resolution image processing, TrOCR, and sentiment entropy fusion using RoBERTa based models to generate a numerical Stress Index. Our method achieves robustness through a five model voting mechanism and unsupervised anomaly detection, making it an innovative framework in academic forensics.

Anthony Diamond

In this work, we propose Perspective Reasoning for Integrated Synthesis and Mediation (PRISM), a multiple-perspective framework for addressing persistent challenges in AI alignment such as conflicting human values and specification gaming. Grounded in cognitive science and moral psychology, PRISM organizes moral concerns into seven "basis worldviews", each hypothesized to capture a distinct dimension of human moral cognition, ranging from survival-focused reflexes through higher-order integrative perspectives. It then applies a Pareto-inspired optimization scheme to reconcile competing priorities without reducing them to a single metric. Under the assumption of reliable context validation for robust use, the framework follows a structured workflow that elicits viewpoint-specific responses, synthesizes them into a balanced outcome, and mediates remaining conflicts in a transparent and iterative manner. By referencing layered approaches to moral cognition from cognitive science, moral psychology, and neuroscience, PRISM clarifies how different moral drives interact and systematically documents and mediates ethical tradeoffs. We illustrate its efficacy through real outputs produced by a working prototype, applying PRISM to classic alignment problems in domains such as public health policy, workplace automation, and education. By anchoring AI deliberation in these human vantage points, PRISM aims to bound interpretive leaps that might otherwise drift into non-human or machine-centric territory. We briefly outline future directions, including real-world deployments and formal verifications, while maintaining the core focus on multi-perspective synthesis and conflict mediation.

Mahmad bin Muhammad Rafiʿ

تروم هذه الدراسة وصف وتحليل إشكال ثنائيّة العمل السياسي والعمل الدعوي في فكر أبو سليمان؛ من أجل معرفة ما قدمه من تفاصيل نظرية وعملية لهذه الثنائية، وقد تضمنت هذه الدراسة بيان الأصول والمفاهيم التي يتأسس عليها النسق المعرفي الناظم لفكر أبو سليمان، ومنه مفهوم العمل السياسي والدعوي، كما تضمنت الدراسة الأصول النظرية التي بنى عليها أبو سليمان مقاربته لهذه الثنائية: من تأصيل وتأريخ؛ لمعرفة ما طرأ عليها من تحولات، والصورة التي آلت إليها في السياق المعاصر، ثم ختمت الدراسة باستعراض تحليلي للحلول الإجرائية التي قدمها أبو سليمان في حل معضلة العلاقة بين العمل السياسي والدعوي، يمكن إجماله في الفصل الوظيفي الدستوري بين العملين؛ على قاعدة التكامل والاستقلال؛ من أجل سد ذريعة احتكار السلطة السياسية للمؤسّسات الدعوية والتربوية وغيرها.

Wenjie Li, Tianyu Sun, Kun Qian et al.

The advent of large language models (LLMs) has significantly advanced various fields, including natural language processing and automated dialogue systems. This paper explores the application of LLMs in psychological counseling, addressing the increasing demand for mental health services. We present a method for instruction tuning LLMs with specialized prompts to enhance their performance in providing empathetic, relevant, and supportive responses. Our approach involves developing a comprehensive dataset of counseling-specific prompts, refining them through feedback from professional counselors, and conducting rigorous evaluations using both automatic metrics and human assessments. The results demonstrate that our instruction-tuned model outperforms several baseline LLMs, highlighting its potential as a scalable and accessible tool for mental health support.

Isuru Thilakasiri, Tharaka Fonseka, Isuri Mapa et al.

Massive changes in many aspects related to social groups of different socioeconomic backgrounds were caused by the COVID-19 pandemic and as a result, the overall state of mental health was severely affected globally. This study examined how the pandemic affected Sri Lankan citizens representing a range of socioeconomic backgrounds in terms of their mental health. The data used in this research was gathered from 3,020 households using a nationwide face-to-face survey, from which a processed dataset of 921 responses was considered for the final analysis. Four distinct factors were identified by factor analysis (FA) that was conducted and subsequently, the population was clustered using unsupervised clustering to determine which population subgroups were affected similarly. Two such subgroups were identified where the respective relationships to the retrieved principal factors and their demographics were thoroughly examined and interpreted. This resulted in the identification of contrasting perspectives between the two groups toward the maintenance and the state of social relationships during the pandemic, which revealed that one group was more 'socially connected' in nature resulting in their mental state being comparatively better in coping with the pandemic. The other group was seen to be more 'socially reserved' showing an opposite reaction toward social connections while their mental well-being declined showing symptoms such as loneliness, and emptiness in response to the pandemic. The study examined the role of social media, and it was observed that social media was perceived as a substitute for the lack of social connections or primarily used as a coping mechanism in response to the challenges of the pandemic

Tyler Garcia, Bill Bridges, Caitlin Solis et al.

Many scientists view science as value-free, despite the fact that both epistemic and non-epistemic values structure scientific inquiry. Current ethics training usually focuses on transmitting knowledge about high-level ethical concepts or rules and is widely regarded as ineffective. We argue that ethics training will be more effective at improving ethical decision making if it focuses on connecting values to science. We pull from philosophy and psychology to define ethical decision making using the Four Component Model. This model states that in order to make an ethical decision someone must consider four components: moral sensitivity, moral reasoning, moral motivation, and moral implementation. We formed a moderated fellowship of fourteen science faculty from different disciplines who met for ten sessions over the course of a year, where they discussed the values embedded in different scientific norms. We then conducted interviews before and after the year-long fellowship that involved guided reflection of scenarios where there was some kind of ethical misconduct where the scientific practice required value judgements (e.g using unpublished data in their own work). We looked at how the fellowship affected the scientists' ability to recognize ethical dimensions regarding the scenarios. We found that this fellowship improved moral sensitivity, but their moral reasoning does not improve. We outlined our approach on how to look at scientists' ethical decision making and made recommendations on how to improve our approach. This work can inform future ethical training to align better with what scientists value and introduce useful concepts from philosophy and psychology to education research in physics.

Astrid Carolus, Martin Koch, Samantha Straka et al.

The goal of the present paper is to develop and validate a questionnaire to assess AI literacy. In particular, the questionnaire should be deeply grounded in the existing literature on AI literacy, should be modular (i.e., including different facets that can be used independently of each other) to be flexibly applicable in professional life depending on the goals and use cases, and should meet psychological requirements and thus includes further psychological competencies in addition to the typical facets of AIL. We derived 60 items to represent different facets of AI Literacy according to Ng and colleagues conceptualisation of AI literacy and additional 12 items to represent psychological competencies such as problem solving, learning, and emotion regulation in regard to AI. For this purpose, data were collected online from 300 German-speaking adults. The items were tested for factorial structure in confirmatory factor analyses. The result is a measurement instrument that measures AI literacy with the facets Use & apply AI, Understand AI, Detect AI, and AI Ethics and the ability to Create AI as a separate construct, and AI Self-efficacy in learning and problem solving and AI Self-management. This study contributes to the research on AI literacy by providing a measurement instrument relying on profound competency models. In addition, higher-order psychological competencies are included that are particularly important in the context of pervasive change through AI systems.

Heinrich Peters, Sandra Matz

Large Language Models (LLMs) demonstrate increasingly human-like abilities across a wide variety of tasks. In this paper, we investigate whether LLMs like ChatGPT can accurately infer the psychological dispositions of social media users and whether their ability to do so varies across socio-demographic groups. Specifically, we test whether GPT-3.5 and GPT-4 can derive the Big Five personality traits from users' Facebook status updates in a zero-shot learning scenario. Our results show an average correlation of r = .29 (range = [.22, .33]) between LLM-inferred and self-reported trait scores - a level of accuracy that is similar to that of supervised machine learning models specifically trained to infer personality. Our findings also highlight heterogeneity in the accuracy of personality inferences across different age groups and gender categories: predictions were found to be more accurate for women and younger individuals on several traits, suggesting a potential bias stemming from the underlying training data or differences in online self-expression. The ability of LLMs to infer psychological dispositions from user-generated text has the potential to democratize access to cheap and scalable psychometric assessments for both researchers and practitioners. On the one hand, this democratization might facilitate large-scale research of high ecological validity and spark innovation in personalized services. On the other hand, it also raises ethical concerns regarding user privacy and self-determination, highlighting the need for stringent ethical frameworks and regulation.

Linda Giovanna Quiñones Gonzales, Fernando Julio Espíritu-Alvarez

Se analizaron estudios empíricos sobre liderazgo transformacional en estudiantes universitarios de pre y posgrado del Perú y el extranjero en el periodo 2014 al 2023. La muestra fue de 23 artículos de investigación obtenidos de cinco bases de datos electrónicas: ERIC, ScienceDirect, Scopus, PubMed y Alicia. Se aplicó la Declaración PRISMA 2020 para realizar la revisión sistemática del tema propuesto. Se encontró que Estados Unidos es el país de donde provienen los principales autores que más publicaron sobre el tema (30,43%), el 2021 fue el año con más publicaciones (26,08%), el enfoque y diseño más usado fue el cuantitativo (69,6%) y correlacional (42,86%) respectivamente. Los instrumentos más utilizados para medir el liderazgo transformacional fueron el Cuestionario de Liderazgo Multifactorial (MLQ) de Bass & Avolio (1990) y el Inventario de práctica de liderazgo (LPI) de Kouzes & Posner (2012). Adicionalmente, se presentó un panorama de las propiedades psicométricas de los instrumentos aplicados y los estadísticos usados por los investigadores de los estudios seleccionados. Finalmente, se brindaron algunas recomendaciones para continuar con el desarrollo del tema abordado.

Nur Fadlina binti Ibrahim, Juwaini Juwaini, Furqan Furqan et al.

Haji Ahmad Laksamana, a controversial figure known through his work “Hakikat Insan”, is often considered deviant from traditional Islamic teachings, particularly regarding the use of Sharia and Tariqa as well as interpretations of the Quran. This article aims to describe the thoughts and Quranic interpretation methodology of Haji Ahmad Laksamana. This study employs a qualitative method with a literature analysis approach. The primary source is the text “Hakikat Insan,” supplemented by various secondary sources, including journals and relevant books. The study finds that Haji Ahmad Laksamana's teachings emphasize the recognition of the essence of knowledge to achieve the level of Insan Kamil Mukamil. His thoughts, reflected in discussions about the relationship between humans and Allah, the dignity of the soul, the essence of the testimony of faith, as well as concepts of Islam, faith, monotheism, and gnosis, demonstrate a unique approach. However, his interpretation method, such as translating Quranic verses into symbols, has sparked controversy and is considered deviant from traditional Islamic understanding. Haji Ahmad Laksamana's controversial ideas are viewed as contradictory to Islamic teachings, diminishing the honor of the Quran and Hadith. Abstrak Haji Ahmad Laksamana, seorang figur kontroversial yang dikenal melalui karyanya “Hakikat Insan”  sering kali dianggap menyimpang dari ajaran Islam tradisional, terutama dalam hal penggunaan syariat dan tarekat serta penafsiran Al-Quran. Artikel ini bertujuan mendeskripsikan tentang pemikiran, dan metodologi penafsiran Al-Quran Haji Ahmad Laksamana.  Kajian ini menggunakan metode kualitatif dengan analisis kepustakaan. Sumber utama adalah teks “Hakikat Insan” serta berbagai sumber sekunder termasuk jurnal dan buku yang relevan. Kajian ini menemukan bahwa ajaran Haji Ahmad Laksamana menitikberatkan pada pengenalan ilmu hakikat untuk mencapai tingkat Insan Kamil Mukamil. Pemikirannya yang tertuang dalam pembahasan mengenai hubungan manusia dengan Allah, martabat nafsu, hakikat syahadat, serta konsep Islam, iman, tauhid, dan makrifat, menunjukkan pendekatan yang unik. Namun, metode interpretasinya, seperti penerjemahan ayat Al-Quran ke dalam simbol, telah menimbulkan kontroversi dan dianggap menyimpang dari pemahaman Islam tradisional. Pemikiran Haji Ahmad Laksamana yang kontroversial dianggap bertentangan dengan ajaran Islam dan mengurangi kehormatan Al-Quran dan hadis.

Ilham habibi Sormin, Muhammad Dalimunthe , Syahrul Abidin

The development of the film world is very diverse and produces films with various styles. Broadly speaking, films can be grouped by story, making orientation, and by genre. This study aims to determine the representation of feminism contained in the science fiction film entitled Level 16. This study uses a qualitative method with the semiotic analysis technique of Ferdinand De Saussure's model which examines the signs in life. Through this method, several scenes are selected in the level 16 film, then these scenes are revealed into denotative and connotative meanings and then interpreted in signifier and signified. In this study, the researcher found ten scenes that presented feminism in level 16 films.

Ümit Karaver, Mohammad Taghi Hosseini

Sheikh Alî al-Bistâmî Musannifak (d. 875/1470), one of the outstanding scholars of his century, spent part of his maturity period in Khorasan after his childhood and youth. Later, he migrated to the land of Rum (Anatolia) and held scientific, military and administrative duties. Musannifak, who came to the Ottoman world at the beginning of the Hijri 860’s, made an effort to introduce himself and established close relations with Grand Vizier Mahmûd Pasha (d. 878/1474). The author dedicated his work Tuhfa-i Mahmûdî/Tuhfa al-vuzara, a work of political advice, to Mahmûd Pasha. This Persian work, completed in Edirne on Thursday, 12 Cemaziyelevvel 861/7 April 1457, is the first work the author wrote when he came to the Ottoman world. The author, who was in search of patronage and seems to have been trying to show off his career, also talked about his family, teachers, ijazets, journeys and books in the eighth chapter of the work consisting of ten chapters. This part, which includes Musannifak’s autobiography, is an interesting example of a biography from the classical period. The section, which can be viewed as analogous to a CV presentation to the prospective employer in today’s terms, has not been the subject of significant scholarly work. In our article, the relevant material has been corrected, translated, and examined from different perspectives.

Zujie Cheng, Banggang Wu, Xiaoyu Deng et al.

Employees' behaviors, as well as the employees' pro-environmental behaviors (PEB), affect the company in many dimensions. Although green innovation performance (GIP) has become an important measurement of a corporate's green development, research investigating PEB from the employees' perspective remains scarce, especially in emerging markets. Therefore, in this study, we developed an original framework to explore the effects of employees' PEB on corporate GIP and examined the underlying mechanism by conducting a survey in China. The results of the empirical analysis showed that employees' PEB increases corporate GIP by positively influencing green organizational identity (GOI). In addition, we also proved how leaders' PEB positively influences GIP, whereas innovation resistance (both technology resistance and resource resistance) has a negative effect on GIP. This study attempted to contribute to theoretical research and practical decision-making in the field of green organizational behavior.